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
Choosing the Number of Attention Heads from Intrinsic Dimension: A Small-Scale Study The paper tests a heuristic to set the number of transformer attention heads using the intrinsic dimension of token representations. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or programming assistants.
Choosing the Number of Attention Heads from Intrinsic Dimension: A Small-Scale Study The paper tests a heuristic to set the number of transformer attention heads using the intrinsic dimension of token representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation.
Choosing the Number of Attention Heads from Intrinsic Dimension: A Small-Scale Study The paper tests a heuristic to set the number of transformer attention heads using the intrinsic dimension of token representations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on transformer attention head count, not AI agents.
SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges The paper systematizes grading requirements for open-text legal exams and compares eight evaluation paradigms using human and model judges. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on LLM grading rubrics, not autonomous tool-using agents.
SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges The paper systematizes grading requirements for open-text legal exams and compares eight evaluation paradigms using human and model judges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on external document retrieval or RAG.
SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges The paper systematizes grading requirements for open-text legal exams and compares eight evaluation paradigms using human and model judges. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates legal essay answers, not code or software engineering.
SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges The paper systematizes grading requirements for open-text legal exams and compares eight evaluation paradigms using human and model judges. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model compression, quantization, or efficient inference.
SoK: Grading Open-Text Legal Answers, from Law School Exams to Trustworthy Rubric Judges The paper systematizes grading requirements for open-text legal exams and compares eight evaluation paradigms using human and model judges. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies to text legal examinations, not multimodal inputs.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on hallucination detection algorithms, not evaluation benchmarks.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study model compression, quantization, or efficient serving.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Operates on text token probabilities without multimodal data.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses hallucination detection, not multi-step autonomous agents.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on coding assistants or software maintenance.
Gray-Box Hallucination Detection in LLMs with Token Identities The paper introduces a lightweight gray-box hallucination detector combining token probabilities with vector representations of token identities. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve document retrieval or external grounding.
LintEval: Benchmarking LLMs on Linter-Guided File Refactoring The paper presents a multilingual benchmark to evaluate how language models resolve linter alerts while preserving program functionality. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation pipelines.
LintEval: Benchmarking LLMs on Linter-Guided File Refactoring The paper presents a multilingual benchmark to evaluate how language models resolve linter alerts while preserving program functionality. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on benchmarking code models, not autonomous planning agents.
LintEval: Benchmarking LLMs on Linter-Guided File Refactoring The paper presents a multilingual benchmark to evaluate how language models resolve linter alerts while preserving program functionality. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with source code in C++, Python, and Java.
LintEval: Benchmarking LLMs on Linter-Guided File Refactoring The paper presents a multilingual benchmark to evaluate how language models resolve linter alerts while preserving program functionality. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or serving.
LintEval: Benchmarking LLMs on Linter-Guided File Refactoring The paper presents a multilingual benchmark to evaluate how language models resolve linter alerts while preserving program functionality. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on code refactoring accuracy rather than deployment safety.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model pruning, quantization, or efficient serving.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on conversational empathy, not guardrails or safety attacks.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a conversational framework, not general benchmark evaluation.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to programming, software maintenance, or code.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on conversation support alignment, not multi-step tool-use agents.
FACES: Fine-grained Gated Semantic Alignment for Multimodal Emotional Support Conversation The paper introduces a gated semantic alignment framework with multi-source rewards to improve multimodal emotional support conversations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or knowledge base grounding.
What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not explore retrieval augmentation or external knowledge grounding.
What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines ambiguity estimation, not multi-step autonomous planning agents.
What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Uses Text-to-SQL as an evaluation setting, not code generation.
What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text QA and SQL ambiguities, not multimodality.
What Is a Clarification? Estimating Model-Dependent Input Ambiguity The paper formalizes clarification criteria to estimate model-dependent input ambiguity based on accessible and valid answers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, or efficient serving.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses multilingual data ordering, not safety or guardrails.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external database retrieval or grounding.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines multilingual pre-training data schedules, not autonomous agents.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on pre-training regimens, not benchmark evaluation methodology.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or software engineering tasks.
Language Exposure Matters: Distribution and Order in Multilingual BabyLM Pre-training The paper evaluates how language exposure distribution and ordering affect the performance of multilingual models trained with limited data. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text pre-training, not multimodal or vision tasks.
Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on web agent execution efficiency, not safety guardrails.
Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on GUI web navigation, not software code writing.
Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates web agent performance, but focus is on architecture.
Why Generate a Click? Plug-in Decision Heads for Web Agents This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on document retrieval or external knowledge bases.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Surveys feature interpretability, not standard LLM evaluation benchmarks.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Surveys mechanistic interpretability methods, not autonomous tool-using agents.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not target software engineering or code LLMs.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on retrieval-augmented generation systems.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Covers interpretability analysis rather than serving or inference efficiency.
Sparse Autoencoders for Mechanistic Interpretability in NLP: A Critical Survey of Recoverability, Interpretability, and Utility This paper surveys 138 studies on sparse autoencoders in NLP, examining dictionary design, feature recoverability, and practical utility. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on language model feature representations, not multimodal models.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with text embedding spaces and language generation.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on response diversity, not deployment safety or jailbreaks.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model compression, pruning, or quantization.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address external document retrieval or grounding.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on generation diversity rather than benchmark evaluation.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on embedding search for diverse generation, not agents.
Gap Detection: Mitigating Homogenization through Demand and Defensibility This paper introduces Gap Detection, an optimization method that identifies unoccupied semantic directions to reduce homogenization in open-ended model answers. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to coding assistants or software repair.
RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops generative reward models, not general evaluation benchmarks.
RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address document retrieval or knowledge augmentation.
RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on code synthesis, inspection, or program repair.
RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on reward model distillation, not autonomous planning agents.
RM-OPD: On-Policy Distillation for Generative Reward Models This paper presents RM-OPD, a framework that trains domain-specialized reward models via on-policy distillation into a generalist model. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Targets text preference reward models, not multimodal tasks.
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text LLM speculative decoding, not multimodal models.
TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding This paper introduces TreeSpark, a framework training semi-autoregressive drafters to generate candidate trees aligned with speculative verification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Accelerates token generation without addressing AI safety or guardrails.

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