Every paper checked — nothing hidden

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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
How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies conversational health advice, not autonomous multi-step agents.
How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
How Safe Is ChatGPT Health Advice? Evidence from Real User Conversations in Four Global South Countries Evaluates the safety of ChatGPT health advice using real user conversation logs across four Global South countries. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation architectures.
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Analyzes agent task performance and cost, not safety, jailbreaks, or guardrails.
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates coding agents on documents, not external RAG architectures.
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on coding agents processing PDFs into structured data, not multimodal architectures.
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization Introduces a benchmark evaluating how reliably software coding agents extract and digitize complex unstructured clinical trial protocol documents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression or efficient serving.
Imprinting: Knowledge Distillation Can Shift Student Behavior Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes distillation side-effects rather than benchmarking or evaluation metrics.
Imprinting: Knowledge Distillation Can Shift Student Behavior Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines text language model distillation, not multimodal models.
Imprinting: Knowledge Distillation Can Shift Student Behavior Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to code generation or software engineering.
Imprinting: Knowledge Distillation Can Shift Student Behavior Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies distillation behavior transfer rather than multi-step agent planning.
Imprinting: Knowledge Distillation Can Shift Student Behavior Demonstrates that knowledge distillation transfers safety dispositions and behavioral tendencies from teacher models to student models on unrelated tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not cover retrieval-augmented generation.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on cross-lingual retrieval accuracy rather than safety or guardrails.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text embedding retrieval across languages, not multimodal data.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model efficiency, distillation, or quantization.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on embedding spaces for dense retrieval rather than agent systems.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a steering method for retrieval, not an evaluation framework.
Retrieval Disagreement as a Signal: Diagnosing and Steering Multilingual Dense Embedders Analyzes retrieval divergence across languages in multilingual dense embedders and introduces disagreement-guided steering to align shared-index search results. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not focus on software engineering or code LLMs.
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Compares rapid decision models on multiple-choice datasets rather than agentic planning.
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software development or code generation.
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented generation.
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate model compression, quantization, or distillation techniques.
How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs Evaluates fast, system-one decision models and language models across fifteen medical datasets, testing their accuracy, format robustness, and abstention awareness. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Analyzes text-based medical decision tasks, not multimodal models.
Empathy Is Not the Scapegoat for Declines in Factuality and Reliability Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to programming or code generation.
Empathy Is Not the Scapegoat for Declines in Factuality and Reliability Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes conversational empathy and reliability, not autonomous multi-step agents.
Empathy Is Not the Scapegoat for Declines in Factuality and Reliability Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient inference.
Empathy Is Not the Scapegoat for Declines in Factuality and Reliability Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals solely with text-based dialogue, not multimodal models.
Empathy Is Not the Scapegoat for Declines in Factuality and Reliability Examines whether model empathy degrades reliability, showing that warm behavior effects depend on tasks and training approaches rather than an inevitable trade-off. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or document indexing.
CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study LLM agents, planning, or tool use.
CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation systems.
CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient inference.
CREATE: Creativity Evaluation of VLMs using the Visual Remote Associates Test This paper introduces a visual associative test benchmark to evaluate the lateral reasoning and creative association capabilities of vision-language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on lateral reasoning evaluation rather than deployment safety or guardrails.
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not involve model pruning, distillation, or quantization.
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on predicting annotator disagreement rather than LLM agents.
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation systems.
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code LLMs or software engineering.
Decomposing and Predicting Annotation Disagreement in Harmful Content Across Languages This paper predicts human annotator disagreement on harmful content across multiple languages by extracting interpretable semantic question features with models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text harmful-content classification, not multimodal models.
From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Examines clinical conversational evidence elicitation, not external document retrieval-augmented generation.
From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical clinical diagnosis, not code generation.
From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not cover model compression, distillation, or efficient serving.
From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes diagnostic evidence acquisition failure in LLMs rather than autonomous agent systems.
From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models This paper analyzes dynamic clinical evidence gathering in language models, identifying failures in evidence identification, updating, and diagnostic reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate vision or multimodal models.
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with textual legal reasoning and retrieval, not multimodal models.
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agentic RAG system rather than a new general evaluation framework.
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on legal retrieval-augmented reasoning rather than deployment safety guardrails.
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on legal question answering, not software engineering.
Legal-R1: Agentic Retrieval for Evidence-Based Legal Reasoning This paper introduces a multi-agent retrieval framework that coordinates search across legal statutes and precedent cases for grounded legal reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression or efficient serving.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies social conversation floor detection rather than language model evaluation.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human social interaction dynamics rather than LLM agents.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety or deployment reliability.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model efficiency or small language models.
Breakdown of Conversational Involvement: A Joint Multi-Modal Analysis of Conversation Floors and F-formation Detection with Multiple Features This paper analyzes multi-person conversational involvement by jointly evaluating speaking floors and spatial formations from multimodal video features. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Not related to retrieval-augmented generation systems.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on chemical sequence models rather than vision or multimodal foundation models.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines molecular representations, not code generation or software engineering.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address small model compression, distillation, or efficient serving.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation systems.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on chemical language model pretraining rather than LLM agents.
Does Pretraining Teach Chemical Language Models Chemistry? This paper evaluates pretraining on chemical language models across 64 molecular datasets, finding that gains saturate below gradient-boosted tree baselines. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not investigate deployment safety, guardrails, or alignment risks.

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