Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Defining and Generating Compositional Structure The paper formalizes compositional structure in complex systems using algorithmic information theory and compression concepts. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical study on compositionality, not AI safety deployment.
Defining and Generating Compositional Structure The paper formalizes compositional structure in complex systems using algorithmic information theory and compression concepts. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Theoretical mathematical framework, not an LLM benchmark.
Defining and Generating Compositional Structure The paper formalizes compositional structure in complex systems using algorithmic information theory and compression concepts. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on theoretical algorithmic information theory, not LLM agents.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Optimizes prompt/skill artifacts rather than model efficiency or compression.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an optimization method rather than an evaluation methodology.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent skill evolution rather than retrieval-augmented generation.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Does not involve multimodal perception or vision-language models.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on capability improvement rather than safety or reliability.
SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills The paper proposes a non-elitist evolutionary optimization framework that retains and iteratively refines low-reward agent skills using execution feedback. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General skill acquisition framework, not specific to software engineering.
RAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks The paper introduces an action and feedback contract separating agent decisions from deterministic tool execution for constraint-governed engineering tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses engineering task execution rather than deployment safety.
RAC: A Relational Agent Contract for Constraint-Governed Engineering Tasks The paper introduces an action and feedback contract separating agent decisions from deterministic tool execution for constraint-governed engineering tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an agent architecture rather than an evaluation benchmark.
MAI-Gym: An Open All-in-One Platform for GUI Agent Evaluation and RL Training across Mobile and Computer Environments The paper provides an open platform and environment suite for evaluating and training GUI agents with online reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL training and evaluation platform, not safety.
Screwtape: Adversarial Transcript Editing for Monitor Red-Teaming The paper introduces an adversarial editing pipeline that alters agent execution transcripts to red-team action-monitoring safety systems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on red-teaming monitors rather than general benchmark evaluation.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Addresses general multiple-choice reasoning rather than software engineering code.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies inference compute efficiency rather than AI safety or reliability.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates on text-based decision tasks rather than multimodal inputs.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a model architecture rather than an evaluation benchmark.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on looping architecture for classification rather than tool-using agents.
LEV: Thinking Fast and Deep with Looped Decision Models The paper presents a decision model that performs deeper reasoning by looping its layers instead of generating output tokens. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on internal looped processing rather than retrieval-augmented generation.
Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Conducts a behavioral study of context techniques rather than introducing a benchmark.
Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Analyzes prompt compaction in agents rather than small model architectures.
Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on heterogeneous data agents rather than multimodal vision models.
Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies data agent accuracy and context techniques rather than safety guardrails.
Same Context, Different Agents: A Behavioral Study of Semantic Layers, Orchestration and Compaction in Data Agents The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Evaluates general data exploration and query agents rather than software code.
StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning The paper introduces a training framework to teach AI agents when to intervene proactively and safely during multi-step tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an intervention training method rather than an evaluation benchmark.
MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data The paper proposes a self-play framework to train multimodal foundation models using text proxies instead of paired cross-modal datasets. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a multimodal training framework rather than an evaluation benchmark.
MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data The paper proposes a self-play framework to train multimodal foundation models using text proxies instead of paired cross-modal datasets. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on cross-modal representation learning rather than safety guardrails.
MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data The paper proposes a self-play framework to train multimodal foundation models using text proxies instead of paired cross-modal datasets. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal model training rather than autonomous tool-using agents.
MSP: Modality Self-Play for Training Multimodal LLMs Without Paired Cross-Modal Data The paper proposes a self-play framework to train multimodal foundation models using text proxies instead of paired cross-modal datasets. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on multimodal pretraining, not industrial defect inspection.
SOMA: Stack-of-Objects Manipulation using a Quadruped Robot The paper presents a hierarchical reinforcement learning framework for non-prehensile 3D manipulation of multi-object stacks using quadruped robots. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on quadrupedal locomotion control rather than language-model planning agents.
SOMA: Stack-of-Objects Manipulation using a Quadruped Robot The paper presents a hierarchical reinforcement learning framework for non-prehensile 3D manipulation of multi-object stacks using quadruped robots. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with physical robotics control rather than LLM deployment safety.
SOMA: Stack-of-Objects Manipulation using a Quadruped Robot The paper presents a hierarchical reinforcement learning framework for non-prehensile 3D manipulation of multi-object stacks using quadruped robots. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Studies quadruped manipulation rather than industrial computer vision or defect detection.
SOMA: Stack-of-Objects Manipulation using a Quadruped Robot The paper presents a hierarchical reinforcement learning framework for non-prehensile 3D manipulation of multi-object stacks using quadruped robots. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates robotics locomotion policies rather than LLM benchmarks.
How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts The paper trains a small language model to generate and refine GPU kernel optimization scripts using compiler traces. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on compiler script generation rather than multi-step autonomous agents.
How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts The paper trains a small language model to generate and refine GPU kernel optimization scripts using compiler traces. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents a compiler optimization training technique rather than a benchmark.
How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts The paper trains a small language model to generate and refine GPU kernel optimization scripts using compiler traces. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on GPU script optimization rather than AI safety or reliability.
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation? The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on image diffusion training efficiency rather than multimodal reasoning.
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation? The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on dataset reduction for diffusion models, not autonomous agents.
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation? The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Focuses on general diffusion model training, not industrial inspection.
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation? The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies diffusion training efficiency rather than safety or guardrails.
EigDiff: Eigensample Diffusion - or how to significantly reduce training dataset size with marginal performance variation? The paper uses latent eigenvectors from CLIP to select smaller, representative training datasets for diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies training data selection rather than model evaluation benchmarks.
Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation The paper evaluates deep reinforcement learning methods against simple heuristics and random baselines for high-performance computing job scheduling. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with HPC cluster scheduling rather than LLM safety or reliability.
Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation The paper evaluates deep reinforcement learning methods against simple heuristics and random baselines for high-performance computing job scheduling. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates classical RL algorithms rather than LLM benchmarks or evaluation methods.
Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation The paper evaluates deep reinforcement learning methods against simple heuristics and random baselines for high-performance computing job scheduling. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on classic reinforcement learning algorithms for job scheduling, not LLM agents.
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight The paper introduces a method that turns failed reasoning attempts into tested supervision notes to improve reinforcement learning training. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents an RL fine-tuning algorithm rather than an interactive planning agent.
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight The paper introduces a method that turns failed reasoning attempts into tested supervision notes to improve reinforcement learning training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a training optimization method rather than an evaluation benchmark.
Failure Is the Mother of Success: Recovering Supervision with Grounding Hindsight The paper introduces a method that turns failed reasoning attempts into tested supervision notes to improve reinforcement learning training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL reasoning optimization rather than safety or reliability.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies internal representation dynamics rather than safety or guardrails.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on pretraining unembedding dynamics, not software code tasks.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Investigates representation probing during pretraining rather than LLM benchmarks.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on text language model pretraining, not multimodal inputs.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies pretraining representation dynamics rather than small model efficiency techniques.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Analyzes pretraining dynamics rather than multi-step tool-using agents.
Learning to Read Out: Unembedding Dynamics in Language Model Pretraining The paper investigates when language models learn capabilities by tracking unembedding matrix dynamics during pretraining using parameter-trajectory crosscoding. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Studies internal model representations without involving external document retrieval.
SCARCE: Emergent Institutions in Long-Horizon Multi-Agent Coding Systems The paper studies how autonomous coding agents spontaneously negotiate and coordinate access to shared compute resources under deadlines. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies multi-agent emergent behavior rather than standard LLM evaluation benchmarks.
Recursive Reward Improvement via Adversarial Meta-Prompt Evolution Proposes an adversarial meta-prompt evolution method to improve model-generated evaluation rubrics for open-ended tasks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Addresses RL training optimization rather than benchmark methodologies.
Recursive Reward Improvement via Adversarial Meta-Prompt Evolution Proposes an adversarial meta-prompt evolution method to improve model-generated evaluation rubrics for open-ended tasks. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on RL training loops and adversarial rubrics rather than interactive agents.
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Applies to systematic biomedical literature reviews, not software engineering.
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an application system rather than an evaluation benchmark.
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews Presents a multi-agent system for paper screening and data extraction in systematic literature reviews following standard guidelines. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient serving.

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