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
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning reuse safety, not model compression or serving.
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Uses code verifiers as a test case, not developing code LLMs.
Many Steps into One: Reasoning Contraction and the Limits of Safe Memory Reuse for LLM Agents The paper analyzes the failure modes of agent memory reuse and evaluates certified memory update strategies when facts change. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal reasoning or vision-language models.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve coding assistants, program repair, or code generation.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines instruction wording retrieval for robots, not LLM agent systems.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, jailbreaking, or red teaming.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic instruction retrieval, not industrial quality control vision.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based lexical retrieval of robot task descriptions.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates lexical ranker wording sensitivity, not general LLM benchmarks.
Reference Wording Shapes Retrieval of Robot Task Instructions The paper investigates how indexed instruction wording affects the performance of lexical rankers for robot task retrieval in manipulation benchmarks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies lexical retrieval sensitivity, not model compression or efficiency.
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a video generation method, not an LLM benchmark.
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops video super-resolution architectures, not LLM planning agents.
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general video super-resolution, not industrial defect detection.
FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching The paper presents an efficient video super-resolution method using hybrid attention and reward-yield distribution matching distillation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on super-resolution quality and speed rather than safety guardrails.
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Benchmarks 3D medical vision models, not language model agents.
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark transferability across medical tasks, not safety risks.
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical vision models rather than industrial defect inspection.
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation The paper benchmarks twenty-three self-supervised 3D medical vision foundation models across segmentation, classification, and regression tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates 3D medical vision encoders, not multimodal vision-language models.
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports The paper introduces an evaluation method that separates question answerability detection from answer span extraction in radiology report question answering. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical report QA, not software engineering or code generation.
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports The paper introduces an evaluation method that separates question answerability detection from answer span extraction in radiology report question answering. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates QA extraction over existing reports without studying retrieval mechanisms.
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports The paper introduces an evaluation method that separates question answerability detection from answer span extraction in radiology report question answering. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies question answering evaluation, not multi-step autonomous LLM agents.
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports The paper introduces an evaluation method that separates question answerability detection from answer span extraction in radiology report question answering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates existing models rather than efficient architectures or compression.
Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports The paper introduces an evaluation method that separates question answerability detection from answer span extraction in radiology report question answering. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text QA over radiology reports, not multimodal models.
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on label shifts in GNNs rather than model compression or efficiency.
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate AI safety, hallucinations, jailbreaks, or guardrails.
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates dynamic graph node classification rather than LLM evaluation benchmarks.
Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification The paper analyzes why dynamic graph neural networks fail on evolving nodes and introduces a change-aware classification framework. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses dynamic graph neural networks, not language model agents.
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control The paper introduces an action-conditioned world model that separates observation representation learning from intervention learning for mobile network energy control. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Develops a world model for mobile network control, not language model agents.
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control The paper introduces an action-conditioned world model that separates observation representation learning from intervention learning for mobile network energy control. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on energy-efficient mobile network control rather than AI safety.
RAN-JEPA: An Action-Conditioned World Model for Energy-efficient Mobile Network Control The paper introduces an action-conditioned world model that separates observation representation learning from intervention learning for mobile network energy control. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates a domain-specific network model, not general LLM benchmarks.
Taming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry The paper applies Hilbert geometry to constrain activation shifts in softmax layers for stable optimizer updates in language model heads. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates mathematical optimization metrics rather than deployment safety or robustness.
Taming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry The paper applies Hilbert geometry to constrain activation shifts in softmax layers for stable optimizer updates in language model heads. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an optimization formulation, not an evaluation method or benchmark.
Taming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry The paper applies Hilbert geometry to constrain activation shifts in softmax layers for stable optimizer updates in language model heads. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimization mathematics for neural network training, not AI agents.
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models The paper re-evaluates the pretraining compute scaling laws between diffusion and autoregressive language models using modern diffusion training designs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on pretraining compute scaling laws, not general LLM evaluation benchmarks.
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models The paper re-evaluates the pretraining compute scaling laws between diffusion and autoregressive language models using modern diffusion training designs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies diffusion language model pretraining compute, not multi-step planning or agents.
Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models The paper re-evaluates the pretraining compute scaling laws between diffusion and autoregressive language models using modern diffusion training designs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address safety, guardrails, hallucinations, or deployment reliability.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates cultural heritage imagery, not industrial defect inspection.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates static vision-language perception, not autonomous agentic systems.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on image classification and knowledge, not RAG systems.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates cultural heritage site recognition, not software engineering.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Introduces an evaluation benchmark, not model compression or efficiency.
Tanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites The paper introduces a dataset and benchmark to evaluate vision-language models on recognizing and answering questions about Philippine cultural heritage sites. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Assesses cultural knowledge coverage rather than safety, jailbreaks, or guardrails.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical outcome prediction, not AI deployment safety guardrails.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a medical survival prediction model, not an LLM benchmark.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on histopathology image analysis, not vision-language multimodal models.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical whole-slide pathology, not autonomous LLM agents.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses multiple-instance learning for pathology, not model compression.
Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images The paper introduces a morphology-aware contrastive learning framework to predict five-year colorectal cancer survival from whole-slide pathology images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Targets medical histology slides, not industrial manufacturing defect inspection.
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks The paper proposes a training-free framework that co-evolves an executable verifier program and harness state to self-improve agents on open-ended tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on runtime self-improvement loops rather than general benchmark design.
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation The paper presents a monocular depth estimation method that uses image editing priors to improve geometric consistency under environmental perturbations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on monocular depth estimation, not LLM-based autonomous agents.
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation The paper presents a monocular depth estimation method that uses image editing priors to improve geometric consistency under environmental perturbations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses computer vision robustness, not AI alignment or safety guardrails.
OpenDepth: Repurposing Image Editing Priors for Robust Open-World Monocular Depth Estimation The paper presents a monocular depth estimation method that uses image editing priors to improve geometric consistency under environmental perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a depth estimation model, not an evaluation benchmark.
MAS-Gate: Unlocking Multi-Agent System Lifecycle via Node-Centric Quantification The paper presents a unified node-centric framework to quantify and manage multi-agent system lifecycles across design, runtime, and post-hoc attribution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on multi-agent execution optimization rather than model benchmarking.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an RL training technique, not an evaluation or benchmarking method.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses RL alignment algorithms, not agent planning or tool-use workflows.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates general domain tasks, not code generation or software engineering.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on optimization dynamics rather than safety guardrails or reliability.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning training, not retrieval-augmented generation.
RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning The paper proposes a rubric-based reinforcement learning method combining selective teacher distillation with an adaptive hand-off to standard optimization. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based reinforcement learning, not multimodal models.
4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling The paper introduces a 4D latent cache to reuse static and dynamic visual representations for efficient conditioning in video generation models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a video generation architecture, not an evaluation benchmark.

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