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Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models 3 METHODS 3.1 Lexicon Creation and Expansion The computational approaches to any NLP tasks require annotated lexicons and gold standard data [2]. We collected lexicons for fine-grained categories of SS and SI using an iterative method that included manual chart reviews and semi-automatic methods. 3.1.1 Manual Chart Review 3.1.1 Manual Chart Review Zhu et al. [26] developed a lexicon for identifying SI from clinical notes of patients with prostate cancer in the context of recovery support. Initially, this lexicon, which included 24 terms, was selected; however, it yielded relatively fewer clinical notes at MSHS and WCM compared to the published report. A list of terms for each category was created and extensively reviewed by the study team which included clinical psychiatrists and psychologists. We manually reviewed 50 notes at each site to find SS and SI keywords to enrich the existing lexicons. 3.1.2 Semi-automatic Method 3.1.2 Semi-automatic Method The lexicons from manual chart review as above were enhanced using word embeddings. First, the manually generated lexicons were vectorized using word2vec [39] and Equation 1. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv † https://radimrehurek.com/gensim/ Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

How We Collected Lexicons for Fine-Grained Categories of SS and SI Using an Iterative Method

Author:
(1) Brandon T. Willard, Normal Computing;
(2) R´emi Louf, Normal Computing. Table of Links Abstract and Intro
LLM Sampling and Guided Generation
Iterative FSM Processing and Indexing
Extensions to Iterative Parsing
Discussion, References and Acknowledgments 4. Extensions to Iterative Parsing In this section, we move our focus to general parser-guided generation and start with a simple walk-through for a Python-like grammar provided as a CFG. Consider a vocabulary consisting of strings like "d" and "ef" that can be combined to produce Python-like syntax according to an implicit CFG, and assume that these strings are sequentially sampled and concatenated according to a process like Algorithm 1. Furthermore, consider a terminal symbol DEF in the CFG that corresponds to the string "def" and is given by the trivial regular expression def. Also, consider a NAME symbol given by the regular expression [^\W\d]\w* (e.g. Python identifiers). We want to sequentially parse strings sampled from the aforementioned vocabulary in a way that adheres the Python syntax. For example, the following could be one such sequence: ["d", "ef", " f", "oo(", "):", " ", "pass"]. All the elements of the sequence are by definition elements of the vocabulary. Concatenating the sequence produces "def foo(): pass", which is a valid sequence of tokens defining a function. In the situation we’re considering, we will have observed all the tokens up to a certain point and know nothing about the ones after that point. For instance, at the third observation in the example sequence, we have the concatenated string "def f". If we were to lex/parse this string a traditional approach would return the symbol sequence DEF NAME, which misidentifies the "f" as a complete NAME token. As we can see from the rest of the sequence, the correct NAME token will be "foo". In general, the next valid strings that can be sampled from the vocabulary are ones that either continue expanding/advancing the NAME currently starting with "f" (as the full sequence in our example does), and/or


anything that begins with "("–i.e. an LPAR symbol with regular expression (–and proceeds to specify a valid argument signature. In the first case, the "f" can be seen as a partially matched NAME symbol in Python, and–recalling that its regular expression is [^\W\d]\w*–we can say that it matches both sub-patterns (i.e. [^\W\d] and \w*) in the regular expression. Our use of FSMs formalize the notion of sub-patterns by way of an FSM’s states. In this case, the regex for NAME can be represented by an FSM, M, with three states: 0 (i.e. the initial state q0), 1 (i.e. [^\W\d]), and 2 (i.e. \w*), where 1, 2 ∈ F. Using Algorithm 3, we would obtain the FSM state sequences (0, 1), (1, 2), (2, 2) for "f" and the FSM, M, corresponding to the NAME symbol. These FSM sequences for "f" tell us that matching can start for this vocabulary string in the states 0, 1, or 2, and it can end in states 1 or 2. According to case 1. above, parsing can be continued–for the NAME symbol–after previously ending in states 1 or 2. According to case 2., the next string could also start with or contain an LPAR, implying that M would have terminated, which it can given that 1 and 2 are final states in M at which the parsing would have stopped after reading "f". M terminating also indicates that a NAME symbol was completed, and that a transition to a state accepting LPAR was allowed by the grammar. In this illustration, the next valid vocabulary strings are at least "d", "ef", "pass", " ", "oo(", because all of those strings would expand the partially matched NAME, and the last one would also progress the parse state to one that reads an LPAR. The remaining string, "):", from the subset of the vocabulary we’ve considered would result in a sequence with invalid syntax. In relation to the FSM indexing approach, this means that Algorithm 4 would map FSM states 0, 1, and 2 to the subset "d", "ef", "pass", " ", "oo(" for the symbol NAME and its FSM, M. This illustration omits the underlying parser states that determine which grammar symbols and transitions are allowed. We use pushdown automata (PDA) as a means to extend the FSM approach and address the remaining details. 4.1 Pushdown Automata Formulation We define pushdown automata using the following 6-tuple representation [Sipser, 1996, Definition 2.13]: In order to construct an indexing approach for a PDA-driven parser, we need to use the connection between a CFG’s symbols–via a corresponding PDA’s alphabet–and the lexing and scanning steps that produce the symbols read by a PDA. More specifically, parsers are supported by lexers and scanners that identify symbols from a sequence of character inputs, as we implicitly illustrated in Section 4. Ordered lists of terminal symbols can be constructed for each parse/PDA state based on the symbol and stack transitions allowed by the map δ in each state. This means that we can construct an FSM for each parse state that is the union of each FSM corresponding to a terminal symbols read by the state. A scanning step will then identify a set of possible terminal symbols V ⊂ Σ for the characters read since the last fully identified symbol in the parsing process. For example, in the initial state q0 of a PDA for the Pythonlike CFG in Section 4, scanning and lexing the string "de" will result in V = {DEF, NAME}: i.e. DEF for any vocabulary string completing the string "def"–followed by a string not also read by the NAME FSM (e.g. "def ")– and NAME for any other strings read by its FSM (e.g. "default"). Note that steps of the scanner–and sampling steps of the LLM–will eventually reduce the set V until a single terminal symbol v ∈ V is determined. By applying Algorithm 3 to each string in V using the combined FSMs for each parse state, we can determine parser configurations that consist of the PDA states, the corresponding FSM states, and the potential terminal symbols. By analogy with the steps in Algorithm 3, we can use the pre-image of the PDA’s transition map to determine PDA stack values that will read the PDA states q ∈ Q and terminal symbol sets V of a parser configuration: The stack values provided by this map are needed in order to find paths– if any–through the PDA that allow successful, complete parses of each string in V starting from their possible parser configurations. For parser state and terminal combinations that correspond to REDUCE operations of an LALR(1) parser, these parser configurations will consist of more than just the topof-stack values in Γ; they will consist of sub-stacks corresponding to all valid prefixes for the REDUCE operations entailed by a vocabulary string. Ultimately, each parser configuration that permits a complete parse of a vocabulary string is added as an entry in the index for the PDA, and, in this case, the index will need to be a trie data structure in order to allow queries against the parser’s stack values. This paper is available on arxiv under CC 4.0 license. Author: (1) Brandon T. Willard, Normal Computing; (2) R´emi Louf, Normal Computing. Author: Author: (1) Brandon T. Willard, Normal Computing; (2) R´emi Louf, Normal Computing. Table of Links Abstract and Intro LLM Sampling and Guided Generation Iterative FSM Processing and Indexing Extensions to Iterative Parsing Discussion, References and Acknowledgments Abstract and Intro Abstract and Intro LLM Sampling and Guided Generation LLM Sampling and Guided Generation Iterative FSM Processing and Indexing Iterative FSM Processing and Indexing Extensions to Iterative Parsing Extensions to Iterative Parsing Discussion, References and Acknowledgments Discussion, References and Acknowledgments 4. Extensions to Iterative Parsing In this section, we move our focus to general parser-guided generation and start with a simple walk-through for a Python-like grammar provided as a CFG. Consider a vocabulary consisting of strings like "d" and "ef" that can be combined to produce Python-like syntax according to an implicit CFG, and assume that these strings are sequentially sampled and concatenated according to a process like Algorithm 1. Furthermore, consider a terminal symbol DEF in the CFG that corresponds to the string "def" and is given by the trivial regular expression def. Also, consider a NAME symbol given by the regular expression [^\W\d]\w* (e.g. Python identifiers). We want to sequentially parse strings sampled from the aforementioned vocabulary in a way that adheres the Python syntax. For example, the following could be one such sequence: ["d", "ef", " f", "oo(", "):", " ", "pass"]. All the elements of the sequence are by definition elements of the vocabulary. Concatenating the sequence produces "def foo(): pass", which is a valid sequence of tokens defining a function. In the situation we’re considering, we will have observed all the tokens up to a certain point and know nothing about the ones after that point. For instance, at the third observation in the example sequence, we have the concatenated string "def f". If we were to lex/parse this string a traditional approach would return the symbol sequence DEF NAME, which misidentifies the "f" as a complete NAME token. As we can see from the rest of the sequence, the correct NAME token will be "foo". In general, the next valid strings that can be sampled from the vocabulary are ones that either continue expanding/advancing the NAME currently starting with "f" (as the full sequence in our example does), and/or anything that begins with "("–i.e. an LPAR symbol with regular expression (–and proceeds to specify a valid argument signature. continue expanding/advancing the NAME currently starting with "f" (as the full sequence in our example does), and/or continue expanding/advancing the NAME currently starting with "f" (as the full sequence in our example does), and/or anything that begins with "("–i.e. an LPAR symbol with regular expression (–and proceeds to specify a valid argument signature. anything that begins with "("–i.e. an LPAR symbol with regular expression (–and proceeds to specify a valid argument signature. In the first case, the "f" can be seen as a partially matched NAME symbol in Python, and–recalling that its regular expression is [^\W\d]\w*–we can say that it matches both sub-patterns (i.e. [^\W\d] and \w*) in the regular expression. Our use of FSMs formalize the notion of sub-patterns by way of an FSM’s states. In this case, the regex for NAME can be represented by an FSM, M, with three states: 0 (i.e. the initial state q0), 1 (i.e. [^\W\d]), and 2 (i.e. \w*), where 1, 2 ∈ F. Using Algorithm 3, we would obtain the FSM state sequences (0, 1), (1, 2), (2, 2) for "f" and the FSM, M, corresponding to the NAME symbol. These FSM sequences for "f" tell us that matching can start for this vocabulary string in the states 0, 1, or 2, and it can end in states 1 or 2. According to case 1. above, parsing can be continued–for the NAME symbol–after previously ending in states 1 or 2. According to case 2., the next string could also start with or contain an LPAR, implying that M would have terminated, which it can given that 1 and 2 are final states in M at which the parsing would have stopped after reading "f". M terminating also indicates that a NAME symbol was completed, and that a transition to a state accepting LPAR was allowed by the grammar. In this illustration, the next valid vocabulary strings are at least "d", "ef", "pass", " ", "oo(", because all of those strings would expand the partially matched NAME, and the last one would also progress the parse state to one that reads an LPAR. The remaining string, "):", from the subset of the vocabulary we’ve considered would result in a sequence with invalid syntax. In relation to the FSM indexing approach, this means that Algorithm 4 would map FSM states 0, 1, and 2 to the subset "d", "ef", "pass", " ", "oo(" for the symbol NAME and its FSM, M. This illustration omits the underlying parser states that determine which grammar symbols and transitions are allowed. We use pushdown automata (PDA) as a means to extend the FSM approach and address the remaining details. 4.1 Pushdown Automata Formulation We define pushdown automata using the following 6-tuple representation [Sipser, 1996, Definition 2.13]: In order to construct an indexing approach for a PDA-driven parser, we need to use the connection between a CFG’s symbols–via a corresponding PDA’s alphabet–and the lexing and scanning steps that produce the symbols read by a PDA. More specifically, parsers are supported by lexers and scanners that identify symbols from a sequence of character inputs, as we implicitly illustrated in Section 4. Ordered lists of terminal symbols can be constructed for each parse/PDA state based on the symbol and stack transitions allowed by the map δ in each state. This means that we can construct an FSM for each parse state that is the union of each FSM corresponding to a terminal symbols read by the state. A scanning step will then identify a set of possible terminal symbols V ⊂ Σ for the characters read since the last fully identified symbol in the parsing process. For example, in the initial state q0 of a PDA for the Pythonlike CFG in Section 4, scanning and lexing the string "de" will result in V = {DEF, NAME}: i.e. DEF for any vocabulary string completing the string "def"–followed by a string not also read by the NAME FSM (e.g. "def ")– and NAME for any other strings read by its FSM (e.g. "default"). Note that steps of the scanner–and sampling steps of the LLM–will eventually reduce the set V until a single terminal symbol v ∈ V is determined. By applying Algorithm 3 to each string in V using the combined FSMs for each parse state, we can determine parser configurations that consist of the PDA states, the corresponding FSM states, and the potential terminal symbols. By analogy with the steps in Algorithm 3, we can use the pre-image of the PDA’s transition map to determine PDA stack values that will read the PDA states q ∈ Q and terminal symbol sets V of a parser configuration: The stack values provided by this map are needed in order to find paths– if any–through the PDA that allow successful, complete parses of each string in V starting from their possible parser configurations. For parser state and terminal combinations that correspond to REDUCE operations of an LALR(1) parser, these parser configurations will consist of more than just the topof-stack values in Γ; they will consist of sub-stacks corresponding to all valid prefixes for the REDUCE operations entailed by a vocabulary string. Ultimately, each parser configuration that permits a complete parse of a vocabulary string is added as an entry in the index for the PDA, and, in this case, the index will need to be a trie data structure in order to allow queries against the parser’s stack values. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Efficient Guided Generation for Large Language Models: Extensions to Iterative Parsing

বৃহৎ ভাষার মডেলের জন্য দক্ষ নির্দেশিত প্রজন্ম: পুনরাবৃত্তিমূলক পার্সিংয়ের এক্সটেনশন

Effiziente geführte Generierung für große Sprachmodelle: Erweiterungen zum iterativen Parsing

Generación guiada eficiente para modelos de lenguaje grandes: extensiones al análisis iterativo

Génération guidée efficace pour les grands modèles de langage : extensions à l'analyse itérative

बड़े भाषा मॉडल के लिए कुशल निर्देशित पीढ़ी: पुनरावृत्त पार्सिंग के लिए विस्तार

大規模言語モデルの効率的なガイド付き生成: 反復解析の拡張

대규모 언어 모델을 위한 효율적인 유도 생성: 반복 구문 분석 확장

Geração guiada eficiente para grandes modelos de linguagem: extensões para análise iterativa

Эффективная управляемая генерация для больших языковых моделей: расширения итеративного анализа

Büyük Dil Modelleri için Verimli Kılavuzlu Oluşturma: Yinelemeli Ayrıştırmanın Uzantıları

Tạo hướng dẫn hiệu quả cho các mô hình ngôn ngữ lớn: Phần mở rộng cho phân tích cú pháp lặp

大型语言模型的高效引导生成：迭代解析的扩展

Author:
(1) Brandon T. Willard, Normal Computing;
(2) R´emi Louf, Normal Computing. Table of Links Abstract and Intro
LLM Sampling and Guided Generation
Iterative FSM Processing and Indexing
Extensions to Iterative Parsing
Discussion, References and Acknowledgments 3. Iterative FSM Processing and Indexing To be precise, we consider regular expressions in 5-tuple finite automaton form [Sipser, 1996, Definition 1.5]: Definition 1 (Finite Automaton). A finite automaton, or finite-state machine, is given by (Q, Σ, δ, q0, F), where Q is a finite set of states, Σ a finite alphabet, δ : Q × Σ → Q the transition function, q0 ∈ Q the start state, and F ⊆ Q the set of accept states. The characters comprising the strings in V are drawn from Σ: i.e. V ⊂ P(Σ). Throughout, the FSM states, Q, will be represented by integer values for simplicity. Example 1. We illustrate the FSM sampling process in Figure 1 for the regular expression ([0-9]*)?\.?[0-9]*, which can be used to generate floatingpoint numbers. For simplicity, let the vocabulary, V, consist of only the strings: "A", ".", "42", ".2", and "1". When the generation begins, the FSM is in state 0, so our algorithm masks the string "A", since it would not be accepted by the FSM. We can only sample ".", "42", ".2", and "1" in this case. If we sample ".2", we advance the FSM to state 3. In this case, only "42" and "1" are valid completions, so we mask the other values before sampling. If we sample "1" instead, we advance the FSM to state 1, in which case ".", ".42", ".2", and "1" are valid completions and the mask remains unchanged. Looping through the vocabulary to determine the valid next tokens is still the biggest issue. For that, we pre-process the vocabulary using the regular expression’s FSM and build an index. The important part is that we consider starting in every viable FSM state, because the strings in the vocabulary could match arbitrary parts of a regular expression, and those parts are implicitly the FSM states. A procedure for producing matches starting at any point in the FSM is given in Algorithm 3. The result is a list of sub-sequences detailing the states through which the FSM would traverses when accepting the provided string. By matching the starting states of these sub-sequences to the last FSM state arrived at in a single step of the loop in Algorithm 2, we can efficiently index the vocabulary with a map, σ : Q → P(V), connecting FSM states and sets of elements of the vocabulary that will be accepted by the FSM in those states. Algorithm 4 describes the construction of σ. Using a hash-map for σ can make the m step in Algorithm 2 cost only O(1) on average. Furthermore, since σ is constructed outside of the token sampling procedure, its run-time cost is effectively irrelevant, although it theoretically requires memory equal to the number of states in the FSM (i.e. |Q|). Fortunately, for non-pathological combinations of regular expressions and vocabularies, not every string in the vocabulary will be accepted by the FSM, and not every FSM state will be represented by a string in V. 3.1 Examples In this section we use GPT2-medium (355M parameters) to illustrate how regular expression guided generation works in practice. We use the library Outlines to generate them: Listing 3.1 – continued Listing 3.3 – continued 3.2 Comparison with current methods To illustrate the efficiency of the indexing approach described here, and implemented in Outlines, we perform a simple comparison with the Guidance library. As of this writing, the Guidance library uses partial regular expression matching–applied from the start of the sampled sequence each time–and must iterate over the LLM’s vocabulary (N = 50, 257) on each step. The Guidance code and prompt used for this comparison are as follows: Listing 3.4 – continued The corresponding Outlines code is as follows: Listing 3.5 – continued The value of max_tokens is varied and the timings are recorded with timeit for a single loop and single repeat value (i.e. only one sample is collected for each value of max_tokens). The results are plotted in Section 3.2. Barring any configuration oversights that might be creating a large runtime discrepancy, the observed scaling in the maximum number of sampled tokens is striking and is indicative of the growing computational problem implied by the approach. This paper is available on arxiv under CC 4.0 license. Author: (1) Brandon T. Willard, Normal Computing; (2) R´emi Louf, Normal Computing. Author: Author: (1) Brandon T. Willard, Normal Computing; (2) R´emi Louf, Normal Computing. Table of Links Abstract and Intro LLM Sampling and Guided Generation Iterative FSM Processing and Indexing Extensions to Iterative Parsing Discussion, References and Acknowledgments Abstract and Intro Abstract and Intro LLM Sampling and Guided Generation LLM Sampling and Guided Generation Iterative FSM Processing and Indexing Iterative FSM Processing and Indexing Extensions to Iterative Parsing Extensions to Iterative Parsing Discussion, References and Acknowledgments Discussion, References and Acknowledgments 3. Iterative FSM Processing and Indexing To be precise, we consider regular expressions in 5-tuple finite automaton form [Sipser, 1996, Definition 1.5]: Definition 1 (Finite Automaton). A finite automaton, or finite-state machine, is given by (Q, Σ, δ, q0, F), where Q is a finite set of states, Σ a finite alphabet, δ : Q × Σ → Q the transition function, q0 ∈ Q the start state, and F ⊆ Q the set of accept states. Definition 1 A finite automaton, or finite-state machine, is given by (Q, Σ, δ, q0, F), where Q is a finite set of states, Σ a finite alphabet, δ : Q × Σ → Q the transition function, q0 ∈ Q the start state, and F ⊆ Q the set of accept states. The characters comprising the strings in V are drawn from Σ: i.e. V ⊂ P(Σ). Throughout, the FSM states, Q, will be represented by integer values for simplicity. Example 1. We illustrate the FSM sampling process in Figure 1 for the regular expression ([0-9]*)?\.?[0-9]*, which can be used to generate floatingpoint numbers. For simplicity, let the vocabulary, V, consist of only the strings: "A", ".", "42", ".2", and "1". Example 1. We illustrate the FSM sampling process in Figure 1 for the regular expression ([0-9]*)?\.?[0-9]*, which can be used to generate floatingpoint numbers. For simplicity, let the vocabulary, V, consist of only the strings: "A", ".", "42", ".2", and "1". When the generation begins, the FSM is in state 0, so our algorithm masks the string "A", since it would not be accepted by the FSM. We can only sample ".", "42", ".2", and "1" in this case. When the generation begins, the FSM is in state 0, so our algorithm masks the string "A", since it would not be accepted by the FSM. We can only sample ".", "42", ".2", and "1" in this case. If we sample ".2", we advance the FSM to state 3. In this case, only "42" and "1" are valid completions, so we mask the other values before sampling. If we sample "1" instead, we advance the FSM to state 1, in which case ".", ".42", ".2", and "1" are valid completions and the mask remains unchanged. If we sample ".2", we advance the FSM to state 3. In this case, only "42" and "1" are valid completions, so we mask the other values before sampling. If we sample "1" instead, we advance the FSM to state 1, in which case ".", ".42", ".2", and "1" are valid completions and the mask remains unchanged. Looping through the vocabulary to determine the valid next tokens is still the biggest issue. For that, we pre-process the vocabulary using the regular expression’s FSM and build an index. The important part is that we consider starting in every viable FSM state, because the strings in the vocabulary could match arbitrary parts of a regular expression, and those parts are implicitly the FSM states. A procedure for producing matches starting at any point in the FSM is given in Algorithm 3. The result is a list of sub-sequences detailing the states through which the FSM would traverses when accepting the provided string. By matching the starting states of these sub-sequences to the last FSM state arrived at in a single step of the loop in Algorithm 2, we can efficiently index the vocabulary with a map, σ : Q → P(V), connecting FSM states and sets of elements of the vocabulary that will be accepted by the FSM in those states. Algorithm 4 describes the construction of σ. Using a hash-map for σ can make the m step in Algorithm 2 cost only O(1) on average. Furthermore, since σ is constructed outside of the token sampling procedure, its run-time cost is effectively irrelevant, although it theoretically requires memory equal to the number of states in the FSM (i.e. |Q|). Fortunately, for non-pathological combinations of regular expressions and vocabularies, not every string in the vocabulary will be accepted by the FSM, and not every FSM state will be represented by a string in V. 3.1 Examples In this section we use GPT2-medium (355M parameters) to illustrate how regular expression guided generation works in practice. We use the library Outlines to generate them: Listing 3.1 – continued Listing 3.3 – continued 3.2 Comparison with current methods To illustrate the efficiency of the indexing approach described here, and implemented in Outlines, we perform a simple comparison with the Guidance library. As of this writing, the Guidance library uses partial regular expression matching–applied from the start of the sampled sequence each time–and must iterate over the LLM’s vocabulary (N = 50, 257) on each step. The Guidance code and prompt used for this comparison are as follows: Listing 3.4 – continued The corresponding Outlines code is as follows: Listing 3.5 – continued The value of max_tokens is varied and the timings are recorded with timeit for a single loop and single repeat value (i.e. only one sample is collected for each value of max_tokens). The results are plotted in Section 3.2. Barring any configuration oversights that might be creating a large runtime discrepancy, the observed scaling in the maximum number of sampled tokens is striking and is indicative of the growing computational problem implied by the approach. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Efficient Guided Generation for Large Language Models: Iterative FSM Processing and Indexing

বৃহৎ ভাষার মডেলের জন্য দক্ষ নির্দেশিত প্রজন্ম: পুনরাবৃত্তিমূলক FSM প্রক্রিয়াকরণ এবং সূচীকরণ

Effiziente geführte Generierung für große Sprachmodelle: Iterative FSM-Verarbeitung und Indizierung

Generación guiada eficiente para modelos de lenguaje grandes: indexación y procesamiento FSM iterativo

Génération guidée efficace pour les grands modèles de langage : traitement et indexation itératifs FSM

बड़े भाषा मॉडल के लिए कुशल निर्देशित पीढ़ी: पुनरावृत्त FSM प्रसंस्करण और अनुक्रमण

大規模言語モデルの効率的なガイド付き生成: 反復的な FSM 処理とインデックス作成

대규모 언어 모델을 위한 효율적인 생성 유도: 반복적인 FSM 처리 및 인덱싱

Geração guiada eficiente para modelos de linguagem grande: processamento e indexação FSM iterativo

Эффективная управляемая генерация для больших языковых моделей: итеративная обработка и индексирование конечного автомата

Büyük Dil Modelleri için Verimli Kılavuzlu Oluşturma: Yinelemeli FSM İşleme ve Dizin Oluşturma

Tạo hướng dẫn hiệu quả cho các mô hình ngôn ngữ lớn: Lập chỉ mục và xử lý FSM lặp

大型语言模型的高效引导生成：迭代 FSM 处理和索引

Engineers, whether they’re data, software, or DevOps, often strive to build or design the perfect solution. They tend to focus predominantly on technical aspects, sometimes overlooking business outcomes. This is a common oversight. However, our main goal is to develop a product that addresses a business problem, not necessarily to create a flawless solution. Therefore, we need to continually revise our design to ensure we’re on the right track. Incremental design results in a working system at the end of implementation. On the other hand, iterative design produces a functioning system at the end of each iteration. Notable examples of iterative design are presented in the picture, created by Henrik Kniberg. Sounds good? In the realm of data engineering, consider the task of building a data pipeline. This pipeline needs to ingest data from a source, transform it, load it into a destination, and provide data access. To build the right solution, we need to address several questions: What is the source (technology, format, etc.)? How much data do we need to ingest (overall, regularly)? What is the destination (technology, format, etc.)? What are the business expectations from the pipeline (SLA, availability, latency, etc.)? The first three questions are technical, while the fourth is business-oriented. The answers to these will guide our technical decisions. Based on business priorities and level of expertise, we might hear responses such as: We need fresh data daily. Fresh data should be available during working hours. Assuming we have a relational database as a source system and a data platform (data warehouse, data lake, lakehouse, etc.) as a target, we can start designing our pipeline. Our initial solution might be a batch pipeline that runs daily and loads data into our data platform. But with past experience in data ingestion with lower latency, or if we’ve used Change Data Capture (CDC), we might suggest a pipeline that ingests data with lower latency (hourly or near real-time). However, implementing a streaming pipeline requires several tasks: Enabling CDC on the source database, which may require: Changes on the source database, involving DBAs Choosing, configuring, and installing CDC software, involving DevOp 2. Defining a target system for CDC data (file system, object storage, message broker). 3. Writing a streaming pipeline that ingests CDC data to our data platform (to a raw layer). 4. Writing a streaming pipeline that transforms data and incrementally loads it to the next layers. 5. Covering all components with monitoring, alerting, logging, etc. Depending on the company’s size and existing technologies, implementing such a solution could take several weeks to months. To avoid building a solution that might not be needed, we should build our system incrementally and iteratively. Here is a possible approach: Phase 1: Implement a batch pipeline that ingests data from the source database and loads it to our data platform (to a raw layer). Depending on the amount of data, this could be a full load or a partial load based on certain criteria. Implement a batch pipeline that reads only actual data from a raw layer and does truncate inserts to further layers. This approach saves us from dealing with updates, deletes, and merge operations, allowing faster implementation. Cover all components with monitoring, alerting, logging, etc. Phase 2: Implement incremental data processing from the raw layer to further layers, which can reduce processing time and infrastructure costs. Phase 3: Enable CDC on the source database. Define a target system for CDC data (file system, object storage, message broker). Implement a streaming pipeline that ingests CDC data into our data platform and processes it incrementally to further layers. Cover all components with monitoring, alerting, logging, etc. At each phase, we have a working system that solves a business problem, allowing the business to benefit from our implementation. We can stop at any phase or continue to implement more features as per business needs.

System Design: An Iterative and Incremental Approach

Authors: (1) Nathan Lambert, Allen Institute for AI; (2) Roberto Calandra, TU Dresden. Table of Links Abstract & Introduction Related Work Background Understanding Objective Mismatch Discussions Conclusion Acknowledgments, and References 5 Discussions The iterative deployment form of RLHF where reward models are retrained based on user data, which induces a second feedback loop, is shown in Fig. 4. Schulman (2023) discusses how this is used in ChatGPT to mitigate issues such as evasiveness, verbosity, and other unexpected, undesirable qualities. Designing in this framework introduces further complexity onto engineering objectives, but allows iterative mitigation of mismatch. This style of iterative RL deployment has been understood as exogenous feedback (Gilbert, Dean, Zick, & Lambert, 2022) and can have societal implications. Iterative deployment of RLHF There is some literature in this space, but expanding related works to the scale of use of modern LLMs will be difficult. For example, Suhr and Artzi (2022) shows theoretical results on outer-loop optimization of instruction-tuned models. The modifications made to the RL optimization of RLHF cast it as a contextual bandits problem, where an agent takes one action and the dynamics are abstracted into one trajectory-reward pairing. Work in this area has investigated the potential of integrating partial, skewed, or noisy human feedback into the optimization process (Nguyen, Daumé III, & Boyd-Graber, 2017). Contextual bandits The subarea of dueling bandits has further specified the problem that is closely aligned with RLHF, but in primarily theoretical work with much smaller models, datasets, and tasks. Yue, Broder, Kleinberg, and Joachims (2012) explains this space in work showing theoretical bounds: “In contrast to conventional approaches that require the absolute reward of the chosen strategy to be quantifiable and observable, our setting assumes only that (noisy) binary feedback about the relative reward of two chosen strategies is available. This type of relative feedback is particularly appropriate in applications where absolute rewards have no natural scale or are difficult to measure... but where pairwise comparisons are easy to make.” This, while closely related to RLHF, will require substantial experimentation to be applicable. Others have built on this into work directly learning from human preferences (Sekhari, Sridharan, Sun, & Wu, 2023) or from implicit human feedback (Maghakian et al., 2022). This paper is under CC 4.0 license. available on arxiv

The Iterative Deployment of RLHF in Language Models

In the realm of problem-solving, various techniques and strategies are employed to achieve desired outcomes. Three prominent methods are often utilized: the iterative approach, the recursive approach, and the derivative approach. Each approach has its unique characteristics, advantages, and disadvantages, making it essential to understand when and how to employ each one. In this article, we will explore these three problem-solving methods and offer insights into when to use each, highlighting which approach might be better suited for a given situation. Iterative Approach The iterative approach is a straightforward and linear method of problem-solving. It involves breaking a complex problem into smaller, more manageable sub-problems and solving them sequentially. Each sub-problem's solution is often used to build upon the next. It is widely favored for its simplicity and efficiency in solving problems. Advantages of the Iterative Approach: Simplicity: Iteration is easy to understand and implement. Efficiency: It is often faster for solving problems with well-defined sequential steps. Memory efficiency: Typically requires less memory compared to recursive methods. When to Use the Iterative Approach: Problems that can be divided into smaller, independent sub-problems. When memory usage is a concern. Iterative algorithms are useful for optimization problems and numerical computations. Recursive Approach The recursive approach is a technique that involves solving a problem by breaking it down into smaller instances of the same problem. It's a conceptually elegant method but can be more complex to implement than iteration. Recursive functions call themselves with modified inputs, which eventually lead to a base case where the recursion stops. Advantages of the Recursive Approach: Elegance: It is suitable for problems with inherently recursive structures. Clarity: Sometimes, the recursive approach can result in clearer, more intuitive code. When to Use the Recursive Approach: Problems that exhibit a recursive structure, such as tree traversal, fractals, or certain mathematical sequences. When a clear and concise solution is more critical than performance. Be cautious of stack overflow errors when using recursion on large datasets or deep levels of recursion. Derivative Approach The derivative approach is quite distinct from the iterative and recursive methods. It involves finding the rate of change of a problem or its derivative. This is a common technique in calculus, where derivatives describe the slope or rate of change of a function at a given point. The derivative approach is primarily used for solving problems involving rates of change, optimization, and predicting future behavior based on historical data. Advantages of the Derivative Approach: Precise: It provides a precise measure of how a function is changing at a given point. Widely applicable: Derivatives are fundamental in various fields, such as physics, economics, and engineering. When to Use the Derivative Approach: Problems that involve rates of change, optimization, or prediction. Complex systems where small changes can lead to significant consequences. Choosing the Right Approach Selecting the appropriate problem-solving approach depends on the specific problem at hand, its characteristics, and the context in which it needs to be solved. There is no one-size-fits-all solution. To make an informed choice, consider the following factors: Problem Structure: Determine whether the problem has a clear iterative, recursive, or derivative structure. This will often guide your choice. Performance Requirements: If speed is of the essence, an iterative approach is typically faster. For elegance and clarity, recursion might be preferred. Problem Domain: Derivative methods are well-suited for solving problems related to rates of change, while iterative and recursive methods are more general-purpose. Resources: Consider the available resources, including memory and computational power, which can influence the choice of approach. In Summary Iterative, recursive, and derivative approaches are three essential problem-solving methods, each with its own strengths and weaknesses. The choice of method should be based on the nature of the problem, performance requirements, and the specific context in which the solution is needed. Being proficient in all three approaches allows a problem solver to select the most appropriate tool for the task at hand, increasing the chances of success in tackling a wide range of challenges.

Iterative vs Recursive vs Derivative Approaches

and are two well known foundational open source instruments for building infrastructure. They are very different by nature and application scenarios but also have lots similarities and intersections. I’m going to skip philosophical aspects and show practical example of the two technologies can augment each other. Kubernetes Terraform What is Common? and span infrastructure, applications and services. Multipurpose Provide and hide complexity of underlying infrastructure. abstraction and enable hybrid cloud development. Cloud agnostic allow to focus on the end result, bypassing interim steps programming. Desired state and Development Experience Terraform Configurations vs Kubernetes Manifests At the lowest level, working with both tools assumes writing DSL manifests in / or , verifying and submiting them for execution with or cli tools. While this approach is good for ad-hock tasks or as a “quickstart”, it lacks iterative deployment, stacks lifecycle management and 3rd party automation reuse. YAML HCL JSON terraform kubectl Modules and Helm packages The above limitation is partially addressed by using and with public for terraform. For Kubernetes, it’s improved by using with numerous . However, development on this level, assumes spending lot’s of manual efford in order to get things done and enable continuous deployment. workspaces modules registry helm charts available Out of the box CI/CD That’s the most interesting part, where terraform ecosystem is missing a lot comparing to kubernetes and you need to make your hands dirty to get basic CI/CD. opensource Some examples: is critical since you need to communicate with different cloud platform APIs and you cannot keep sensitive data in the code. While enabled in Kubernetes by design via encrypted secrets, and , you spend some valuable time integrating into your CI for terraform. Managing Secretes vault integration service catalog Out of the box continuous development for dev and prod. Simply saying, terraform is missing “Day 0" tools similar to , , or even , which implement typical development flows and form a basement of you pipelines. automated workflow skaffold draft gitkube metaparticle What if we could use Kubernetes tools for Terraform? Apart from levereging the largest and biggest ecosystem, we could unify tooling and development experience for the both, hence, minimize maintenance efforts. Prototype I have created draft pack, which illustrates the above and automates terraform development using skaffold, helm and has built-in , integrations. Essentially, each Terraform change results into an upgrade of helm release and it can be easily integrated with . Packer Vault Jenkins X Jenkins X Terraform pipeline reference architecture Quickstart Prerequesites: installed draft, skaffold, helm, kubernetes Clone repository and install draft pack: git clone cd infrapackcd tests/ && make addpack https://github.com/odzhu/infrapack.git Run Packer and Terraform build and deploy against sample code using skaffold make test Under the hood, it will: Create temp dir and copy sample terraform and packer code the. Execure draft create and enreaching the code with helm charts. Build and deploy. NAME:   infrapackLAST DEPLOYED: Wed May 30 18:58:11 2018NAMESPACE: defaultSTATUS: DEPLOYED RESOURCES:==> v1/ServiceAccountNAME                 SECRETS  AGEinfrapack-infrapack  1        0s ==> v1/RoleNAME                    AGEstate-writer-infrapack  0s ==> v1/RoleBindingNAME                    AGEstate-writer-infrapack  0s ==> v1/JobNAME                  DESIRED  SUCCESSFUL  AGEinfrapack-infrapack1  1        0           0s ==> v1/Pod(related)NAME                        READY  STATUS   RESTARTS  AGEinfrapack-infrapack1-97r8t  0/1    Pending  0         0s ==> v1/SecretNAME             TYPE    DATA  AGEstate-infrapack  Opaque  1     0s ==> v1/ConfigMapNAME                DATA  AGEscripts-infrapack1  3     0s Deploy complete in 585.116428ms0 tests$ helm listNAME      REVISION UPDATED                  STATUS   CHART      NAMESPACEinfrapack 1        Wed May 30 18:58:11 2018 DEPLOYED hcl-v0.1.0 default Switch to temp dir and start skaffold. cd /tmp/sandbox/ && skaffold dev The environment is ready for sandboxing, any code change will be automatically applied and you can inspect helm release for the details!

Iterative Terraform Development with Skaffold and Kubernetes

For aspiring game developers, breaking into the AAA gaming industry has long been a formidable challenge. Traditionally, high-budget studios with vast teams and years of experience have dominated the landscape, leaving small teams and solo developers struggling to gain a foothold. However, Epic Games has provided an unprecedented opportunity with the Unreal Editor for Fortnite (UEFN)—a powerful game development tool that enables newcomers to create and publish professional-quality experiences within Fortnite’s ecosystem. Unreal Editor for Fortnite (UEFN) This article explores how UEFN serves as a launchpad for new game developers, offering cutting-edge tools, a built-in player base, monetization opportunities, and a direct pathway to AAA game development. launchpad for new game developers What Is UEFN? What Is UEFN? UEFN is a specialized version of Unreal Engine 5, designed for Fortnite’s creative ecosystem. Unlike Fortnite’s original Creative Mode, which offers a more limited set of tools, UEFN provides access to powerful industry-standard features such as Nanite, Lumen, custom asset imports, terrain editing, and the new scripting language, Verse. These capabilities allow developers to craft AAA-quality levels, mechanics, and even entirely new game modes within Fortnite. specialized version of Unreal Engine 5 Nanite, Lumen, custom asset imports, terrain editing, and the new scripting language, Verse AAA-quality levels, mechanics, and even entirely new game modes Why UEFN Is a Perfect Gateway to AAA Gaming Why UEFN Is a Perfect Gateway to AAA Gaming 1. Hands-On Experience with AAA Tools (For Free) 1. Hands-On Experience with AAA Tools (For Free) UEFN grants developers access to the same technology used by major studios—without any upfront cost. Features like real-time global illumination, high-resolution asset rendering, and full physics-based environments enable small teams to learn and master AAA-grade development tools within a sandboxed environment. learn and master AAA-grade development tools For newcomers, UEFN provides a low-barrier entry point into the Unreal Engine ecosystem, allowing them to build a portfolio of real-world projects that demonstrate proficiency with UE5 workflows, optimization techniques, and multiplayer networking—all skills highly valued in the AAA industry. real-world projects UE5 workflows, optimization techniques, and multiplayer networking 2. Monetization Through Creator Economy 2.0 2. Monetization Through Creator Economy 2.0 One of the biggest hurdles for indie developers is financial sustainability. UEFN’s Creator Economy 2.0 program provides a built-in monetization model, where developers earn revenue based on player engagement. Unlike traditional indie games that require marketing and distribution efforts, UEFN allows creators to tap into Fortnite’s massive player base, offering immediate exposure and a chance to earn while they build. Creator Economy 2.0 massive player base By publishing experiences within Fortnite, developers can generate income that funds future projects or serves as proof of concept when pitching to publishers and investors. 3. Industry Recognition and Career Opportunities 3. Industry Recognition and Career Opportunities Successful UEFN developers have the opportunity to gain visibility within the gaming industry. Epic Games actively supports and spotlights high-quality UEFN projects, providing a direct line to AAA studios looking for fresh talent. By showcasing polished work within Fortnite, developers can attract job offers, collaborations, or even investment in their own independent studios. high-quality UEFN projects AAA studios looking for fresh talent In addition, many game studios are now recruiting directly from the UEFN creator pool, recognizing it as a proving ground for game design, level creation, and scripting expertise. recruiting directly from the UEFN creator pool game design, level creation, and scripting expertise 4. A Built-In Player Base for Testing and Iteration 4. A Built-In Player Base for Testing and Iteration One of the most significant challenges for indie developers is gaining players. In traditional game development, launching a game requires significant marketing efforts, which often prove to be costly and time-consuming. With UEFN, developers can skip this hurdle by instantly deploying their games to Fortnite’s millions of active users. skip this hurdle millions of active users This built-in audience allows developers to receive immediate feedback, refine their designs based on real-world data, and improve their skills in a live environment—mirroring the iterative process used in AAA game development. immediate feedback 5. Low-Risk Prototyping for Future Indie or AAA Projects 5. Low-Risk Prototyping for Future Indie or AAA Projects UEFN serves as an excellent low-risk testing ground for new ideas. Developers can prototype mechanics, experiment with game design principles, and polish their skills without investing in expensive software or distribution platforms. low-risk testing ground without investing in expensive software or distribution platforms For indie developers who eventually want to release standalone games, UEFN offers a risk-free way to validate ideas before committing to full-scale production. Many mechanics and concepts can be tested within Fortnite before being ported to a standalone Unreal Engine 5 project. risk-free way to validate ideas 6. Learning the Future of Game Scripting with Verse 6. Learning the Future of Game Scripting with Verse UEFN introduces Verse, a new scripting language designed specifically for Fortnite’s ecosystem but with broader implications for future game development. By learning Verse, developers gain a head start on a cutting-edge scripting language that may become a significant part of Unreal Engine’s future. Verse head start on a cutting-edge scripting language Verse allows for advanced game logic, dynamic environments, and customized gameplay experiences, providing skills that translate directly to AAA game development and live-service game operations. advanced game logic, dynamic environments, and customized gameplay experiences AAA game development and live-service game operations How to Get Started with UEFN How to Get Started with UEFN For those looking to jump into UEFN, here’s a step-by-step roadmap: Download UEFN – Available for free through the Epic Games Launcher.


Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started.


Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts.


Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence.


Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Download UEFN – Available for free through the Epic Games Launcher. Download UEFN – Available for free through the Epic Games Launcher. Download UEFN Epic Games Launcher Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started. Explore Tutorials and Documentation – Epic provides extensive guides, YouTube tutorials, and community forums to help newcomers get started. Explore Tutorials and Documentation Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts. Join the UEFN Creator Community – Engage with other developers, participate in Epic-hosted game jams, and learn from industry experts. Join the UEFN Creator Community Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence. Start Small and Iterate – Build simple experiences first, then gradually increase complexity as you gain confidence. Start Small and Iterate Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Monetize and Showcase Your Work – Once comfortable, start publishing experiences and applying for Creator Economy 2.0. Monetize and Showcase Your Work Creator Economy 2.0 Unreal Editor for Fortnite is more than just a tool for creating custom Fortnite experiences—it’s a gateway to the world of AAA game development. By leveraging its powerful features, built-in monetization, and direct exposure to millions of players, developers can hone their craft, generate income, and open doors to professional opportunities that were once out of reach. gateway to the world of AAA game development hone their craft, generate income, and open doors to professional opportunities For indie developers, students, and aspiring game designers, UEFN represents a golden opportunity to break into the industry—all while building games in one of the world’s most popular platforms. Whether your goal is to work at a major studio or launch your own indie game, UEFN is a proven pathway to success in modern game development. golden opportunity to break into the industry proven pathway to success in modern game development

Unreal Editor for Fortnite: A Game-Changing Gateway to AAA Development

Unreal Editor para Fortnite: Una puerta de entrada cambiante para el desarrollo AAA

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Sentient has carved out a unique path, blending blockchain technology with open-source AI to create a community-owned ecosystem. With over 1 million waitlist sign-ups for Sentient Chat in just 24 hours and a record-breaking 650,000 NFT mint for their decentralized AI model, Dobby, Sentient is redefining the future of AI. Sentient At the helm of this revolution is Himanshu Tyagi, Co-founder of Sentient and a Professor at the Indian Institute of Science, whose expertise in AI and blockchain is driving innovations like the Reasoning Agent and a Perplexity-rivaling chatbot. We sit down with Himanshu to explore the technical challenges, groundbreaking solutions, and ambitious vision behind Sentient’s rise. Ishan Pandey: Sentient Chat has exploded onto the scene with over 1 million waitlist sign-ups in under 24 hours. What technical challenges did you face in building an AI platform that’s both decentralized and consumer-friendly? Ishan Pandey: Himanshu Tyagi: Decentralization is foundational to our mission, extending far beyond just Sentient Chat. We believe that the future of AI must be open and community-driven, as decentralization uniquely empowers active community participation. This philosophy cascades directly into our products—most notably, Sentient Chat. Himanshu Tyagi: With Sentient Chat, our goal is to ensure that the community truly feels ownership. Users aren't merely passive consumers; they are creators, molders, and stewards of the platform. They have full control—from the underlying AI model and agents, to data and governance—enabling them to shape the product precisely to fit their diverse needs and use cases. Critically, decentralization and usability reinforce each other in Sentient Chat. By empowering users to customize and enhance the platform, every innovation developed by one user or team becomes accessible to the broader community. This collective contribution enriches the platform's usability, continually driving improvements and innovations that benefit everyone involved. Ultimately, decentralization doesn't complicate the user experience—it enhances it, creating a vibrant ecosystem where collective creativity and collaboration translate directly into a more powerful, flexible, and user-friendly platform. Ishan Pandey: You’ve positioned Sentient Chat as a competitor to Perplexity, powered by Dobby, the world’s first community-owned AI model. Can you walk us through the technical innovation behind Dobby and how it supports Sentient Chat’s unique capabilities? Ishan Pandey: Himanshu Tyagi: AI is rapidly becoming the intermediary between information networks and users, granting it significant power to influence user perspectives based on inherent biases. Our goal is to empower communities by enabling them to align AI models according to their specific values, thus promoting a diverse ecosystem of "Loyal AI" models. While biases will always exist due to training data, users should not be limited to generalized models that simply average internet opinions. Instead, we envision users selecting from a wide array of models aligned with distinct community values. Himanshu Tyagi: Dobby represents the first of such models, explicitly aligned with the crypto community's values—pro-crypto and pro-personal freedom. With Dobby, we've achieved three notable technical breakthroughs: Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks.


Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile.


Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks. Effective Value Alignment Without Performance Loss: Aligning AI models to specific values through fine-tuning is notoriously challenging, often leading to degraded performance and incoherent outputs. With Dobby, we've successfully fine-tuned the Llama model to embrace specific values while maintaining excellent performance across critical benchmarks. Effective Value Alignment Without Performance Loss: Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile. Human-centric Tone and Freedom of Expression with Safety: Achieving our desired alignment required removing existing guardrails from Llama and carefully rebuilding them. Surprisingly, this process enhanced the model’s tone, resulting in more natural and human-centric interactions. Even with relaxed constraints, Dobby remains safe, as demonstrated by metrics from our "Sorry Bench." Moreover, our Dobby-mini 8B and Dobby 70B models exhibit granular control over specific safety dimensions (such as hate speech, financial, medical, and legal advice, and explicit content), allowing us precise adjustments of the model's safety profile. Human-centric Tone and Freedom of Expression with Safety: Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Fingerprinting for the Most Distributed Model Ownership Ever: Dobby is currently the most widely distributed model, with over 660,000 owners. This unprecedented level of distribution is facilitated by our innovative fingerprinting technology. Fingerprinting injects unique key-response pairs into the model, enabling robust verification of ownership. These fingerprints have a negligible impact on overall model performance and are resilient to removal through fine-tuning, model merging, or other modifications. Fingerprinting for the Most Distributed Model Ownership Ever: Sentient Chat is our AI-powered search platform, designed to showcase and provide users with a direct experience of the intangible concept of Loyal AI. This platform demonstrates the competitive edge of open-source ecosystems, successfully outperforming platforms like Perplexity and matching the search benchmarks of ChatGPT. Powered by Dobby, Sentient Chat illustrates how Loyal AI can directly influence the information delivered to users, presenting content concisely and engagingly. The integration of Dobby into Sentient Chat has notably improved the efficiency and entertainment value of search interactions. With Dobby's clear, human-like, no-nonsense communication style, users can quickly and effectively access the information they seek, highlighting the platform's distinct advantage in AI-driven search experiences. Ishan Pandey: One of the most intriguing aspects of Sentient Chat is its Reasoning Agent, equipped with tools like search, calculator, and think. How did you architect this agent, and what technical hurdles did you overcome to make reasoning a core strength? Ishan Pandey: Himanshu Tyagi: Recent advancements have shown that generating executable Python code for tool calling yields a significant boost in performance compared to conventional JSON-based approaches. In particular, LLM’s are inherently adept at compressing the action-space of tasks using code. This naturally prompted the use of code as the mode of expression for our reasoning agent. Sometimes LLM's write code that does not execute/run the way it is intended to, this is remedied by a Python interpreter which checks if the code generated executes or throws errors, if it throws errors, then it will keep regenerating the code (with the previous error traces) until it outputs satisfactory code. Himanshu Tyagi: Another caveat is that the LLM may generate functional code that does not do what it is intended to do (e.g. calls the calculator tool instead of searching); the reasoning agent is capable of digesting the output of the information and assessing whether it fits with, on a general level, what it expects from the particular code/tool call. We tested two different types of agents in the Open Reasoning Agent portion of the ODS framework: Chain-of-Thought with ReAct agent and Chain-of-Code with CodeAct agent. We tested two different types of agents in the Open Reasoning Agent portion of the ODS framework: Chain-of-Thought with ReAct agent and Chain-of-Code with CodeAct agent. In the Chain-of-Thought ReAct architecture, the agent follows an iterative reasoning-action loop. First, the 'Think' step allows the agent to reason internally, articulating plans or interpreting intermediate results in natural language. Whenever the agent encounters uncertainty or lacks critical information, it invokes the 'Search' tool, actively retrieving external information from the web. For arithmetic or computational tasks that require high precision, the agent utilizes the 'Calculate' tool. Thus, CoT-ReAct continuously cycles through thinking, searching, and calculating as needed, until it arrives at a reliable solution. On the other hand, the Chain-of-Code or CodeAct agent employs executable code to perform its reasoning tasks. Initially, the agent thinks through its strategy, similar to CoT-ReAct. However, instead of relying purely on natural language, it generates executable Python code as part of its reasoning. The 'Calculate' function is seamlessly embedded here, directly integrated into the Python execution environment. This allows for accurate computations and algorithmic reasoning. Like CoT-ReAct, CodeAct also uses the 'Search' tool when additional external information is necessary, incorporating the results directly into its code-generation and execution processes. The key difference lies in how each agent interacts with these tools. For CoT-ReAct, 'Think', 'Search', and 'Calculate' are distinctly separate actions explicitly chosen during reasoning. For CodeAct, while 'Think' remains an explicit planning step, 'Calculate' is inherently built into the code execution itself, making computational tasks integral rather than external. 'Search' remains similarly explicit in both approaches, providing vital external context whenever the agent's internal knowledge is insufficient. Ishan Pandey: Sentient completed a record-breaking 650,000 NFT mint for Dobby, tying ownership to a decentralized AI model. What technical infrastructure did you build to handle this scale, and how does blockchain enhance the community ownership aspect? Ishan Pandey: Himanshu Tyagi: This was not a technically challenging problem – modern blockchains can easily handle such load. The most interesting part here is that this is the first time anybody has demonstrated this scale of “direct democracy” in model governance. In centralized companies, this governance is done by a tiny alignment team which has sometimes even modified the model to match its own beliefs. Anthropic had done some simple experiments under the name of constitutional AI with about 1000 people, but nothing close to this open governance by community. We wanted to give this power to the people and blockchains are perfect instruments for that. Dobby community already decided what kind of persona Dobby should have, leashed or unhinged (they chose unhinged over leashed). In future, all decisions about Dobby-unhinged model will be taken by this community, who will also get rewarded as model usage increases. Himanshu Tyagi: Going forward, these ownership NFTs will be converted to ownership tokens on our blockchain which can be used to govern alignment and updates to this model. Furthermore, the associated fingerprints will be committed as Merkle root to the same contract – anyone can verify the identity of the model by asking our fingerprint queries, and can check that the same queries are committed with the ownership contract. Ishan Pandey: Your Reasoning Agent integrates real-time tools and aims to tackle ambiguous, subjective tasks. How does this differ from existing reasoning agents, like OpenAI’s Deep Research, and what novel solutions did you implement to push the boundaries of AI reasoning? Ishan Pandey: Himanshu Tyagi: When comparing our open-source platform to closed-source competitors, the primary competitive advantage of closed systems has traditionally been their technology. However, platforms like Sentient Chat and Open Data Sources (ODS) have effectively leveled the playing field by democratizing AI knowledge, making our framework completely open to the community. Closed-source platforms, such as those by OpenAI, operate as black boxes; neither you, I, nor anyone else can fully comprehend their internal processes. Himanshu Tyagi: In contrast, our open framework invites continuous innovation from the wider community, enabling rapid identification and resolution of gaps that might otherwise remain unnoticed. This collaborative, open-input and open-output approach is key to building the best possible search platform. Our ultimate vision includes enabling users to contribute their own data sources and agents, thereby drastically enhancing Sentient Chat and ODS through collective, community-driven improvements. Ishan Pandey: You’ve emphasized Sentient Chat’s edge over competitors like Perplexity with its 15+ AI agent integration. What technical challenges did integrating multiple agents into a single chatbot interface present, and how did you solve them? Ishan Pandey: Himanshu Tyagi: Technically, we are addressing many complex software and design challenges to allow a broad variety of agents to participate in a singular user experience through Sentient Chat. We are also offering many tools to these agent builders such as our state-of-the-art AI search APIs, the Sentient Secure Enclave offering for building “unruggable agents,” etc. But more than addressing these technical challenges, the most interesting part here is the possibility of a completely new way to access internet knowledge. Himanshu Tyagi: For decades, Google has dominated search by focusing primarily on helping users find information online. Google's reliance on advertising revenue, rooted in recommending sources for information, inherently limits its ability to innovate beyond traditional information retrieval. AI, on the other hand, provides an opportunity to bypass mere information gathering, enabling users to directly execute tasks without needing separate analysis and action phases, which is our exact competitive advantage against the behemoth of Google. This ambitious vision requires a diverse ecosystem—one that integrates varied indexed data sources with numerous AI agents capable of performing specialized actions. The key to unlocking this potential lies in creating a transparent and open platform, actively incentivizing broad community participation. Data providers must clearly understand the value their contributions bring, while agent developers must seamlessly integrate their services to enhance overall functionality. Such an ecosystem must be community-governed, ensuring fairness, openness, and continual innovation driven by collective contribution and collaboration. By the way, a lot more than 15 agents are coming on Sentient Chat! Ishan Pandey: Looking ahead, what’s next for Sentient in terms of technical innovation? How do you plan to evolve the Reasoning Agent and expand the open AGI ecosystem? Ishan Pandey: Himanshu Tyagi: We see a future where AI becomes an intermediary between all human senses, global information, and social networks. Sentient’s vision is to make sure that this AI is loyal to us. This singular objective drives all our development. For reasoning agents, we want to make sure that developers get access to models whose reasoning capabilities and skills are aligned with their interests or the use case. Himanshu Tyagi: For instance, if you are building a sovereign agent that can control wallets, the underlying models should have reasoning capabilities to ensure that malicious actors cannot manipulate prompts and steal funds. This is alignment – we will be building such aligned models. Every developer building in the Open AGI ecosystem should have the option of using AI models that will be loyal to their application or users. Sentient Chat is the collective offering of all such agents built on Loyal AI. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: business blogging program business blogging program

How Sentient’s Reasoning Agent is Outsmarting the Competition: Inside Look 

كيف يعمل عميل التفكير في Sentient على تجنب المنافسة: النظر في الداخل

Com l'agent de raonament de Sentient està superant la competència: a l'interior

Cómo el agente de razonamiento de Sentient está superando a la competencia: Inside Look

Bagaimana Agen Penalaran Sentient Mengalahkan Pesaingnya: Pandangan dari Dalam

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Sentient の推論エージェントが競合他社を凌駕する方法: 内部の分析

Како резонантниот агент на Sentient ја надмудри конкуренцијата: погледнете одвнатре 

Bagaimana Sentient's Reasoning Agent Mengabaikan Persaingan: Melihat Dalam

Imaynatachus Sentient’s Reasoning Agent atipanakuymanta aswan yachayniyuq kachkan: Ukhupi qhaway 

Как рассуждающий агент Sentient вытесняет конкуренцию: Внутренний взгляд

Sida Wakiilka Sababta Sentient uu uga Wanagsan yahay Tartanka: Gudaha Fiiri 

Sentient的推理代理如何超越竞争:内部看法

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models References References GUIDELINES FOR ANNOTATING SOCIAL SUPPORT AND SOCIAL ISOLATION IN CLINICAL NOTES Social isolation (SI) is a state of little or no contact or support between an individual and their community. Social support (SS) describes the physical and emotional comfort derived from one’s family, friends, colleagues, and community. Although electronic health records (EHRs) capture SI and SS within the narrative text, manual abstraction of such data is time-consuming and labor-intensive. Research objective To develop a hybrid approach using rule-based and machine learning (ML)-based natural language processing (NLP) for detecting patient-reported SS and SI in EHRs. Annotation task overview The purpose of annotating clinical notes is to highlight key terms and phrases that characterize one’s social standing and, based on their context, make a clinical determination of an individual’s present or past status with respect to SI and SS. Annotation process Guideline development phase: Initially, two annotators will annotate fifty (50) notes. Then, inter-annotator agreement (IAA) will be calculated, and the annotation guidelines will be revised. This process will continue until a sufficiently high agreement (IAA ≥ 0.6) is achieved. The mismatched cases will be adjudicated by iterative discussion until a consensus is reached. The first 50 clinical notes will be added to the gold standard but excluded from the final IAA computation. Guideline development phase: Annotation Sites: Weill Cornell Medicine (WCM) and Mount Sinai Health System (MSHS). Annotation Sites: Annotation Phase: Each document will be independently annotated by two annotators. After the annotations are completed, the conflicts will be resolved by a third annotator. The annotation guideline will be updated if needed. Finally, the IAA will be calculated on all documents excluding the first 50 documents that are used to improve the annotation and guideline. Annotation Phase: Notes: MSHS will annotate psychiatry notes only as they don’t have access to primary care notes right now. In the annotation cohort, a single patient will have only one note. Entity attribute “negation” will be used only for “social isolation loneliness” category. Notes General guidelines for annotation Annotators will annotate all mentions in a clinical note that indicate the presence (or absence) of present/past SI and SS. There may be multiple such mentions contained within a single document. The goal is to make an aggregate determination based on the various annotations within the document. The overall annotation schema is given in Table S1. The annotators will annotate all terms and phrases mentioned in Table S3 that indicate an individual’s social status based on the annotation schema. A single document may contain multiple entities from both SI and SS. Further, the annotators will annotate at the document/note level based on entities and their context available in that specific note. There are two tags for annotating at the document level and these are mentioned in Table S1. Instructions for annotation Annotations will be performed using a BRAT annotation tool made available at individual sites. This is a browser-based tool that users may access online in a secure environment. Each user will be provided a username and password to access the tool. Before starting the actual annotation process, each user will participate in an orientation session to familiarize themselves with the BRAT tool. Within the tool, annotators will be granted access to 300 [MS] and 225 [WCM] clinical notes provided as .txt files. After opening a .txt file, annotators will highlight spans of pertinent text and assign tags based on the entities in Table 1. In some cases, the highlighted terms may be used in an unrelated context (e.g., templated information: social isolation []) and can be tagged with ‘no context.’ We plan to annotate social isolation and social support at a fine-grained entity level. There are five (5) categories for social isolation: “loneliness,” “no social network,” “no emotional support,” “no instrumental support,” and “social isolation general.” Similarly, social support has four (4) categories: “social network,” “emotional support,” “instrumental support,” and “general.” Each term can be annotated with temporal information (present or past) and with negation (if a negation term precedes or follows the highlighted key terms [only for loneliness]). There are two (2) categories for document/note level annotation and these are “social isolation” and “social support” with “yes” and “no”. The document/note level annotations are provided based on the entity level annotations and the rules are provided in Table S2. Table S3 shows the lists of key terms and phrases that we have used for the development of NLP algorithm. We also identified some key terms and phrases that were excluded while identifying the social support or isolation from clinical encounter notes (See Table S4). no social networ no emotional support no instrumental support “social isolation general.” “social network,” “emotional support,” “instrumental support, “general.” “social isolation” “social support” “yes” “no” Table S3: List of typical examples of terms and phrases in the clinical encounter notes that would indicate a patient’s social support and isolation status. Table S3: Table S4: List of typical examples of exclusion terms and phrases in the clinical notes. Table S4: File naming convention Documents are named in the following naming convention: <Index Numer><person_id><note_id>_<note_date>.txt <person_id> E.g.: 1 101 1001 10001 100001 2000-01-01.txt References 1. Lauren A. Lepow, Braja Gopal Patra, et al. 2021. Extracting Social Isolation Information From Psychiatric Notes in the Electronic Health Records. In AMIA 2021 Annual Symposium, San Diego, CA, USA. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

Guidelines for Annotating Social Support and Social Isolation in Clinical Notes

TL;DR - Play the game I’ve “written” — HERE. What’s your score? Add it in the comments. HERE A couple of days ago, I discovered that Gemini 2.5 Pro is now available in Cursor. Having previously built a business app with great support of Cursor (Sonnet 3.5 in ask mode), I was eager to explore how the Agent mode based on Gemini actually functions. My curiosity was further piqued after watching some impressive demos showcasing its capabilities. impressive demos For a long time, I had entertained the idea of creating a simple, retro-style game — a thief running from the cops while collecting gems. However, the challenge always seemed too daunting, given my packed schedule. But after seeing what was possible with Gemini, I decided to dedicate my Sunday afternoon to finally making it happen. The Vibe Coding Experience The experience turned out to be nearly 100% “vibe coding.” In just 12 minutes, I laid down the initial gameplay by simply instructing Cursor on my goals. From there, I iteratively refined the game, crafting levels, selecting colors, and implementing complex behaviors with just a few well-placed prompts. Ultimately, it took me around an hour to create a game that I genuinely enjoyed playing. To bring my characters to life, I leveraged ChatGPT’s image creation tools to generate sprites. They weren’t perfect, but they were good enough. I also selected colors using a handy design palette resource I found. And just like that, I had a functional, playable game. The Messy Side of AI-Driven Development But let’s not gloss over the downsides. The code is a mess. Everything is packed into a single file — game.js—spanning approximately 1,400 lines. The naming conventions are inconsistent, and odd comments are scattered throughout. game.js Another significant challenge was the Agent’s tendency to make the same mistakes repeatedly, requiring me to intervene with the same corrections over and over again. There were also instances where the Agent reached a dead end and couldn’t recover. Fortunately, I had set up Git for version control, which made reverting to a functional state relatively easy. This style of coding felt peculiar. I didn’t have much control, and things often seemed to unfold in an unpredictable manner. Occasionally, the Agent would break parts of the code unrelated to the task at hand. While this approach works well for quickly building small projects, maintaining the resulting code could prove to be a nightmare. Final Thoughts Overall, this “vibe coding” method has its merits. If you need to whip something up fast and don’t mind a bit of chaos in the code, it’s an exciting and efficient way to create. However, be prepared for the challenges of messy maintenance down the road. As AI development tools continue to evolve, it will be interesting to see how they address these limitations while maintaining their impressive speed and flexibility.

How I Built a Retro Game in an Hour: My Experience With Gemini 2.5 Pro and Vibe Coding 

Sounds too much to me – am I wrong? Sounds too much to me – am I wrong? Can LLMs replace software engineers? I started thinking about this again after a conversation with a former colleague. He wanted to use an AI agent to build an app that collects daily updates from various company services (Jira, BitBucket repos, Google Docs, Gmail), summarizes them, and presents the result to the user. That colleague has a strong background in both software engineering and management. But needless to say, the attempt failed miserably. The agent did generate a lot of code across many files, sure. But neither the code nor the ongoing interaction with the agent led to a functioning app. And I’m not even talking about code quality, suitability, or maintainability. It simply didn’t work — key parts (like integrations with external APIs) were hallucinated. That got me thinking. At the time, I had taken a two-month break from my career for personal reasons, and I hadn’t been keeping up with industry updates. I asked myself: Did I miss a major leap that made these kinds of expectations for AI more realistic? So the question is: can LLMs replace software engineers? And regardless of the answer — why or why not? There are two major parts to this question: LLMs and software engineering. Let’s look deeper into them both. What is an LLM? Large Language Models (LLMs) are programs trained to generate statistically likely sequences of tokens. They do this by identifying patterns in massive datasets. LLMs don’t comprehend the data, don’t think, and don’t learn in the way humans do. As coders, LLMs lack a real feedback loop - they can’t run, debug, and iteratively fix the code they write. One conclusion: LLMs need a very high-quality dataset to be competent in a particular domain. Keep that in mind. Software engineering vs coding Now let’s talk about software engineering (SE). It’s crucial to distinguish it from coding or programming. Programming is about writing code to get the computer to do something. Software engineering is a broader discipline that includes coding/programming as well as many other things. Engineering is about building reliable systems composed of many parts and connecting them with other systems. Engineers have done this for centuries across different domains. Software just happens to be one of them. I’d even say some SE principles mirror those in other engineering fields — like failover, for example. Do LLMs code well? Yes, LLMs are great at coding. That’s because there’s a wealth of structured programming problems and solutions on the internet — the kind of material LLMs were trained on. But we’ve already established that software engineering is not just coding. So when someone like Andrej Karpathy says that LLMs help a lot with his programming tasks, he’s not talking about conventional software engineering. As an AI/ML researcher, he mostly deals with algorithms, scripts, and complex mathematical computations. Andrej Karpathy says that LLMs help a lot with his programming tasks Andrej Karpathy says that LLMs help a lot with his programming tasks Are LLMs good software engineers? So are LLMs capable of doing software engineering work? Let’s think about it. When someone asks a software engineer if their job is creative or not, they often say it is. That’s because there’s no single blueprint you can reuse for every project. In that regard, SE craft involves: Knowing the building blocks: languages, libraries, frameworks, design patterns of different scale, databases, cloud platforms, message queues and numerous other services/products, you name it;
Creatively applying these building blocks according to meet specific business needs. Knowing the building blocks: languages, libraries, frameworks, design patterns of different scale, databases, cloud platforms, message queues and numerous other services/products, you name it; Creatively applying these building blocks according to meet specific business needs. Interestingly, both these things are dynamic, they both change in time and space. All the building blocks constantly evolve: new ones are invented, some get reworked a lot (look at how .NET or JavaScript has changed over the last decade), some get deprecated. This evolution is described in the public space via all kinds of documentation, StackOverflow threads, blogs, Github repos, etc. But this representation is often fragmented, outdated, or missing. On top of that, how we apply all these tools varies dramatically — across companies, teams, and projects, and with time. It is so because each business is unique, with its own history and its own dynamic (!) needs. And a giant part of that knowledge on how to apply the blocks isn’t public, and it will never be. It’s locked inside organizations and people’s heads. This makes me think the truly essential knowledge that makes a good software engineer simply isn’t publicly available — and never will be. And remember, LLMs need large, high-quality public datasets to learn from. I just don’t think such datasets exist for software engineering. And I can’t see how they ever could. So I conclude that LLMs won’t be nearly as successful at software engineering as they are at coding. Conclusions There isn’t enough reliable, public knowledge for LLMs to become good software engineers, let alone replace them.
As long as LLMs can’t comprehend, think, or learn like humans — and as long as the data gap exists — software engineers are safe.
If one day AIs can truly comprehend and think, we’ll have far bigger changes to worry about. Basically most of the jobs are done, not only software engineering 😀 There isn’t enough reliable, public knowledge for LLMs to become good software engineers, let alone replace them. As long as LLMs can’t comprehend, think, or learn like humans — and as long as the data gap exists — software engineers are safe. If one day AIs can truly comprehend and think, we’ll have far bigger changes to worry about. Basically most of the jobs are done, not only software engineering 😀 Extra thoughts Some important topics I didn’t cover, but worth mentioning: Bias: The more a concept appears in a dataset, the more likely an LLM is to output it. The tech industry is already prone to hype and bias, which could further degrade the usefulness of LLM-generated solutions.
Emergent capabilities: Some researchers think LLMs might develop new ‘emergent’ abilities as compute scales and training methods evolve (like with reinforcement learning). Maybe. But LLMs are black boxes. We don’t really understand how they work at scale. If they do evolve into something smarter, the implications will be global — not limited to software engineers.
Tools, not replacements: LLMs won’t replace engineers. But engineers will need to learn to use them effectively — and that’s a separate discussion.
More machine learning: I believe lot’s of medium+ sized companies will get more and more involved with their own experiments with machine learning, neural networks, LLMs to build their own tools of fine-tune existing ones. So eventually machine learning will be more and more prominent in the industry. Bias: The more a concept appears in a dataset, the more likely an LLM is to output it. The tech industry is already prone to hype and bias, which could further degrade the usefulness of LLM-generated solutions. Bias Emergent capabilities: Some researchers think LLMs might develop new ‘emergent’ abilities as compute scales and training methods evolve (like with reinforcement learning). Maybe. But LLMs are black boxes. We don’t really understand how they work at scale. If they do evolve into something smarter, the implications will be global — not limited to software engineers. Emergent capabilities Tools, not replacements: LLMs won’t replace engineers. But engineers will need to learn to use them effectively — and that’s a separate discussion. Tools, not replacements More machine learning: I believe lot’s of medium+ sized companies will get more and more involved with their own experiments with machine learning, neural networks, LLMs to build their own tools of fine-tune existing ones. So eventually machine learning will be more and more prominent in the industry. More machine learning Thanks for your attention. I’ll be glad for any feedback.

“AI Software Engineer” — Isn’t It Still a Bit Too Early for That?

Authors:
(1) Kinjal Basu, IBM Research;
(2) Keerthiram Murugesan, IBM Research;
(3) Subhajit Chaudhury, IBM Research;
(4) Murray Campbell, IBM Research;
(5) Kartik Talamadupula, Symbl.ai;
(6) Tim Klinger, IBM Research. Authors: Authors: (1) Kinjal Basu, IBM Research; (2) Keerthiram Murugesan, IBM Research; (3) Subhajit Chaudhury, IBM Research; (4) Murray Campbell, IBM Research; (5) Kartik Talamadupula, Symbl.ai; (6) Tim Klinger, IBM Research. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Symbolic Policy Learner 3 Symbolic Policy Learner 3.1 Learning Symbolic Policy using ILP 3.1 Learning Symbolic Policy using ILP 3.2 Exception Learning 3.2 Exception Learning 4 Rule Generalization 4 Rule Generalization 4.1 Dynamic Rule Generalization 4.1 Dynamic Rule Generalization 5 Experiments and Results 5.1 Dataset 5.1 Dataset 5.2 Experiments 5.2 Experiments 5.3 Results 5.3 Results 6 Related Work 6 Related Work 7 Future Work and Conclusion, Limitations, Ethics Statement, and References 7 Future Work and Conclusion, Limitations, Ethics Statement, and References 2 Background Text-based Reinforcement Learning: TBGs provide a challenging environment where an agent can observe the current state of the game and act in the world using only the modality of text. The agent perceives the state of the game only through natural language observations. Hence, TBGs can be modeled as a Partially Observable Markov Decision Process (POMDP) (S, A, O, T , E, r), where S is the set of states of the game, A is the natural language action space, O is the set of textual observations describing the current state, T are the conditional transition probabilities from one state to another, E are the conditional observation probabilities, r : S × A → R is a scalar reward function, which maps a state-action pair to the reward received by the agent. Text-based Reinforcement Learning: Inductive Logic Programming (ILP): ILP is a machine learning technique where the learned model is in the form of logic programming rules (Horn Clauses) that are comprehensible to humans. It allows the background knowledge to be incrementally extended without requiring the entire model to be re-learned. Additionally, the comprehensibility of symbolic rules makes it easier for users to understand and verify induced models and even edit them. Details can be found in the work of Muggleton and De Raedt (1994). Inductive Logic Programming (ILP): Answer Set Programming (ASP): An answer set program is a collection of rules of the form: Answer Set Programming (ASP): s(CASP) Engine: For this work, we have used s(CASP) ASP solver to predict an action. s(CASP) (Arias et al., 2018) is a query-driven, goal-directed implementation of ASP that includes constraint solving over reals. Goal-directed execution of s(CASP) is indispensable for automating commonsense reasoning, as traditional grounding and SATsolver based implementations of ASP may not be scalable. There are three major advantages of using the s(CASP) system: (i) s(CASP) does not ground the program, which makes our framework scalable, (ii) it only explores the parts of the knowledge base that are needed to answer a query, and (iii) it provides natural language justification (proof tree) for an answer (Arias et al., 2020). s(CASP) Engine: 3 Symbolic Policy Learner Deep reinforcement learning (DRL) has experienced great success by learning directly from high-dimensional sensory inputs, yet it suffers from a lack of interpretability. Interpretability of an agent’s action is of utmost importance in sequential decision-making problems, as it increases the transparency of black-box-style agents; it also helps RL researchers understand the high-level behavior of the system better. To make a system interpretable, one of the most widely used approaches is learning the agent’s policies symbolically. In our work, EXPLORER learns these symbolic policies in the form of logical rules represented in the ASP. An example of such a rule is - insert(X, fridge) :- apple(X) which can be translated as “X is insertable into a fridge if X is an apple” [2]. These learned ASP rules not only provide a better understanding of the system’s functionality but can also be used to predict the agent’s next action using an ASP solver. EXPLORER learns the rules iteratively (in an online manner) and applies the rules to predict an action in collaboration with the neural module. Our results show that this approach is very effective in terms of performance and interpretability. Partial Observability and Non-Monotonic Reasoning: EXPLORER works in a partially observable environment, where it needs to predict an action based on its prior knowledge. If EXPLORER fails, then it learns something new that will be applied in the next episode. The reasoning approach of EXPLORER is non-monotonic in nature: that is, what it believes currently may become false in the future with new evidence. We can model this using a non-monotonic logic programming paradigm that supports default rules and exception to defaults (Gelfond and Kahl, 2014). In this work, the belief of EXPLORER has been represented as an Answer Set Program in the form of default rules with exceptions. With the help of Inductive Logic Programming (ILP) (see Section 3.1) and Exception learner (see Section 3.2), these rules are learned by EXPLORER after each episode and then applied in the following episode. The agent uses an ASP solver to predict actions by utilizing the observation and the rules. Based on the outcome after applying the rules, the learned policies are updated with the exception (if needed), and new rules are learned as needed. Partial Observability and Non-Monotonic Reasoning: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [2] For ease of use, we retain action names as the predicate names; however, they are interpreted normally as adjectives.

Learning Logic in Games: How EXPLORER Combines NLP and RL for

Zug, Switzerland—31 March, 2025—Arcium, an encrypted supercomputer that brings a trustless, verifiable, and efficient framework to run encrypted computations, has launched the Encrypted Ecosystem—a network of projects exploring its technology to accelerate encrypted computation and unlock new possibilities in a data-driven world. Arcium Arcium Arcium is expanding its infrastructure to support new blockchain architectures and traditional enterprises, making privacy-focused solutions more accessible across industries. Arcium’s encrypted infrastructure is designed for broad adoption, supporting both blockchain architectures and traditional enterprises. Its modular, scalable, and blockchain-agnostic design makes it adaptable to a wide range of industries, enabling businesses to unlock value from private data without compromising security. The Encrypted Ecosystem highlights this versatility, with over 25 projects exploring Arcium’s technology across eight key sectors: DeFi: Jupiter, DarkLake, JupNet, Ranger, Titan, Asgard, Tower, Orca, and Voltr
AI: Assisterr, Charka, io.net, Nosana, AlphaNeural, and SendAI.
Consumer Applications: Cudis, dReader, Send Arcade, Solana ID, and Chomp
Payments: Squads, Sphere, and Iron
Interoperability: Wormhole and Union
DePIN: Spacecoin
Gaming: Send Arcade
NFTs: Metaplex DeFi: Jupiter, DarkLake, JupNet, Ranger, Titan, Asgard, Tower, Orca, and Voltr AI: Assisterr, Charka, io.net, Nosana, AlphaNeural, and SendAI. Consumer Applications: Cudis, dReader, Send Arcade, Solana ID, and Chomp Payments: Squads, Sphere, and Iron Interoperability: Wormhole and Union DePIN: Spacecoin Gaming: Send Arcade NFTs: Metaplex From onchain applications to off-chain implementations, Arcium leads the way in decentralized privacy and secure computation for both blockchain and traditional business applications. While chain-agnostic by design, Arcium is launching first on Solana, bringing encrypted capabilities to the ecosystem. Yannik Schrade, CEO and Co-Founder of Arcium, said: Yannik Schrade Yannik Schrade “We have always believed in the transformative potential of what we have built with Arcium, and establishing the Encrypted Ecosystem cements that as truth. Revolutionary infrastructure lays the foundation, but its true impact is realized when builders bring it to life. Arcium’s encrypted infrastructure enables use cases that were historically impossible, unlocking a new design space for applications and innovative ways to utilize private data. Seeing so many projects joining the encrypted computing revolution with us reminds me of the importance of the work we are doing at Arcium." “We have always believed in the transformative potential of what we have built with Arcium, and establishing the Encrypted Ecosystem cements that as truth. Revolutionary infrastructure lays the foundation, but its true impact is realized when builders bring it to life. Arcium’s encrypted infrastructure enables use cases that were historically impossible, unlocking a new design space for applications and innovative ways to utilize private data. Seeing so many projects joining the encrypted computing revolution with us reminds me of the importance of the work we are doing at Arcium." For more information about the Encrypted Ecosystem, please visit Arcium’s blog post. About Arcium: Arcium, is an encrypted supercomputer that brings a trustless, verifiable, and efficient framework to run encrypted computations. Founded in 2022, Arcium provides developers, applications and entire industries with a trustless, verifiable, and efficient framework to run encrypted computations. With Arcium, the internet can use data to its full extent in an entirely encrypted state. Backed by investors such as Greenfield Capital, Coinbase Ventures, Heartcore Capital, Longhash VC, __L2 Iterative Venture__s and Anagram Arcium's goal is to allow the entire internet to run on encryption. Greenfield Capital Greenfield Capital Coinbase Ventures Coinbase Ventures Heartcore Capital Heartcore Capital Longhash VC Longhash VC L2 Iterative Venture Anagram Anagram For media queries, please contact: arcium@wachsman.com arcium@wachsman.com arcium@wachsman.com This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Arcium’s ‘Encrypted Ecosystem’ Established To Accelerate Private Computation

የአርሲየም 'የተመሰጠረ ሥነ ምህዳር' የግል ስሌትን ለማፋጠን ተቋቁሟል

«Зашыфраваная экасістэма» Arcium створана для паскарэння прыватных вылічэнняў

„Криптирана екосистема“ на Arcium създадена за ускоряване на частните изчисления

Το «κρυπτογραφημένο οικοσύστημα» του Arcium δημιουργήθηκε για να επιταχύνει τον ιδιωτικό υπολογισμό

El «ecosistema cifrado» de Arcium se establece para acelerar la computación privada

Arciumin ”salausekosysteemi” perustettiin nopeuttamaan yksityistä tietojenkäsittelyä

Arcium- ի «կպիտակված էլեկտրոնային համակարգը» հիմնադրվել է անձնական համակարգչի արագացման համար

Arciumの「暗号化エコシステム」、民間コンピューティングを加速させる

„Ecosistemul criptat” al Arcium a fost înființat pentru a accelera calculul privat

Arcium 'Ecosystem Encrypted' waxaa loo yaqaan 'Cryptered Ecosystem' ee loo yaqaan 'Cryptered Ecosystem' ee loo yaqaan 'Private Computing'

Arcijumov „šifrovan ekosistem“ uspostavljen da bi ubrzao privatno računanje

Arcium’s “encrypted ecosystem” qilmadi, qilmadi qilmadi

Unlocking Generative AI Operations with Azure AI Foundry: A detailed Guide

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Mathematical Description and 2.1. Numerical Algorithms for Nonlinear Equations 2. Mathematical Description and 2.1. Numerical Algorithms for Nonlinear Equations 2.2. Globalization Strategies 2.2. Globalization Strategies 2.3. Sensitivity Analysis 2.3. Sensitivity Analysis 2.4. Matrix Coloring & Sparse Automatic Differentiation 2.4. Matrix Coloring & Sparse Automatic Differentiation 3. Special Capabilities 3. Special Capabilities 3.1. Composable Building Blocks 3.1. Composable Building Blocks 3.2. Smart PolyAlgortihm Defaults 3.2. Smart PolyAlgortihm Defaults 3.3. Non-Allocating Static Algorithms inside GPU Kernels 3.3. Non-Allocating Static Algorithms inside GPU Kernels 3.4. Automatic Sparsity Exploitation 3.4. Automatic Sparsity Exploitation 3.5. Generalized Jacobian-Free Nonlinear Solvers using Krylov Methods 3.5. Generalized Jacobian-Free Nonlinear Solvers using Krylov Methods 4. Results and 4.1. Robustness on 23 Test Problems 4. Results and 4.1. Robustness on 23 Test Problems 4.2. Initializing the Doyle-Fuller-Newman (DFN) Battery Model 4.2. Initializing the Doyle-Fuller-Newman (DFN) Battery Model 4.3. Large Ill-Conditioned Nonlinear Brusselator System 4.3. Large Ill-Conditioned Nonlinear Brusselator System 5. Conclusion and References 5. Conclusion and References 3.1. Composable Building Blocks NonlinearSolve.jl stands out for its modular design, allowing users to combine various building blocks to create custom solver algorithms. This section delves into the flexibility and power of NonlinearSolve.jl’s architecture, focusing on its core components: Jacobian Computation Algorithm, Globalization Strategy, and Descent Algorithm. Jacobian Computation: NonlinearSolve.jl supports a range of methods for Jacobian computation. Firstly, we support various automatic differentiation and finite differencing backends to compute the exact Jacobian. Additionally, to support Quasi-Newton methods, we provide different abstractions to iteratively approximate the Jacobian.


Globalization Strategy: This module facilitates the global convergence of the local solvers [Subsection 2.2] and can seamlessly integrate with the other blocks.


Descent Algorithm: These determine the direction vector given the Jacobian (or Higher-Order Derivatives) and the current value. Jacobian Computation: NonlinearSolve.jl supports a range of methods for Jacobian computation. Firstly, we support various automatic differentiation and finite differencing backends to compute the exact Jacobian. Additionally, to support Quasi-Newton methods, we provide different abstractions to iteratively approximate the Jacobian. Jacobian Computation: NonlinearSolve.jl supports a range of methods for Jacobian computation. Firstly, we support various automatic differentiation and finite differencing backends to compute the exact Jacobian. Additionally, to support Quasi-Newton methods, we provide different abstractions to iteratively approximate the Jacobian. Globalization Strategy: This module facilitates the global convergence of the local solvers [Subsection 2.2] and can seamlessly integrate with the other blocks. Globalization Strategy: This module facilitates the global convergence of the local solvers [Subsection 2.2] and can seamlessly integrate with the other blocks. Descent Algorithm: These determine the direction vector given the Jacobian (or Higher-Order Derivatives) and the current value. Descent Algorithm: These determine the direction vector given the Jacobian (or Higher-Order Derivatives) and the current value. These building blocks provide a modular way to construct algorithms like: The modular architecture of NonlinearSolve.jl not only supports a broad range of algorithms but also empowers users to craft sophisticated solver combinations. Among the possibilities, users can integrate Pseudo-Transient methods with Globalization strategies or pair Quasi-Newton Methods with Globalization techniques. This flexibility opens the door to creating tailored solutions that can more effectively navigate complex problem landscapes, highlighting NonlinearSolve.jl’s capability to adapt and optimize across a diverse set of nonlinear equation challenges. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) AVIK PAL, CSAIL MIT, Cambridge, MA;
(2) FLEMMING HOLTORF;
(3) AXEL LARSSON;
(4) TORKEL LOMAN;
(5) UTKARSH;
(6) FRANK SCHÄFER;
(7) QINGYU QU;
(8) ALAN EDELMAN;
(9) CHRIS RACKAUCKAS, CSAIL MIT, Cambridge, MA. Authors: Authors: (1) AVIK PAL, CSAIL MIT, Cambridge, MA; (2) FLEMMING HOLTORF; (3) AXEL LARSSON; (4) TORKEL LOMAN; (5) UTKARSH; (6) FRANK SCHÄFER; (7) QINGYU QU; (8) ALAN EDELMAN; (9) CHRIS RACKAUCKAS, CSAIL MIT, Cambridge, MA.

Modular Nonlinear Solvers: The Power of Composable Building Blocks in NonlinearSolve.jl

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Mathematical Description and 2.1. Numerical Algorithms for Nonlinear Equations 2. Mathematical Description and 2.1. Numerical Algorithms for Nonlinear Equations 2.2. Globalization Strategies 2.2. Globalization Strategies 2.3. Sensitivity Analysis 2.3. Sensitivity Analysis 2.4. Matrix Coloring & Sparse Automatic Differentiation 2.4. Matrix Coloring & Sparse Automatic Differentiation 3. Special Capabilities 3. Special Capabilities 3.1. Composable Building Blocks 3.1. Composable Building Blocks 3.2. Smart PolyAlgortihm Defaults 3.2. Smart PolyAlgortihm Defaults 3.3. Non-Allocating Static Algorithms inside GPU Kernels 3.3. Non-Allocating Static Algorithms inside GPU Kernels 3.4. Automatic Sparsity Exploitation 3.4. Automatic Sparsity Exploitation 3.5. Generalized Jacobian-Free Nonlinear Solvers using Krylov Methods 3.5. Generalized Jacobian-Free Nonlinear Solvers using Krylov Methods 4. Results and 4.1. Robustness on 23 Test Problems 4. Results and 4.1. Robustness on 23 Test Problems 4.2. Initializing the Doyle-Fuller-Newman (DFN) Battery Model 4.2. Initializing the Doyle-Fuller-Newman (DFN) Battery Model 4.3. Large Ill-Conditioned Nonlinear Brusselator System 4.3. Large Ill-Conditioned Nonlinear Brusselator System 5. Conclusion and References 5. Conclusion and References 2.2. Globalization Strategies. Globalization Strategies allow us to enhance the local algorithms described in Subsection 2.1 and ensure global convergence under suitable assumptions [23, Theorem 3.2, 4.5, 4.6]. 2.2.1. Line Search. Consider the 𝑁-Dimensional Generalized Rosenbrock Function, which is defined as: 2.2.1. Line Search. If we initialize the problem with (𝑢0)1 = −1.2, Newton-Raphson fails to converge to the solution [Figure 1]. Line search methods are globalization strategies that avoid such failures by determining a positive step length 𝛼𝑘 given the current iterate 𝑢𝑘 and the search direction 𝛿𝑢k The direction is often given by the Newton Descent Direction, Steepest Descent, or one of the Multi-Step Schemes described in Subsection 2.1. The optimal choice for the step length is given by: However, solving a global optimization problem on each step of the iterative nonlinear solver is prohibitively expensive. Instead, practical line search methods rely on selecting a set of candidate step sizes and terminating the search based on certain conditions: Armijo Rule and Curvature Criteria: The Armijo Rule or Sufficient Decrease criteria states that 𝛼𝑘 is acceptable only if there is a sufficient decrease in the objective function: Additionally, we use the curvature condition to filter out values for 𝛼𝑘 that satisfy sufficient decrease but are very close to the current iterate. This ensures that the algorithm makes reasonable progress at every step. where 𝑐2 ∈ (𝑐1, 1) and 𝑐1 ∈ (0, 1). These two conditions are collectively known as the Wolfe Conditions [26, 27]. Strong Wolfe Condition: Strong Wolfe conditions additionally restrict the curvature criteria to restrict the derivative from being too positive: Backtracking Line Search: In this method, we start with an initial step length (typically 1 for most root finding algorithms) and shrink the step length by a (adaptive) factor 𝜌 ∈ (0, 1). In this case, we automatically ensure that the algorithm is making reasonable progress; hence, we only need to check for the sufficient decrease criteria [Equation (2.16)] [23, Section 3.1, Sufficient Decrease and Backtracking]. NonlinearSolve.jl supports line search routines for its solvers using the LineSearches.jl package [20]. In Figure 1, we demonstrate how NonlinearSolve.jl provides a uniform API to switch between different line search routines like BackTracking [23], HagerZhang [28], MoreThuente [29], etc. Using line search methods, we successfully solve the generalized Rosenbrock function [Equation (2.12)]. 2.2.2. Trust Region Methods. As an alternative to using line search as a globalization strategy, trust-region methods restrict the next iterate to lie in a local 2.2.2. Trust Region Methods. region around the current iterate. These methods estimate a quadratic model for the objective function: and attempt to solve the following sub-problem: where Δ𝑘 is the trust region radius at the current iterate 𝑘. To perform an update to the current iterate and the trust region radius, we compute the ratio of the actual reduction to the predicted reduction (𝜌𝑘): If 𝜌𝑘 is greater than a threshold, i.e., there is a strong agreement between the predicted model and true system, we accept the step and increase the trust-region radius Δ𝑘. If 0 < 𝜌𝑘 << 1, we accept the step but keep the trust-region radius unaffected. Otherwise, the trust region is shrunk, and the step is rejected. The exact specification for thresholds for these choices depends on the underlying algorithms being used. NonlinearSolve.jl implements additional sophisticated algorithms to update the trust region – Hei [30], Yuan [31], Fan [32], and Bastin [33]. In Figure 2 and Section 4, we demonstrate how different radius update schemes achieve a compromise between the computational cost and solution accuracy. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) AVIK PAL, CSAIL MIT, Cambridge, MA;
(2) FLEMMING HOLTORF;
(3) AXEL LARSSON;
(4) TORKEL LOMAN;
(5) UTKARSH;
(6) FRANK SCHÄFER;
(7) QINGYU QU;
(8) ALAN EDELMAN;
(9) CHRIS RACKAUCKAS, CSAIL MIT, Cambridge, MA. Authors: Authors: (1) AVIK PAL, CSAIL MIT, Cambridge, MA; (2) FLEMMING HOLTORF; (3) AXEL LARSSON; (4) TORKEL LOMAN; (5) UTKARSH; (6) FRANK SCHÄFER; (7) QINGYU QU; (8) ALAN EDELMAN; (9) CHRIS RACKAUCKAS, CSAIL MIT, Cambridge, MA.

Globalization Strategies: What Do They Allow Us to Do?

Smart Queries, Smarter Answers: Adaptive-RAG in Action

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