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
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on code or programming.
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Text-only evaluation, not multimodal.
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Does not focus on AI agents or tool planning.
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies parametric internal knowledge extraction, not external retrieval.
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on diagnostic extraction metrics, not safety guardrails or jailbreaks.
MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation The paper presents a diagnostic evaluation framework to measure how inference-time techniques affect parametric knowledge extraction in models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies knowledge extraction techniques, not model compression or efficiency.
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or inference efficiency.
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Text-based chat retrieval, not multimodal.
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a retrieval framework, not general benchmark methodology.
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval accuracy, not AI safety or guardrails.
EGCForest: Structured Agent Memory Organization for Enterprise Group Chats The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on enterprise chat retrieval, not code generation.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on domain knowledge testing, not AI safety or robustness.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text-based knowledge benchmarks, not multimodal models.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Does not address AI agents.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not about coding LLMs.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model efficiency or compression.
OpenTWBench (Open Taiwan Benchmark): Taiwan-Domain Knowledge Evaluation for Large Language Models, and How the Measurement Apparatus Shapes the Score The paper introduces a two-part benchmark to evaluate language models on general and examination knowledge concerning Taiwan. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation.
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets The paper presents an audio evaluation benchmark assessing accuracy drops between speech-input and text-input pipelines in Southeast Asian languages. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation.
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets The paper presents an audio evaluation benchmark assessing accuracy drops between speech-input and text-input pipelines in Southeast Asian languages. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Voice-agent mention is secondary; core focus is an evaluation benchmark.
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets The paper presents an audio evaluation benchmark assessing accuracy drops between speech-input and text-input pipelines in Southeast Asian languages. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on retrieval-augmented generation.
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets The paper presents an audio evaluation benchmark assessing accuracy drops between speech-input and text-input pipelines in Southeast Asian languages. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not focused on safety, jailbreaks, or guardrails.
SEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets The paper presents an audio evaluation benchmark assessing accuracy drops between speech-input and text-input pipelines in Southeast Asian languages. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or efficient serving.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on prompt transfer evaluation, not AI safety or alignment.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on coding or software engineering.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Examines prompt transfer and token caps, not model compression.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates reasoning prompts, not multi-step agent systems.
RCCM: Diagnosing Gains and Harms in Reasoning-Prompt Transfer The paper analyzes question-level variations and prompt-execution mismatches when transferring reasoning prompts across different language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Text-based reasoning, not multimodal.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Not about agentic planning or multi-agent systems.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not focused on AI safety, attacks, or guardrails.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on chemical structure reasoning, not multimodal VLMs.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on efficiency or model compression.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not focus on retrieval-augmented generation systems.
Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction The paper proposes a diagnostic test hiding target identity to evaluate structure-property chemical reasoning in language models. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on chemical prediction, not software code.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model efficiency or compression.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text evaluation, not multimodal models.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not related to code generation or repair.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval-augmented generation.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on general evaluation rubrics, not deployment safety or guardrails.
Not All Criteria Matter: Importance-Aware Criteria Weighting for Rubric-Based LLM Evaluation The paper introduces a method that weights rubric criteria through pairwise model comparisons to improve evaluation accuracy. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on evaluation weighting, not AI agents.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular graphs and RL, not multimodal foundation models.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression or efficient serving.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on AI safety or deployment guardrails.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Not about general LLM evaluation or benchmarks.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies RL to chemical design, not software code.
MolHop: Scaffold-Hopping Molecular Design with Agentic Reinforcement Learning The paper presents an agentic reinforcement learning framework that navigates organized chemical spaces for cross-scaffold molecular discovery. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation.
MemDuet: Learning Dual-Process Memory for Long-Context Reasoning The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a framework evaluated on existing benchmarks, not benchmark design.
MemDuet: Learning Dual-Process Memory for Long-Context Reasoning The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not involve multimodal data.
MemDuet: Learning Dual-Process Memory for Long-Context Reasoning The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal agent recurrent memory rather than external RAG retrieval systems.
MemDuet: Learning Dual-Process Memory for Long-Context Reasoning The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Not focused on code generation or software engineering.
MemDuet: Learning Dual-Process Memory for Long-Context Reasoning The paper introduces a dual-process memory framework coordinating small and large models for efficient long-context reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address safety, jailbreaks, or guardrails.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not address retrieval or external knowledge.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes pruned model behavior, not LLM benchmarking methodology.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on model pruning termination, not agent architectures.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Text-only reasoning models, not multimodal.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines termination loops in pruned models, not AI safety or guardrails.
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models The paper shows pruned reasoning models fail by looping, proposing an activation steering method to restore proper termination. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates reasoning on code benchmarks, but primary focus is pruning.
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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not involve multimodal models.
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 safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety, jailbreaks, or deployment reliability.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates a training heuristic, not LLM evaluation benchmarks.

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