Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| 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 | 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 | 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 | 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 | 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 | 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 | 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 | 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 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 | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Focuses on chemical prediction, not software code. | |
| 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 | 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 | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on chemical structure reasoning, not multimodal VLMs. | |
| 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. | |
| 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 | 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 | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not address retrieval-augmented generation. | |
| 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 | 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 | 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 | Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06 | Not relevant Does not involve retrieval-augmented generation. | |
| 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 | Multimodal models via arxiv · 15 Oct, 15:06 | Not relevant Focuses on molecular graphs and RL, not multimodal foundation models. | |
| 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 | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Does not address safety, jailbreaks, or guardrails. | |
| 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. | |
| 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 | 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 | 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 | 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 | LLMs for code via arxiv · 15 Oct, 15:06 | Not relevant Evaluates reasoning on code benchmarks, but primary focus is pruning. | |
| 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. | |
| 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 | LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06 | Not relevant Evaluates a training heuristic, not LLM evaluation benchmarks. | |
| 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. | |
| 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 safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Does not focus on safety, jailbreaks, or deployment reliability. | |
| 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. | |
| 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 | 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 | 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 | AI agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on LLM grading rubrics, not autonomous tool-using 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 | 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 | 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 | 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 | 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 | 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 | AI safety and reliability in deployment via arxiv · 15 Oct, 15:06 | Not relevant Focuses on code refactoring accuracy rather than deployment safety. | |
| 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 | 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 agents via arxiv · 15 Oct, 15:06 | Not relevant Focuses on benchmarking code models, not autonomous planning agents. |
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