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
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.
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.
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.
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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical outcome prediction, not AI deployment safety guardrails.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a depth estimation model, not an evaluation benchmark.
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.
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 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	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	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based reinforcement learning, not multimodal models.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on video generation caching, not language-model planning agents.
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	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Targets dynamic video generation, not industrial inspection or defect detection.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses video generation efficiency rather than safety or reliability guardrails.
Executable Prefix Values for Locating Agent Failures The paper evaluates judge labels on agent steps by restarting failed trajectories from intermediate states to identify true failure points. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Examines software engineering agent trajectories, not multimodal or vision tasks.
Executable Prefix Values for Locating Agent Failures The paper evaluates judge labels on agent steps by restarting failed trajectories from intermediate states to identify true failure points. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates agent failure analysis rather than model compression or efficiency.
Executable Prefix Values for Locating Agent Failures The paper evaluates judge labels on agent steps by restarting failed trajectories from intermediate states to identify true failure points. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent failure trajectory analysis, not retrieval-augmented generation.
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision Presents SADDER, an inference-time text diffusion safety framework using directional embedding signals to steer unsafe tokens toward benign distributions. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion model sampling, not tool-using or planning agents.
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision Presents SADDER, an inference-time text diffusion safety framework using directional embedding signals to steer unsafe tokens toward benign distributions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates safety steering rather than model compression or inference efficiency.
The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision Presents SADDER, an inference-time text diffusion safety framework using directional embedding signals to steer unsafe tokens toward benign distributions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a safety steering method, not an evaluation framework.
Beyond Bias Scores: Answerability Confounds in Activation Steering with Gradient-Constrained Direction Design (GCD) Shows that activation steering evaluations can conflate bias reduction with model abstention, introducing a gradient-constrained direction design to isolate preferences. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies activation steering and bias metrics, not autonomous agent planning.
When Should Enterprise Policy Decisions Be Learned, and When Should They Be Reasoned? Evaluates enterprise policy enforcement across structured JSON versus single-token outputs, measuring the impact of explicit reasoning on decision stability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates agent decision interfaces rather than establishing a general LLM benchmark.
RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment Introduces RoTA-Bench to evaluate vision-language models on 3D spatial reasoning through interactive object rotation to match target 2D silhouettes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates spatial reasoning accuracy, not safety, jailbreaks, or guardrails.
RoTA-Bench: Evaluating 3D Spatial Reasoning through Interactive Projection Alignment Introduces RoTA-Bench to evaluate vision-language models on 3D spatial reasoning through interactive object rotation to match target 2D silhouettes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on 3D spatial reasoning evaluation rather than autonomous agent systems.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on step-level agent RL, not RAG architecture or retrieval.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, pruning, or efficient inference serving.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text-based agents, not multimodal or vision-language models.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Aimed at task completion optimization rather than safety or reliability.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an RL training algorithm, not an evaluation methodology or benchmark.
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter Introduces DeltaTTG, a method using predicted remaining token count as a progress signal to allocate advantages in multi-turn reinforcement learning. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to multi-turn agents generally, not specifically code assistants.
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotics action density estimation, not AI deployment safety.
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses general robot motor control benchmarks, not industrial visual inspection.
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a continuous RL method rather than general model evaluation methods.
PrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning Proposes PrT-Flow, an invertible flow matching method that enables exact action probability computation for reinforcement learning in robotics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on continuous robotic reinforcement learning, not LLM-based autonomous agents.
Cowork-Gym: Synthesizing Interactive Work Environments at Scale Introduces Cowork-Gym, a framework synthesizing multi-tool collaborative work environments and user simulations to train enterprise agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent task capability and benchmark performance, not safety guardrails.
Encounter-Driven City-Scale Human Mobility Generation with Large Language Models Introduces EncounterMob, an LLM-based framework that generates human mobility trajectories while preserving both individual routines and multi-person encounter patterns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses mobility generation rather than AI safety, guardrails, or alignment.
Encounter-Driven City-Scale Human Mobility Generation with Large Language Models Introduces EncounterMob, an LLM-based framework that generates human mobility trajectories while preserving both individual routines and multi-person encounter patterns. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies LLMs to mobility generation, not agent planning or tool execution.
Encounter-Driven City-Scale Human Mobility Generation with Large Language Models Introduces EncounterMob, an LLM-based framework that generates human mobility trajectories while preserving both individual routines and multi-person encounter patterns. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on human mobility modeling rather than LLM benchmarking standards.
Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning Proposes Dynamic K-Head, a question-conditioned budget selector that sets continuous latent reasoning steps based on input difficulty. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Optimizes latent reasoning budgets, not tool use or autonomous agents.
Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning Proposes Dynamic K-Head, a question-conditioned budget selector that sets continuous latent reasoning steps based on input difficulty. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reasoning efficiency, not deployment safety or robustness.
Dynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning Proposes Dynamic K-Head, a question-conditioned budget selector that sets continuous latent reasoning steps based on input difficulty. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a dynamic budget selector rather than a new evaluation framework.
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents Introduces LatentRiskBench to evaluate safety risks in enterprise agents where action safety depends on latent workflow context. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not explore model compression, efficiency, quantization, or on-device execution.
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents Introduces LatentRiskBench to evaluate safety risks in enterprise agents where action safety depends on latent workflow context. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses entirely on text-based agent actions, not multimodal or vision-language data.
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents Introduces LatentRiskBench to evaluate safety risks in enterprise agents where action safety depends on latent workflow context. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent workflow state risks rather than retrieval-augmented generation systems.
LatentRiskBench: Measuring State-Dependent Risks in Enterprise Agents Introduces LatentRiskBench to evaluate safety risks in enterprise agents where action safety depends on latent workflow context. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates enterprise agent workflow actions, not code generation or software engineering.
Stable Token-level Scheduling for Serverless LLM Inference Proposes Token Window Pacing, a serverless scheduling framework that regulates request admissions at the token level to reduce inter-token latency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an inference scheduling algorithm, not an evaluation method or benchmark.
Stable Token-level Scheduling for Serverless LLM Inference Proposes Token Window Pacing, a serverless scheduling framework that regulates request admissions at the token level to reduce inter-token latency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on serverless LLM serving latency, not autonomous planning or tool use.
Stable Token-level Scheduling for Serverless LLM Inference Proposes Token Window Pacing, a serverless scheduling framework that regulates request admissions at the token level to reduce inter-token latency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses system latency and throughput rather than safety, jailbreaks, or alignment.
MedEvo: Medical Reasoning Evolution through Knowledge Navigation Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Targets clinical medical imaging, not industrial manufacturing inspection.
MedEvo: Medical Reasoning Evolution through Knowledge Navigation Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on trajectory repair for medical models, not compression or serving.
MedEvo: Medical Reasoning Evolution through Knowledge Navigation Proposes MedEvo, an iterative training framework that repairs multimodal medical reasoning trajectories using minimal hints and taxonomy-guided feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training framework rather than an evaluation benchmark.

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