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
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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Deals with structured memory modules, not retrieval-augmented generation systems.
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.
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.
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.
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	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	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.
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.
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.
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.
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.
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.
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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on VLSI benchmark evaluations for RL, not LLMs.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on world modeling architecture rather than LLM evaluation benchmarks.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL optimization recipes, not model compression or quantization.
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.
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.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on harness policy distillation rather than model compression techniques.
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on federated learning optimization, not autonomous AI agents.
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes defense screening against Byzantine updates, not LLM benchmarking.
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning The paper studies Byzantine robustness in fair federated learning and proposes an update screening method using client trajectory consistency. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses federated learning robustness, not model compression or inference.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Evaluates multilingual creative writing, not retrieval-augmented generation.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses linguistic code-switching in creative writing, not software code.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on creative writing evaluation, not deployment safety risks.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Assesses multilingual text generation, not autonomous tool-using agents.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents an evaluation benchmark, not model efficiency techniques.
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing The paper introduces a benchmark and evaluation framework assessing context-sensitive code-switching by language models in creative writing tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Examines multilingual text generation without any visual or audio modalities.
CASI: Credit Assignment via Set Intervention for Agentic Retrieval The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an attribution algorithm, not benchmark evaluation methodologies.
CASI: Credit Assignment via Set Intervention for Agentic Retrieval The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval credit assignment, not safety or guardrails.
CASI: Credit Assignment via Set Intervention for Agentic Retrieval The paper presents an intervention method using Shapley values to assign credit to individual actions within multi-action agentic retrieval sets. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on retrieval policy optimization, not model compression or efficiency.
Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied spatial intelligence, not deployment safety guardrails.
Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence The paper introduces a brain-inspired reflection framework that diagnoses failure points to guide self-evolving agents in embodied spatial tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an agent architecture rather than an evaluation benchmark.
Beyond Static Cues: Unified Dynamic Detection of MLLM Backdoors via Dual-Flow Trajectory Analysis The paper detects backdoor triggers in multimodal language models by tracking output evolution trajectories across internal hidden layers. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies backdoor defenses in MLLMs, not autonomous planning agents.
Beyond Static Cues: Unified Dynamic Detection of MLLM Backdoors via Dual-Flow Trajectory Analysis The paper detects backdoor triggers in multimodal language models by tracking output evolution trajectories across internal hidden layers. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal safety attacks, not industrial manufacturing inspection.
Beyond Static Cues: Unified Dynamic Detection of MLLM Backdoors via Dual-Flow Trajectory Analysis The paper detects backdoor triggers in multimodal language models by tracking output evolution trajectories across internal hidden layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an attack detection framework, not a general benchmark.
Argument Graph Uncertainty: Quantifying Uncertainty from the Logical Structure of Reasoning Chains The paper estimates reasoning confidence by segmenting chain-of-thought traces into argument graphs evaluated through natural language inference. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on QA reasoning chain uncertainty estimation, not agents.

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