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
Maximising Transparency in Context Compaction Improves Retention of Facts and Commitments for Lightweight Coding Agents The paper presents a deterministic context compaction method designed to preserve factual history and commitments in lightweight coding agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on context management, not AI safety, jailbreaks, or guardrails.
Maximising Transparency in Context Compaction Improves Retention of Facts and Commitments for Lightweight Coding Agents The paper presents a deterministic context compaction method designed to preserve factual history and commitments in lightweight coding agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent memory retention methods rather than LLM benchmarking.
DreamHMR: From Video Generation to Human Motion Modeling via Structural Focusing The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses 3D human pose reconstruction, not language model agents.
DreamHMR: From Video Generation to Human Motion Modeling via Structural Focusing The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on vision geometric recovery, not AI safety or guardrails.
DreamHMR: From Video Generation to Human Motion Modeling via Structural Focusing The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates computer vision mesh recovery, not LLM benchmarks.
DreamHMR: From Video Generation to Human Motion Modeling via Structural Focusing The paper presents a framework adapting video generation priors to improve 3D human body and hand motion reconstruction from video. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Reconstructs human body meshes from video, not industrial defect inspection.
LLM-Based Policy Compliance Verification of Loan Applications The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on banking policy compliance, not software development or code.
LLM-Based Policy Compliance Verification of Loan Applications The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, pruning, or efficient inference.
LLM-Based Policy Compliance Verification of Loan Applications The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Processes text loan policies and applications, not multimodal inputs.
LLM-Based Policy Compliance Verification of Loan Applications The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents a document rule-checking pipeline, not multi-step autonomous planning agents.
LLM-Based Policy Compliance Verification of Loan Applications The paper describes an automated system using language models to verify corporate loan applications against internal bank lending policies. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an application prototype rather than general LLM evaluation methodology.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on spoken dialogue turn-taking, not planning or tool-using agents.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies conversational turn-taking dynamics, not AI safety or guardrails.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies multi-party spoken dialogue, not code generation.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses conversational floor control, not retrieval-augmented generation.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on spoken audio dialogue rather than general multimodal reasoning.
MultiPartyTurnBench: Benchmarking Floor Control in Multi-Party Spoken Dialogue The paper introduces a benchmark to evaluate conversational floor control, turn completion, and interruptions in multi-party spoken dialogue systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates dialogue baselines, not model efficiency or distillation.
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Studies internal representations, not retrieval-augmented generation.
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, quantization, or efficient serving.
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Investigates activation control, not programming or code generation.
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses exclusively on text LLM activation vectors.
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs The paper uses in-context neurofeedback sessions to evaluate whether language models can intentionally control their internal activations. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Analyzes internal model activation dynamics, not multi-step tool-using agents.
Value-Guided Repulsion for Offline Reinforcement Learning The paper proposes a value-guided repulsion method for offline reinforcement learning that aligns behavior regularization with action value estimates. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers core offline reinforcement learning algorithms, not LLM agents.
Value-Guided Repulsion for Offline Reinforcement Learning The paper proposes a value-guided repulsion method for offline reinforcement learning that aligns behavior regularization with action value estimates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with particle interaction in offline RL, not deployment safety.
Value-Guided Repulsion for Offline Reinforcement Learning The paper proposes a value-guided repulsion method for offline reinforcement learning that aligns behavior regularization with action value estimates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates offline RL benchmarks, not LLM evaluation.
HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on a clinical recommendation model, not general LLM benchmarking.
HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation The paper introduces a hierarchical memory agent framework for multi-center medication recommendation that accounts for hospital differences and drug safety constraints. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with structured memory modules, not retrieval-augmented generation systems.
SWE-Stream: Scaling Executable Training Data for SWE Agents via Progressive Construction and Elastic Streaming The paper introduces a framework that progressively constructs and streams executable training tasks from code repositories for software engineering agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on training data synthesis rather than proposing new evaluation benchmarks.
SWE-Stream: Scaling Executable Training Data for SWE Agents via Progressive Construction and Elastic Streaming The paper introduces a framework that progressively constructs and streams executable training tasks from code repositories for software engineering agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on software engineering performance rather than safety guardrails or reliability.
What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on coordination effectiveness rather than AI safety or reliability risks.
What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent negotiation and coordination, not retrieval-augmented generation.
What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Deals with social coordination scenarios, not software engineering or coding.
What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates text-based dialogue scenarios without multimodal modalities.
What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds The paper presents a benchmark evaluating whether conversational agents agree on verifiable plans that satisfy both parties' private conditions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes a policy optimization training method, not small model efficiency.
Drivers of LLM Consensus Formation Under Competing Incentives: A Simulation Study The paper evaluates how open and closed language models reach consensus when presented with competing payoff incentives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies multi-agent game-theoretic incentives, not safety, guardrails, or hallucination.
Drivers of LLM Consensus Formation Under Competing Incentives: A Simulation Study The paper evaluates how open and closed language models reach consensus when presented with competing payoff incentives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on multi-agent consensus dynamics rather than LLM benchmarking methodologies.
What and Where in Identifying and Quantifying Conventions in Humans and AI Agents The paper analyzes implicit communication conventions between humans and AI agents in the cooperative card game Hanabi. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates implicit conventions in Hanabi, not LLM benchmark evaluation.
What and Where in Identifying and Quantifying Conventions in Humans and AI Agents The paper analyzes implicit communication conventions between humans and AI agents in the cooperative card game Hanabi. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Analyzes human-agent coordination conventions, not AI safety or deployment guardrails.
Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties The paper introduces a framework providing analytical bounds for counterfactual reasoning in sequential decision processes under statistical and causal uncertainty. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Covers MDP counterfactual inference bounds, not LLM evaluation benchmarks.
Robust Counterfactuals for Markov Decision Processes under Statistical and Causal Uncertainties The paper introduces a framework providing analytical bounds for counterfactual reasoning in sequential decision processes under statistical and causal uncertainty. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies theoretical Markov Decision Processes, not LLM agents.
Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies benchmark translation for safety and reasoning, not multi-step LLM agents.
Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates general reasoning and safety, not code generation or software engineering.
Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve retrieval-augmented generation or external document grounding.
Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on multilingual evaluation datasets rather than small efficient model architectures.
Scaling Multilingual Safety and Reasoning Evaluation through Reasoning-Guided Translation The paper introduces a reasoning-guided translation framework to scale multilingual safety and reasoning benchmarks while preserving original task meanings. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with multilingual text translation and reasoning, not multimodal inputs.
Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement The paper explores reinforcement learning design configurations for post-legalization placement refinement in chip design. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers hardware circuit layout optimization, not AI deployment safety.
Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement The paper explores reinforcement learning design configurations for post-legalization placement refinement in chip design. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Applies RL to VLSI circuit placement, not LLM agents.
Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement The paper explores reinforcement learning design configurations for post-legalization placement refinement in chip design. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on VLSI benchmark evaluations for RL, not LLMs.
PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on world modeling architecture rather than LLM evaluation benchmarks.
PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Covers video world models in Minecraft, not industrial inspection.
PAWA: Panoramic Action-conditioned World Modeling for Multiple Agents The paper introduces a dataset and framework pairing synchronized point-of-view and panoramic video to evaluate multiplayer action-conditioned world models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies environment modeling in games, not AI deployment safety or guardrails.
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on model selection for time series, not small LLMs.
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers general time-series anomaly detection, not deployment safety or LLM guardrails.
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates classical time series anomaly detectors, not LLM benchmarking.
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection The paper proposes a retrieval framework that selects suitable anomaly detection models for time-series data using similarity search over existing datasets. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with time-series anomaly detection algorithms, not LLM agents.
P2Patch: Exploit-Guided Vulnerability Repair The paper presents an adversarial framework where an exploitation agent iteratively attacks and refines automated patches generated by a repair agent. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on automated program repair rather than general LLM evaluation benchmarks.
Long-Horizon RL Needs Muon The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes an RL training recipe rather than introducing benchmarks.
Long-Horizon RL Needs Muon The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on training optimizers, not safety monitoring or alignment guardrails.
Long-Horizon RL Needs Muon The paper investigates an asynchronous training recipe combining the Muon optimizer, token-averaged loss, and penalties for long-horizon agent tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL optimization recipes, not model compression or quantization.
Harness Policy Distillation: Aligning Agent Policies with Runtime Semantics The paper introduces a distillation method that trains agent policies on explicit runtime control semantics recovered from replayed execution traces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on policy distillation methods, not benchmark evaluations.

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