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
Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction Presents ME-World, an egocentric world model generating synchronized multi-agent first-person video streams with fine-grained physical interactions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate safety, red teaming, or operational guardrails.
Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction Presents ME-World, an egocentric world model generating synchronized multi-agent first-person video streams with fine-grained physical interactions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on multi-agent egocentric video generation, not industrial manufacturing or inspection.
Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models Introduces grasp, a framework combining functional gradient boosting with LLM proposal generation to learn probabilistic logic programs from relational data. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on safety, red teaming, jailbreaks, or deployment reliability.
Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models Introduces grasp, a framework combining functional gradient boosting with LLM proposal generation to learn probabilistic logic programs from relational data. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Uses LLM as a proposal generator in functional gradient boosting, not autonomous agentic systems.
Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models Introduces grasp, a framework combining functional gradient boosting with LLM proposal generation to learn probabilistic logic programs from relational data. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a neurosymbolic method for logic programs rather than an LLM evaluation framework.
Looking Inside LLMs: Small-World Connectivity as a Signature of Reasoning Performance Analyzes attention-head activation graphs to show that small-world connectivity correlates with reasoning performance in large language models. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Analyzes internal attention graph metrics, not tool use or autonomous agents.
Looking Inside LLMs: Small-World Connectivity as a Signature of Reasoning Performance Analyzes attention-head activation graphs to show that small-world connectivity correlates with reasoning performance in large language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on internal attention network topology rather than safety, alignment, or guardrails.
A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space Proposes a formal structural framework defining context formation, abstraction, and invariance operations for cognitive representation and problem solving. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes theoretical cognitive representations without evaluating or benchmarking LLMs.
A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space Proposes a formal structural framework defining context formation, abstraction, and invariance operations for cognitive representation and problem solving. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Theoretical cognitive representation paper; does not address deployment safety or guardrails.
A Structural Theory of Cognitive Representation and Problem Solving,Contexts, Invariance, and the Knowledge Space Proposes a formal structural framework defining context formation, abstraction, and invariance operations for cognitive representation and problem solving. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents a theoretical cognitive framework, not LLM-based autonomous agent architectures.
Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate code generation, repair, or software engineering tasks.
Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies rule sensitivity in compliance classification, not multi-step tool-using language model agents.
Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on regulatory compliance decision sensitivity, not retrieval systems or RAG pipelines.
Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses entirely on text-based regulatory compliance without multimodal inputs.
Verdict Without the Rule: Diagnosing and Auditing Regulatory Rule Sensitivity in LLM Compliance Systems Evaluates whether LLM compliance systems adjust verdicts when governing rules are perturbed, finding many verdicts are insensitive to rule changes. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, quantization, or efficient serving.
Prior or Feedback? What an LLM Uses When Adapting Neural Operators Investigates whether LLM scientific agents rely on task priors or experimental feedback when configuring neural operator adaptations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an experimental analysis of agent decision drivers rather than an LLM benchmark.
Prior or Feedback? What an LLM Uses When Adapting Neural Operators Investigates whether LLM scientific agents rely on task priors or experimental feedback when configuring neural operator adaptations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Examines adaptation feedback mechanisms rather than deployment safety, jailbreaking, or guardrails.
Prior or Feedback? What an LLM Uses When Adapting Neural Operators Investigates whether LLM scientific agents rely on task priors or experimental feedback when configuring neural operator adaptations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, distillation, or efficient serving.
Prediction-Powered Data Fusion for Treatment Effect Estimation Develops a framework fusing small randomized controlled trials with large observational data to improve treatment effect estimation precision without bias. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses statistical treatment effect estimation, not LLM agents.
Prediction-Powered Data Fusion for Treatment Effect Estimation Develops a framework fusing small randomized controlled trials with large observational data to improve treatment effect estimation precision without bias. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not cover model compression, efficient inference, or small language models.
Prediction-Powered Data Fusion for Treatment Effect Estimation Develops a framework fusing small randomized controlled trials with large observational data to improve treatment effect estimation precision without bias. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on causal inference estimators rather than LLM benchmarks or evaluation methods.
Prediction-Powered Data Fusion for Treatment Effect Estimation Develops a framework fusing small randomized controlled trials with large observational data to improve treatment effect estimation precision without bias. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety guardrails, hallucination, or alignment in deployment.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Deals with general physics simulation rather than industrial visual inspection or defect detection.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a physics simulation method, not an LLM benchmark.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses contact simulation efficiency, not small language models or LLM quantization.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on rigid-body physics modeling rather than vision-language foundation models.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not explore AI safety, reliability guardrails, or alignment.
RiCo: Neural Simulation of Rigid-Body Interactions via Local Contact Reasoning Presents RiCo, a neural physics simulation method that models rigid-body interactions via sparse local contact surface reasoning. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on physical contact simulation rather than language model agents.
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Deals with medical imaging reconstruction, not LLM agents or tool use.
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not investigate AI safety, guardrails, or alignment.
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to medical clinical imaging rather than industrial manufacturing inspection or robotics vision.
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Addresses volumetric medical image restoration rather than multimodal text-image foundation models.
ContiLNN: Mitigating Slice Sampling Discontinuity with Liquid Neural Networks for Medical Image Restoration Proposes ContiLNN, which uses closed-form continuous-time modules to improve cross-slice continuity and restoration in 2D medical imaging. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an image restoration network architecture, not an evaluation benchmark.
Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates textual/code game environments, not multimodal vision-language processing.
Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, or lightweight serving.
Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not explore document retrieval, grounding, or RAG systems.
Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition Introduces AAArena, a benchmark evaluating how AI agents revise executable code policies across 12 adversarial games under fixed budgets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study safety alignment, jailbreaking, or deployment guardrails.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate model pruning, quantization, or small on-device models.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an instruction selection technique rather than an LLM evaluation benchmark.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text LLM fine-tuning, not multimodal models.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on SFT data selection for LLMs, not retrieval-augmented generation architectures.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address deployment safety, jailbreak robustness, or guardrails.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies supervised fine-tuning data selection rather than LLM agent planning or tool execution.
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution Analyzes long-tail concept learning during supervised fine-tuning using a theoretical prior barrier metric to guide data needs. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Addresses general concept fine-tuning, not specialized code generation or software repair.
Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models Distills geometric privilege into vision-language models during training to improve spatial reasoning without inference overhead. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates spatial reasoning on general 3D datasets, not industrial inspection or defect detection.
Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models Distills geometric privilege into vision-language models during training to improve spatial reasoning without inference overhead. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on spatial reasoning in vision-language models rather than tool-using autonomous agents.
Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models Distills geometric privilege into vision-language models during training to improve spatial reasoning without inference overhead. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a distillation training method rather than an evaluation benchmark.
Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models Distills geometric privilege into vision-language models during training to improve spatial reasoning without inference overhead. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not examine AI safety, guardrails, or deployment alignment.
Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict Proposes an evaluation framework to measure how LLM agents handle uncertainty and resolve conflicting knowledge during execution. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Focuses on text-based agent reasoning and retrieval conflicts, not multimodal models.
Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict Proposes an evaluation framework to measure how LLM agents handle uncertainty and resolve conflicting knowledge during execution. Peer-reviewed	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Evaluates epistemic humility across general agent tasks, not code generation or software engineering.
Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict Proposes an evaluation framework to measure how LLM agents handle uncertainty and resolve conflicting knowledge during execution. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, or efficient serving.
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, quantization, or serving latency.
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text-only legal reasoning models, not multimodal systems.
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Applies to legal text and contracts, not code generation or software engineering.
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Investigates internal reliance on cited authorities rather than RAG retrieval or grounding architectures.
Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness Audits legal chain-of-thought faithfulness by substituting cited authorities and observing whether model verdicts causally change. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies reasoning faithfulness in legal tasks, not multi-step tool-using agents.
HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing Introduces an annotated dataset of handwritten student answer sheets with complex formulas, text, tables, and noise artifacts. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on document recognition and OCR rather than multi-step LLM agents.
HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing Introduces an annotated dataset of handwritten student answer sheets with complex formulas, text, tables, and noise artifacts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address deployment safety, guardrails, or alignment.
HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing Introduces an annotated dataset of handwritten student answer sheets with complex formulas, text, tables, and noise artifacts. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to educational answer sheets rather than industrial manufacturing inspection or defect detection.
HANS: A Handwritten Answer Sheet Dataset for Noisy Hybrid Document Parsing Introduces an annotated dataset of handwritten student answer sheets with complex formulas, text, tables, and noise artifacts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a document dataset for OCR rather than an LLM capability evaluation benchmark.

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