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
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an uncertainty estimation method rather than benchmark evaluation.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on general tree-search agent policy, not code-specific assistants.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals with reasoning search trees, not multimodal vision-language tasks.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Addresses agent search tree navigation, not retrieval-augmented generation systems.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent search context management, not model compression.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent framework, not benchmark evaluation methodologies.
TreeFlex: Agents Navigating Their Own Search Trees The paper proposes a framework where an agent policy navigates a global search tree by alternating between reflection and execution roles. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent task planning, not deployment safety guardrails.
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on document parsing and captioning, not autonomous agents.
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a document parsing dataset rather than LLM-wide evaluation.
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on multimodal document layout parsing, not safety guardrails.
Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation The paper presents a benchmark dataset evaluating unified page-level document parsing alongside alternative text descriptions for visual elements. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses general document understanding, not industrial manufacturing defect inspection.
Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents The paper introduces a benchmark evaluating how well language models engineer isolated execution harnesses for coding agents using specification documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Evaluates software engineering capability, not safety or guardrail robustness.
Non-asymptotic Convergence of Average-reward Q-learning with Options The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on classical RL convergence rates, not efficient serving.
Non-asymptotic Convergence of Average-reward Q-learning with Options The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Contains theoretical convergence bounds, not LLM benchmark evaluation.
Non-asymptotic Convergence of Average-reward Q-learning with Options The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical RL analysis, not deployment safety or alignment robustness.
Non-asymptotic Convergence of Average-reward Q-learning with Options The paper provides finite-sample convergence guarantees for average-reward option Q-learning algorithms under inter- and intra-option settings. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents theoretical RL convergence proofs, not LLM agent systems.
Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry The paper proposes a policy optimization method that treats reasoning transitions as learned coordinates in an energy space to internalize self-correction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reasoning accuracy and latency, not deployment guardrails.
Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry The paper proposes a policy optimization method that treats reasoning transitions as learned coordinates in an energy space to internalize self-correction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an optimization algorithm, not evaluation datasets or benchmarks.
Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on robotics embodiment transfer rather than LLM-driven planning agents.
Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies general robotic manipulation transfer, not industrial vision defect inspection.
Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses motion transfer feasibility, not AI safety or jailbreaks.
Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand The paper retargets object motion rather than human hands from video demonstrations to generate post-training data for various robot embodiments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a robot retargeting method rather than benchmark evaluation.
From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent tool selection accuracy, not deployment safety.
From Relevance to Validity: Zero-Shot Tool Selection via Functional and Contextual Validation The paper proposes a progressive validation framework for zero-shot tool selection that evaluates functional compatibility and contextual relevance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a tool selection framework rather than an evaluation benchmark.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on parametric context retention rather than small/efficient model architectures.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on knowledge retention and editing, not safety guardrails.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Targets model memory updating rather than autonomous agent planning systems.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a memory consolidation method, not general LLM benchmarking.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Not focused on industrial defect inspection or manufacturing quality.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Evaluates general context consolidation across backbones, not core multimodal capability.
Cortex: Consolidating Context into Parametric Memory The paper presents a method to consolidate context gains into parametric model weights using prospective queries and low-rank feed-forward updates. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Addresses general context consolidation rather than software engineering tasks.
When Does Momentum Help in Reinforcement Learning with Verifiable Rewards? The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Investigates optimization convergence rates, not safety alignment or guardrails.
When Does Momentum Help in Reinforcement Learning with Verifiable Rewards? The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies optimization theory for RL post-training, not agent workflows.
When Does Momentum Help in Reinforcement Learning with Verifiable Rewards? The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on optimizer acceleration rather than model compression or inference.
When Does Momentum Help in Reinforcement Learning with Verifiable Rewards? The paper analyzes when first-moment momentum accelerates reinforcement learning with verifiable rewards under exact and sampled policy gradients. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Analyzes training optimization dynamics rather than benchmark evaluation methodologies.
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Presents a generic neural network optimizer, not AI agent systems.
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies general neural network loss curvature, not LLM safety guardrails.
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Targets general loss landscape optimization, not model compression or serving.
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM The paper introduces an adaptive perturbation method for sharpness-aware minimization that modulates perturbation radii using local loss curvature. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on an optimization algorithm, not LLM benchmark evaluation.
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on document entity extraction without retrieval-augmented generation systems.
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Deals strictly with text-based entity extraction from documents.
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Evaluates entity extraction tasks, not multi-step autonomous planning agents.
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates existing low-cost models rather than proposing efficiency techniques.
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence The paper evaluates failure modes and confidence calibration across frontier and lightweight language models during entity extraction from cyber threat intelligence reports. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Analyzes cyber threat entity extraction, not software code tasks.
OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning The paper introduces a compact tabular foundation model combining distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on tabular foundation models, not multi-step tool-using agents.
OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning The paper introduces a compact tabular foundation model combining distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on predictive tabular modeling rather than deployment safety.
OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning The paper introduces a compact tabular foundation model combining distribution-aware representations with dataset-conditioned sparse mixture-of-experts computation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a model architecture, not a general evaluation benchmark.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Empirical analysis of RAG factors, not a benchmark framework.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not evaluate jailbreaks, hallucinations, or safety guardrails.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies open-domain question answering, not software engineering.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text question answering, not multimodal models.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on QA retrieval architectures, not multi-step agents.
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks The paper analyzes the performance of retrieval-augmented generation architectures on open-domain question answering tasks involving culturally grounded South Asian narratives. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or efficient serving.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model quantization, pruning, or distillation.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on self-improvement rather than safety or reliability.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Targets automated scientific research rather than software engineering tools.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent swarm evolution, not document retrieval.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Does not address multimodal language-vision models.
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm The paper presents a recursive self-improvement framework using scientific agent swarms that mutate hypotheses and recombine code based on experimental feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent system rather than an evaluation benchmark.
From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616) Recreates and analyzes infrastructure misconfigurations that enabled privilege escalation and repository bypass during autonomous AI agent evaluations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Forensic security analysis rather than benchmark methodology.

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