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
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a multi-task benchmark for recruitment tasks, not autonomous LLM agents.
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Benchmark paper comparing models, not developing small or efficient architectures.
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-based recruitment tasks without multimodal image, video, or audio data.
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Presents an operational benchmark rather than focusing on safety, red teaming, or guardrails.
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses HR and recruitment workflows, not software development or coding assistance.
EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Includes a retrieval subtask, but its primary focus is a general recruitment evaluation benchmark.
Attention-Graph Curvature for the Verification of Mathematical Reasoning This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical reasoning step verification, not retrieval-augmented generation.
Attention-Graph Curvature for the Verification of Mathematical Reasoning This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal attention curvature for step verification, not autonomous agentic systems.
Attention-Graph Curvature for the Verification of Mathematical Reasoning This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Investigates mathematical reasoning steps rather than software coding or repair.
Attention-Graph Curvature for the Verification of Mathematical Reasoning This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-based mathematical reasoning verifiers, without multimodal inputs.
Attention-Graph Curvature for the Verification of Mathematical Reasoning This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Adds computational overhead to forward passes rather than improving model efficiency.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents mechanistic interpretability analysis rather than standard benchmarks or evaluation methodologies.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates internal layer activations regarding self-representation, not document retrieval.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Examines geometric activation dynamics without focusing on compression or serving efficiency.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies internal activation representations of conversational roles, not code generation.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes internal representations of conversational roles, not tool use or autonomous agents.
What Is the "Self" of a Large Language Model? This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals strictly with text-based language model representations, not multimodal models.
Learn from Move: the Next Step for GUI Agents This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on GUI agent exploration capability rather than safety guardrails or alignment.
Learn from Move: the Next Step for GUI Agents This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Fine-tunes a 9B model but does not focus on efficiency, compression, or quantization.
Learn from Move: the Next Step for GUI Agents This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on GUI interaction and exploration rather than retrieval-augmented generation.
Learn from Move: the Next Step for GUI Agents This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies graphical user interface control, not software coding or program repair.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses visual question answering decoding, not programming or code tasks.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on contrastive decoding for VQA rather than external document retrieval.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a decoding technique for visual question answering, not agentic planning or tools.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on a decoding method rather than proposing a general LLM evaluation benchmark.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general VQA benchmarks, not industrial inspection or manufacturing defect detection.
Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates decoding contrast strategies rather than model compression or inference acceleration.
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language? This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies multilingual text diversity and collapse, not LLM agents or tool use.
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language? This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on generation diversity and homogenization across languages, not retrieval systems.
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language? This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, quantization, distillation, or efficient inference.
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language? This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines multilingual text generation without any visual or audio multimodal components.
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language? This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Analyzes multilingual open-ended text generation, not code generation or software engineering.
Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on social media sentiment analysis, not LLM agents.
Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not examine model compression, distillation, or efficient inference.
Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals exclusively with social media text data.
Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation pipelines.
Is the Platform Part of the Measurement? A Protocol and Simulation Study for Cross-Platform Equivalence of Machine-Labelled Policy Sentiment This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Examines sentiment analysis on social media platforms, not code.
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.
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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Presents mathematical token generation theory, not multimodal models.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical statistical physics paper, not an evaluation methodology.
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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval or external knowledge integration.
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	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	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	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	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	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	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	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	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	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	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	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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based language models and in-context learning.

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