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
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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not involve retrieval-augmented 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	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	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	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	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	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	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	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	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	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.
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.
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.
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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not involve model pruning, distillation, or quantization.
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.
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	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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Examines clinical conversational evidence elicitation, not external document retrieval-augmented generation.
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	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	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	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	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	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.
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	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	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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not related to software engineering or code generation.
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	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	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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not investigate deployment safety, guardrails, or alignment risks.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal models or vision-language data.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on similarity metric inference rather than multi-step agent planning or tool use.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Not focused on coding assistants or software engineering tasks.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on general test-time similarity inference rather than document retrieval-augmented generation architectures.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a Bayesian test-time inference method, not evaluation benchmarks or methodology.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or efficient serving.
Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback This paper proposes a Bayesian method that infers task-aligned similarity metrics at test time using weak interactive feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, hallucinations, or guardrails.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech audio processing, not language model agents.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a speech detection method rather than LLM benchmarking methods.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Deals with speech representation, not retrieval-augmented generation.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines speech encoder representations, not LLM compression or serving.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech pretraining filtering rather than deployment safety guardrails.
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models This paper presents a method to detect spoken code-switching directly from speech representations using language-selective hidden dimensions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses multilingual spoken utterances, not code generation or repair.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on internal activation explanations, not retrieval-augmented generation.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Analyzes model interpretability rather than code synthesis or software engineering.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines internal oversight mechanisms, not model compression or efficient serving.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of language autoencoders, not multimodal vision-language systems.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates interpretability methods, not autonomous language model agents.
On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination This paper uses referential communication games to analyze why natural language autoencoders fail to provide discriminative oversight of model activations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes activation autoencoder theoretical oversight, not LLM benchmark evaluation.
Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports This paper evaluates language-model-generated disaster situation reports against human-written reports using a domain-specific sixteen-slot information schema. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates report generation quality rather than multi-step autonomous planning agents.

8,101–8,160 of 13,754← Previous Next →