Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
A Bethe-Ansatz Source of Early Token Correlations: A marginal-preserving construction for parallel proposals This paper constructs joint token probability laws from physics models to improve speculative verification in parallel language model decoding. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Mathematical study of token correlation, not software development.
A Bethe-Ansatz Source of Early Token Correlations: A marginal-preserving construction for parallel proposals This paper constructs joint token probability laws from physics models to improve speculative verification in parallel language model decoding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Purely mathematical study of token decoding dynamics, not safety.
A Bethe-Ansatz Source of Early Token Correlations: A marginal-preserving construction for parallel proposals This paper constructs joint token probability laws from physics models to improve speculative verification in parallel language model decoding. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical decoding laws, not agent architectures.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluated purely on textual mathematical and reasoning tasks.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on general RL training for mathematical and scientific reasoning, not coding assistants.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training algorithm, not an evaluation method or benchmark.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval mechanisms or external knowledge bases.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL value parameterization rather than model compression or inference efficiency.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL optimization for reasoning accuracy, not safety or guardrails.
TPPO: Tracking-Value PPO For Language Model Reasoning This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes an RL training algorithm for reasoning, not agent workflows or tool use.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Theoretical capacity bounds rather than practical distillation, pruning, or efficient inference.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multi-task representations theoretically, not LLM agents or tool use.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical analysis of task interference, not deployment safety or guardrails.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval methods or grounded generation.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Theoretical multi-task representation study, not software engineering.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies multi-task capacity theoretically without addressing multimodal architectures.
The Bell Boundary of Shared Representations This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates representational capacity limits rather than benchmark evaluation frameworks.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines in-context label bias, not agent planning or tool execution.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on classification failure modes, not software engineering or code.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based language models and in-context learning.
Label Collapse in Diffusion Language Model In-Context Learning This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies diffusion and autoregressive model biases, not compression or serving efficiency.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates agent compliance on software tasks, not model compression or efficiency.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not focus on retrieval-augmented generation architectures.
ANCHOR: Assessing Naturalistic Constraint Hierarchies and Ordering in Repository-Grounded Agentic Coding This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates code and repository tasks without multimodal perception.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on test harness generation rather than external document retrieval.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Pertains to software repositories and specifications, not multimodal data.
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications? This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on evaluating agent testing capabilities, not model compression or efficiency.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not examine model compression, pruning, or efficient serving frameworks.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation pipelines or document retrieval.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses multimodal uncertainty quantification, not software code generation.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on uncertainty quantification of model responses rather than agentic planning.
Estimating Uncertainty of Omnimodal Large Language Models via External Omnimodal Embeddings This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on uncertainty estimation techniques rather than benchmarking LLMs.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies prompt-level word meaning integration, not autonomous agentic systems.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies lexical semantics in prompts, not code generation or software engineering.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval systems, databases, or grounding.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on text instruction following and semantics.
Lexical Redefinition Reveals Context-Sensitive Instruction Following This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate compression, quantization, distillation, or serving speed.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, quantization, or serving techniques.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on narrative and episodic memory analysis, not code generation.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on cognitive event modeling, not retrieval-augmented generation.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text interview narratives without multimodal data.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses LLMs for narrative extraction, not autonomous planning or tool execution.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Applies models to cognitive memory research, not AI safety or deployment robustness.
Event Memory and Narrative Dynamics: A Large Language Model Framework for Multidimensional State Spaces This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a cognitive modeling framework, not an LLM benchmark or evaluation protocol.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on survival analysis of Reddit user data, not agentic LLMs.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address document retrieval or retrieval-augmented generation.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with mental health prediction, not code generation or program repair.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain-specific predictive modeling study rather than an LLM benchmark.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals only with text and behavioral features on social media.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies user behavior prediction on Reddit rather than AI system safety and reliability.
Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, or inference optimization.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies to text-based tool-use agents, not multimodal systems.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Targets general workflow policies (e.g., travel booking), not software engineering or coding.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or inference speed.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a runtime shielding framework rather than a general evaluation benchmark.
MENTOR: Procedural Shielding of Tool-Using LLM Agents This paper introduces a system to ensure tool-using language model agents comply with natural language company procedural manuals. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on procedural compliance and shielding rather than retrieval-augmented generation.
Beyond Model Bias: Cross-Entropy Based Rankings in Literary and Academic Texts Remain Stable across Small Language Models This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text surprisal and predictability, not multimodal architectures.
Beyond Model Bias: Cross-Entropy Based Rankings in Literary and Academic Texts Remain Stable across Small Language Models This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies cross-entropy rankings without focusing on safety, hallucination, or guardrails.
Beyond Model Bias: Cross-Entropy Based Rankings in Literary and Academic Texts Remain Stable across Small Language Models This paper studies cross-entropy surprisal metrics, showing that predictability rankings of texts remain consistent across multiple small language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates poetry and academic text predictability, not software engineering.

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