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
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Probes social reasoning in LLMs, but does not study autonomous agents, tool use, or multi-agent workflows.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not involve retrieval mechanisms or grounding on external databases.
Examining Social Attribution in LLM Reasoning: A Theory-Guided Probing Methodology Proposes a benchmark and probing methodology based on attribution theory to examine how language models assign responsibility and blame. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text-based social reasoning rather than multimodal processing.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational text-based interview interactions, not multimodal inputs.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on measurement of interview elicitation quality rather than safety, jailbreaks, or guardrails.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not investigate retrieval systems, chunking, or external document grounding.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on conversational interviewing for social research, not code generation or software engineering.
InterviewPlayground: A Validated Simulation Environment for Evaluating AI Interviewers Presents a simulated participant environment to evaluate multi-turn AI interviewers across market research and social science scenarios. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not explore model compression, quantization, distillation, or serving efficiency.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a world action model architecture rather than LLM benchmarking or evaluation frameworks.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on humanoid low-level control and proprioceptive action generation, not conversational LLM agents.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses physical control discrepancies in robotics rather than AI safety or guardrails.
Humanoid World Action Model With Joint State--Action Generation Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Targets humanoid whole-body control and physical manipulation, not industrial inspection or defect detection.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a robotics runtime architecture rather than an LLM evaluation benchmark.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on state provenance representation and replay runtime rather than industrial computer vision.
Traceable World State: A Provenance-Aware State Representation and Deterministic Replay Framework for Robotic Systems Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Targets robotic state management and auditing, not LLM code generation or software engineering agents.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on local post-hoc explanations for molecular GNNs, not deployed AI safety or robustness.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on GNN explainability for molecular classification, not LLM agents or tool orchestration.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Addresses graph explainability techniques rather than language model evaluation or benchmarks.
MPGE: A Multi-Perspective Graph Explainer for Molecular Classification Explanation Presents an explainer for molecular graph neural networks combining factual, counterfactual, and exemplar graph perturbations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, quantization, or efficient serving.
Structure Tax: How Structured Output affects LLMs Performance Evaluates how schema ordering impacts LLM performance under structured output constraints, showing reasoning-first designs prevent accuracy drops. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies structured output formatting and reasoning preservation rather than multi-step agent planning or tools.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not involve multimodal models or vision-language tasks.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a training method for adaptive reasoning rather than evaluation benchmarks or testing methodologies.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on reasoning efficiency rather than safety, jailbreaks, or alignment risks.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation or external document grounding.
When Should Agents Think? Adaptive Reasoning via Cross-Turn Estimation Proposes a training method to adaptively skip turn-by-turn agent reasoning when previous thoughts remain sufficient for future actions. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general agent reasoning across turns rather than software engineering or code generation.
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation Combines motif priors with search-tree diffusion to generate 2D and 3D molecular structures from spectroscopic data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies a method to a chemical benchmark; does not focus on LLM evaluation or benchmark design.
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation Combines motif priors with search-tree diffusion to generate 2D and 3D molecular structures from spectroscopic data. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on molecular structure elucidation via diffusion search trees, not multi-step LLM agents or tool use.
MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation Combines motif priors with search-tree diffusion to generate 2D and 3D molecular structures from spectroscopic data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on molecular property prediction rather than LLM safety, guardrails, or deployment reliability.
All Verdicts are Not Equal: Rethinking LLM Judge Reliability Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Audits text-based LLM judges rather than multimodal vision, audio, or document models.
All Verdicts are Not Equal: Rethinking LLM Judge Reliability Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on evaluation reliability rather than model compression, quantization, or efficient serving.
All Verdicts are Not Equal: Rethinking LLM Judge Reliability Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Audits general LLM evaluators rather than models writing, debugging, or reviewing code.
All Verdicts are Not Equal: Rethinking LLM Judge Reliability Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on LLM evaluator consistency rather than information retrieval or document-grounded generation.
All Verdicts are Not Equal: Rethinking LLM Judge Reliability Audits the reliability of LLM judges across multiple prompt formats, temperatures, and orderings, introducing a trustworthy verdict metric. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies evaluation methodology rather than systems where LLMs plan, use tools, or execute tasks.
Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes Analyzes training data extraction risks in language models when prompts include contextual conditioning such as retrieved documents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Analyzes training data extraction phenomena rather than proposing evaluation benchmarks.
Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes Analyzes training data extraction risks in language models when prompts include contextual conditioning such as retrieved documents. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates memorization extraction rather than multi-step agent planning or tool execution.
EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an optimization agent rather than general LLM evaluation benchmarks or assessment techniques.
EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution Proposes a self-evolving agent that allocates evaluation resources dynamically across candidate programs during iterative code evolution. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on computational efficiency in code search rather than AI safety or reliability.
Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models Introduces a framework that dynamically retrieves and recomposes prior latent reasoning steps to guide vision-language-action robot policies. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a robot policy architecture rather than LLM evaluation or benchmarking methods.
Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models Introduces a framework that dynamically retrieves and recomposes prior latent reasoning steps to guide vision-language-action robot policies. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses policy execution in robotics rather than AI deployment safety, alignment, or guardrails.
Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models Introduces a framework that dynamically retrieves and recomposes prior latent reasoning steps to guide vision-language-action robot policies. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on continuous robotic control via vision-language-action models rather than autonomous LLM agents.
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a video editing diffusion framework rather than an evaluation benchmark.
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Deals with generative video editing techniques rather than LLM agent planning or tool use.
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on visual synthesis rather than AI safety, guardrails, or alignment.
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on creative video editing rather than industrial defect detection or inspection.
A Geometric Approach to Soft Actor-Critic with Zonotopes for Locomotion Learning Applies zonotope geometry to adaptively scale critic pessimism and disagreement handling in soft actor-critic locomotion policies. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on deep RL optimization techniques rather than LLM evaluation or benchmark design.
A Geometric Approach to Soft Actor-Critic with Zonotopes for Locomotion Learning Applies zonotope geometry to adaptively scale critic pessimism and disagreement handling in soft actor-critic locomotion policies. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses continuous control RL rather than small language models or efficient inference.
A Geometric Approach to Soft Actor-Critic with Zonotopes for Locomotion Learning Applies zonotope geometry to adaptively scale critic pessimism and disagreement handling in soft actor-critic locomotion policies. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Studies locomotion control optimization rather than AI deployment safety or guardrails.
A Geometric Approach to Soft Actor-Critic with Zonotopes for Locomotion Learning Applies zonotope geometry to adaptively scale critic pessimism and disagreement handling in soft actor-critic locomotion policies. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Develops a reinforcement learning actor-critic algorithm rather than LLM-based autonomous agent systems.
Universal Textual Teaching for LLMs Presents a parameter-free distillation framework transferring knowledge between language models via an iteratively updated natural language guide. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on cross-model capability transfer rather than AI safety guardrails or alignment risks.
Universal Textual Teaching for LLMs Presents a parameter-free distillation framework transferring knowledge between language models via an iteratively updated natural language guide. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on teaching and knowledge distillation between models rather than autonomous tool-using agents.
Universal Textual Teaching for LLMs Presents a parameter-free distillation framework transferring knowledge between language models via an iteratively updated natural language guide. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a knowledge transfer framework rather than studying LLM evaluation or benchmark integrity.
Universal Textual Teaching for LLMs Presents a parameter-free distillation framework transferring knowledge between language models via an iteratively updated natural language guide. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on universal textual distillation, not LLM code tools.
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation Applies Weisfeiler-Leman graph kernel features to improve automated algorithm selection for constraint optimization problems without training GNNs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on algorithm selection for constraint programming rather than language model evaluation.
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation Applies Weisfeiler-Leman graph kernel features to improve automated algorithm selection for constraint optimization problems without training GNNs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Applies graph kernel features to constraint optimization rather than LLM agent workflows.
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation Applies Weisfeiler-Leman graph kernel features to improve automated algorithm selection for constraint optimization problems without training GNNs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety, alignment, jailbreaks, or deployment reliability risks.
Using Weisfeiler-Leman Features for Algorithm Selection in Constraint Optimisation Applies Weisfeiler-Leman graph kernel features to improve automated algorithm selection for constraint optimization problems without training GNNs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses constraint programming algorithms rather than efficient neural model compression or inference.
Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses continual learning in agents rather than safety, guardrails, or jailbreak prevention.
Use and Disuse: Intent-Structured Experience Consolidation for Memory and Learning in LLM Agents Introduces a hierarchical memory architecture that consolidates nested agent task intents into reusable knowledge across long horizons. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on an agent memory architecture rather than LLM benchmarking or evaluation frameworks.
LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a graphics rendering framework rather than an evaluation benchmark for models.
LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to computer graphics rendering, not AI safety or deployment robustness.

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