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
How Reliable are AMR Parser Outputs? Uncertainty Estimation for AMR Parsing Studies uncertainty estimation for abstract meaning representation parsers using top-k beam candidates and prediction rejection ratios. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not study autonomous agents, tool use, or multi-agent collaboration.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate jailbreaks, prompt injections, safety guardrails, or alignment.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate multi-step planning, tool-using, or interactive agents.
Tabular Foundation Models Are Effectively Shallow Analyzes tabular foundation models and shows most transformer layers can be replaced with affine translators without sacrificing performance. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies model architectural depth rather than benchmark contamination or evaluation methods.
Forgetting is Not Always Bad: A Neuro-Inspired Memory Repair Mechanism for Poisoned LLM Agents Introduces DREAM, an agent-intrinsic defense that triggers functional forgetting of poisoned memory entries through implicit reactivation and topological diagnosis. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not focus on LLM benchmark validation, saturation, or evaluation metrics.
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study security attacks, prompt injection, or safety guardrails.
Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis Presents Agent4Spec, a multi-agent framework that automatically validates OpenAPI syntax errors and identifies functional API gaps through market analysis. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an application system and dataset rather than studying LLM benchmark reliability.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Focuses on general reasoning distillation, not code generation or software engineering.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Does not focus on multi-step tool-use agents or multi-agent planning systems.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Does not examine vision-language or multimodal cross-domain models.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not focus on benchmark design, contamination, or LLM evaluation frameworks.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation or grounding with external corpora.
BoundaryEvo: Data-Model Co-Evolution at the Capability Boundary for Long-Chain Reasoning Distillation Proposes BoundaryEvo, dynamically adapting training data around a student model's evolving capability boundary during long-chain reasoning distillation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate safety, robustness, jailbreaks, or red-teaming deployments.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Uses standard benchmarks to evaluate retrieval rather than studying LLM evaluation methodology.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Mentions agents in passing; main focus is index-side retrieval enhancement for documents.
PRIME: Pre-Retrieval Index Modeling Enhancement for Structured Document Retrieval-Real Proposes PRIME, an index-side optimization framework using semantic restructuring and deterministic metadata synthesis to improve document retrieval. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not investigate safety guardrails, red-teaming, jailbreaks, or alignment failures.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	LLMs for code via arxiv · 12 Oct, 16:28	Not relevant Does not evaluate software engineering, code generation, or program repair.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Evaluates text summarization and dialogue without studying multimodal vision or audio models.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies LLM evaluation panels rather than multi-step tool-using autonomous agents.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model efficiency, distillation, quantization, or compact architectures.
Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans Evaluates a label-free rubric clarification method, finding it increases consensus among LLM judges without improving agreement with human evaluations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Does not address retrieval-augmented generation, chunking, or knowledge-base search.
Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not investigate small language models, quantization, pruning, or efficient inference techniques.
Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Proposes a new agent architecture rather than evaluating benchmarks or LLM-as-a-judge schemes.
Masked Diffusion Language Agents for Tool-Integrated Chemical Reasoning Introduces ChemDiffAgent, applying masked diffusion to plan tool use and reasoning trajectories bidirectionally for chemical problem-solving. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not investigate safety risks, prompt injections, guardrails, or alignment vulnerabilities.
AlphaNIST: Emergent Assembly Skills from Binary Task Rewards Evaluates whether robotic manipulation agents can learn complex multi-step assembly skills purely from sparse binary task rewards. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study LLM safety, guardrails, hallucinations, or deployment red-teaming.
AlphaNIST: Emergent Assembly Skills from Binary Task Rewards Evaluates whether robotic manipulation agents can learn complex multi-step assembly skills purely from sparse binary task rewards. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Examines RL policy learning rather than testing or benchmarking LLM evaluation methods.
AlphaNIST: Emergent Assembly Skills from Binary Task Rewards Evaluates whether robotic manipulation agents can learn complex multi-step assembly skills purely from sparse binary task rewards. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on reinforcement learning for robotic manipulation, not LLM-based autonomous planning agents.
AgentSWE: Can Coding Agents Build the Agent You Actually Want? Introduces AgentSWE, a 25-task benchmark evaluating whether coding agents can build, edit, and optimize other software agents against user requirements. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model compression, distillation, quantization, or efficient on-device serving.
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies solely to source code generation and software environments without multimodal or vision data.
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Focuses on code reproduction environments rather than model compression or inference efficiency.
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Focuses on program exploration and verifiable environments rather than retrieval-augmented generation architectures.
ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents Introduces ProgramGym, a framework providing verifiable sandbox environments and black-box test feedback for training long-horizon program reproduction agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on software engineering capability rather than AI safety guardrails, alignment, or jailbreaks.
RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning Presents RegulaTutor, a personalized tutoring framework that grounds instructional policies in explicit cognitive and self-regulated learning theories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses pedagogical instructional policies, not AI safety risks, guardrails, or jailbreaks.
RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning Presents RegulaTutor, a personalized tutoring framework that grounds instructional policies in explicit cognitive and self-regulated learning theories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a personalized tutoring framework rather than a general LLM evaluation benchmark.
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies in-context learning for classical dynamical system identification, not language model agents.
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on control theory system identification rather than LLM safety, guardrails, or reliability risks.
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Addresses meta-learning for dynamical systems rather than model compression, distillation, or lightweight serving.
In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation Develops a probabilistic transformer meta-learning architecture to perform zero-shot system identification directly from long training sequence contexts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies meta-learning to dynamical systems rather than evaluating LLMs on standardized tasks.
Web$hell: A Unified Interface for Agentic Web Access Proposes Web$hell, a unified interface combining broad information gathering, browser interactions, and evidence management for web-based agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on agent interface efficiency and tool execution rather than deployment safety or guardrails.
Web$hell: A Unified Interface for Agentic Web Access Proposes Web$hell, a unified interface combining broad information gathering, browser interactions, and evidence management for web-based agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces an agent web-interaction interface rather than a standardized evaluation benchmark.
CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation Combines a CNN-Transformer network with a continuous sigmoid label encoding to overcome grid quantization errors in direction-of-arrival estimation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Covers signal processing label encodings, not AI safety, robustness, jailbreaks, or guardrails.
CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation Combines a CNN-Transformer network with a continuous sigmoid label encoding to overcome grid quantization errors in direction-of-arrival estimation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on signal processing DOA accuracy rather than LLM evaluation or benchmark critique.
CNN-Transformer with Sigmoid Label for High-Resolution Sub-Degree DOA Estimation Combines a CNN-Transformer network with a continuous sigmoid label encoding to overcome grid quantization errors in direction-of-arrival estimation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses signal processing direction-of-arrival estimation using CNN-Transformers, not LLM agents.
Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC Evaluates neural embedding and diagnostic strategies to interpret detected anomalies in high-luminosity collider particle physics data. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on high-energy particle physics collision representations, not language model agents.
Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC Evaluates neural embedding and diagnostic strategies to interpret detected anomalies in high-luminosity collider particle physics data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies physics collision anomaly detection rather than evaluating general language models.
Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC Evaluates neural embedding and diagnostic strategies to interpret detected anomalies in high-luminosity collider particle physics data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses particle physics anomaly detection rather than AI deployment safety, guardrails, or alignment.
Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits Proposes Rollout-Time Intervention to convert failed agent trajectories into successful off-policy training signals during reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on improving agent task performance via RL interventions rather than deployment safety or guardrails.
Rollout-Time Intervention: Unlocking Agentic Reinforcement Learning Beyond Policy Limits Proposes Rollout-Time Intervention to convert failed agent trajectories into successful off-policy training signals during reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents an RL training algorithm for agents rather than proposing a benchmark or evaluation methodology.
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers Incorporates marginal-cost rewards into multi-agent reinforcement learning to reduce autonomous vehicle travel times without worsening human driver delays. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Deals with traffic congestion externalities rather than language model deployment safety, red-teaming, or guardrails.
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers Incorporates marginal-cost rewards into multi-agent reinforcement learning to reduce autonomous vehicle travel times without worsening human driver delays. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Studies traffic routing optimization algorithms rather than language model evaluation or benchmarks.
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers Incorporates marginal-cost rewards into multi-agent reinforcement learning to reduce autonomous vehicle travel times without worsening human driver delays. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates traditional multi-agent reinforcement learning for vehicle routing, not language model agents.
Agentic Large Language Models Presents a textbook providing an overview of the concepts, architectures, and interactions of agentic large language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Presents a general survey of agentic models rather than a targeted safety, security, or robustness study.
Agentic Large Language Models Presents a textbook providing an overview of the concepts, architectures, and interactions of agentic large language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Acts as a broad book on agentic LLMs rather than focusing on evaluation methodologies or benchmarks.
SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability Introduces SqueezeBench, a saturation-resilient benchmark for formal mathematical reasoning where new accepted proofs dynamically raise the target bounds. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses benchmark saturation in formal mathematics, not AI safety, alignment, or guardrails.
SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability Introduces SqueezeBench, a saturation-resilient benchmark for formal mathematical reasoning where new accepted proofs dynamically raise the target bounds. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on Lean-verified mathematical evaluation rather than LLM agent planning or tool actions.
SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability Introduces SqueezeBench, a saturation-resilient benchmark for formal mathematical reasoning where new accepted proofs dynamically raise the target bounds. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies mathematical capability benchmarking, not model compression, distillation, or efficient serving.
When MLLMs miss what humans read: diagnosing and learning to read squint text Investigates multimodal LLM failures on squint-readable low-frequency text and provides a diagnostic benchmark alongside training interventions. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates reading stylized text in moderation images, not industrial inspection or manufacturing anomaly detection.
When MLLMs miss what humans read: diagnosing and learning to read squint text Investigates multimodal LLM failures on squint-readable low-frequency text and provides a diagnostic benchmark alongside training interventions. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies visual text reading deficiencies in vision-language models, not agent planning or tool use.
A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance Presents a local agentic framework using smaller language models to automatically synthesize edge-case testing scenarios for autonomous systems. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on scenario generation for autonomous system testing, not computer vision defect inspection.
Branching On-Policy Distillation Introduces BranchOPD, which branches student rollouts from shared prefixes at uncertain decision points to improve on-policy distillation efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a knowledge distillation technique rather than benchmarking methodology or evaluation critique.

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