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
AI-Driven Disaster Response and Displacement Monitoring Surveys the application of artificial intelligence methods to humanitarian disaster response and displacement monitoring. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant General disaster response application overview, not technical AI safety, guardrails, or hallucination.
FlashAttention with Statistics: Low-Overhead Attention Features for Compact Hallucination Detectors Extracts attention statistics directly during fast attention computation to build lightweight hallucination detectors for language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on proxy hallucination detection, not multi-step agent planning or tool execution.
FlashAttention with Statistics: Low-Overhead Attention Features for Compact Hallucination Detectors Extracts attention statistics directly during fast attention computation to build lightweight hallucination detectors for language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a detection mechanism rather than evaluating or benchmarking LLM evaluation methods.
MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling Presents a training framework that teaches language model agents to transfer reusable problem-solving experience across structured task families. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a framework for agent learning, not a benchmark.
MetaExp: Learning to Self-Improve through Experience via Verifiable Cross-Task Environment Scaling Presents a training framework that teaches language model agents to transfer reusable problem-solving experience across structured task families. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on learning efficiency and task transfer, not safety.
ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing Combines language model dialogue with workflow automation to process satellite imagery for internal solitary wave detection and forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Describes a specific remote sensing pipeline, not general benchmark methodology.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Examines cognitive neuroscience alignment rather than LLM benchmarking standards.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not address retrieval-augmented generation architectures.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Investigates fMRI neural predictions, not code or software engineering.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Cognitive neuroscience alignment study, not AI safety or robustness.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Investigates brain response to text stories, not multimodal LLMs.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model compression or inference efficiency.
Decomposing Brain-LLM Alignment with Interpretable Semantic Representations Decomposes the alignment between language model activations and human brain scans using neurocognitively grounded semantic property representations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes brain alignment of LLMs, not multi-step autonomous agents.
QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering Improves deep search agents by refining search queries and filtering evidence to align retrieval with multi-step reasoning goals. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on search efficiency and accuracy, not AI safety.
QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering Improves deep search agents by refining search queries and filtering evidence to align retrieval with multi-step reasoning goals. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a retrieval agent method rather than benchmark evaluation.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Computational neuroscience paper, not deployment safety or guardrails.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Deals with brain semantic representations, not code generation.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not investigate retrieval systems or knowledge-augmented language generation.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on brain fMRI prediction from text, not multimodal VLMs.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on computational neuroscience and semantic representations, not agents.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates brain-prediction fidelity rather than LLM benchmarking methods.
SCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics Introduces a framework using language models to construct context-sensitive semantic representations that align with human brain activity during language comprehension. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model efficiency, pruning, or quantization.
Hierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck Studies a hierarchical conditioning architecture that predicts acoustic scenes directly from intermediate audio event probabilities. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies interpretability in audio networks, not AI safety deployment.
Hierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck Studies a hierarchical conditioning architecture that predicts acoustic scenes directly from intermediate audio event probabilities. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Investigates acoustic scene learning, not LLM evaluation frameworks.
Hierarchical Interpretable Multitask Audio Representation Learning: An Analysis of Event-Scene Relationships through the Information Bottleneck Studies a hierarchical conditioning architecture that predicts acoustic scenes directly from intermediate audio event probabilities. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies audio classification architectures, not language model agents.
CriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Demonstrates a game application rather than standard benchmark methodology.
CriticaLLM: Using LLMs as Autonomous Game Master for Tabletop Role-Playing Games Presents an automated framework using language models to manage storytelling, rules, and combat in tabletop role-playing games. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on tabletop game role-playing, not enterprise AI safety.
Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models Introduces an optimization framework that localizes functional subnetworks in language models to enable continual learning without catastrophic forgetting. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses catastrophic forgetting, not deployment safety or jailbreak defense.
Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models Introduces an optimization framework that localizes functional subnetworks in language models to enable continual learning without catastrophic forgetting. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a continual learning framework, not an evaluation benchmark.
Functional Subnetwork Localization for Efficient Continual Learning in Large Language Models Introduces an optimization framework that localizes functional subnetworks in language models to enable continual learning without catastrophic forgetting. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on continual learning parameter updates, not autonomous agents.
Agent Status Protocol (ASP): A Lightweight Specification for State Reporting in Multi-Agent Systems Proposes a lightweight protocol for AI agents to report runtime status and liveness to central servers and monitoring dashboards. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Defines an operational specification, not a testing benchmark.
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond Reviews and analyzes reinforcement learning methods used to guide and improve diffusion models across vision, language, and robotics. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Survey on diffusion models and reinforcement learning, not LLM agents.
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond Reviews and analyzes reinforcement learning methods used to guide and improve diffusion models across vision, language, and robotics. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Surveys diffusion model post-training without focusing on AI safety.
Reinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond Reviews and analyzes reinforcement learning methods used to guide and improve diffusion models across vision, language, and robotics. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Reviews RL-driven diffusion methods rather than evaluating LLM benchmarks.
When Is Muon Optimal? Radial Fragility and Minimax Geometry The paper investigates the mathematical conditions under which the Muon optimizer's polar update acts as an optimal step. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical optimization paper unrelated to deployment safety or robustness.
When Is Muon Optimal? Radial Fragility and Minimax Geometry The paper investigates the mathematical conditions under which the Muon optimizer's polar update acts as an optimal step. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Theoretical math and optimization paper, not an evaluation benchmark.
When Is Muon Optimal? Radial Fragility and Minimax Geometry The paper investigates the mathematical conditions under which the Muon optimizer's polar update acts as an optimal step. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on optimization mathematics rather than model compression techniques.
When Is Muon Optimal? Radial Fragility and Minimax Geometry The paper investigates the mathematical conditions under which the Muon optimizer's polar update acts as an optimal step. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Theoretical analysis of optimizer geometry, not AI agent planning.
Uncertainty Quantification in Pairwise Difference Learning for Classification The authors formulate classification and uncertainty quantification methods for pairwise difference learning frameworks. Peer-reviewed	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study model compression, quantization, or efficient inference.
Uncertainty Quantification in Pairwise Difference Learning for Classification The authors formulate classification and uncertainty quantification methods for pairwise difference learning frameworks. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies classification uncertainty, not LLM evaluation or benchmarks.
Uncertainty Quantification in Pairwise Difference Learning for Classification The authors formulate classification and uncertainty quantification methods for pairwise difference learning frameworks. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Investigates pairwise difference learning for classification, not language agents.
Uncertainty Quantification in Pairwise Difference Learning for Classification The authors formulate classification and uncertainty quantification methods for pairwise difference learning frameworks. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on general ML uncertainty decomposition, not deployed AI safety.
When Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark The paper compares the Muon optimizer against AdamW across controlled data, wall-clock time, and tuning budgets. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on optimization training efficiency, not safety or reliability.
When Does Muon Actually Win? A Paired Data-, Time-, and Tuning-Budget Benchmark The paper compares the Muon optimizer against AdamW across controlled data, wall-clock time, and tuning budgets. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies optimizer performance during training, not LLM agents.
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Evaluates raw dialogue text, not multimodal image or video tasks.
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Evaluates dialogue flows, not software engineering or code generation.
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate retrieval-augmented generation systems or knowledge retrieval.
DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Benchmark paper with no focus on small or efficient architectures.
BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition The paper demonstrates vulnerabilities in unilateral weight obfuscation schemes used for on-device secure language model inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on security vulnerabilities, not model evaluation benchmarks.
BiShield-TEE: On the (In-)Security of Unilateral Weight Obfuscation in On-Device TEE-Shielded LLM Partition The paper demonstrates vulnerabilities in unilateral weight obfuscation schemes used for on-device secure language model inference. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Examines hardware-enforced parameter protection, not multi-step agent planning.
Independent Data-Integrity Verification for LLM-Driven Functional Coverage Closure in Hardware Design The authors present an agent loop that closes functional test coverage in hardware design using independent simulation scoreboards. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Describes a specific agent workflow rather than general evaluation methodology.
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Examines agent memory structures, not external document retrieval systems.
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Discusses call overhead, not model architecture or efficient serving.
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates task performance and tool errors, not AI safety.
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses entirely on text-based tool calling, not multimodal inputs.
Rethinking the Role of Tool Graph Memory in Multi-Turn Tool Use The authors analyze tool graph memory in multi-turn agents and find that stored transitions provide little benefit over baselines. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Evaluates tool use generally rather than software engineering code models.
Learning to Search for Executable Patternmaking from Certified Programs The paper proposes a search framework to recover executable editing programs between before-and-after sewing pattern pairs. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on search over program steps, not autonomous agents.
Learning to Search for Executable Patternmaking from Certified Programs The paper proposes a search framework to recover executable editing programs between before-and-after sewing pattern pairs. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not address model compression, distillation, or serving efficiency.
Learning to Search for Executable Patternmaking from Certified Programs The paper proposes a search framework to recover executable editing programs between before-and-after sewing pattern pairs. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a search framework, not a general evaluation benchmark.
Learning to Search for Executable Patternmaking from Certified Programs The paper proposes a search framework to recover executable editing programs between before-and-after sewing pattern pairs. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on patternmaking synthesis, not deployment safety or robustness.

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