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
Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports This paper evaluates language-model-generated disaster situation reports against human-written reports using a domain-specific sixteen-slot information schema. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies evaluation quality, not model compression or inference efficiency.
Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports This paper evaluates language-model-generated disaster situation reports against human-written reports using a domain-specific sixteen-slot information schema. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on humanitarian situation reports, not software code generation.
Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports This paper evaluates language-model-generated disaster situation reports against human-written reports using a domain-specific sixteen-slot information schema. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals entirely with textual reports and statistics, not multimodal inputs.
Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with reward models and policy optimization, not model compression or serving.
Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Investigates reward modeling with tools, not retrieval-augmented generation architectures.
Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies reward modeling for general instructions, not code generation or repair.
Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text evaluation and reward modeling, not multimodal models.
Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges This paper proposes a rubric-based reward model that derives criteria strictly from instructions and evaluates responses pointwise using tool assistance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on reward modeling and judging rather than autonomous agent systems.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Presents general agentic reinforcement learning, not specialized code generation.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses skill assignment in RL rather than model compression or efficient serving.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Operates strictly on textual skills and language interactions, not multimodal models.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a training scaffold algorithm rather than an evaluation benchmark.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning policy guidance, not external document retrieval.
SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection This paper introduces a reinforcement learning scaffold that selectively injects textual skills into agents using low-cost utility estimation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning efficiency rather than deployment safety or jailbreaks.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general video-audio understanding, not industrial manufacturing quality control.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Evaluates video grounding models rather than document retrieval-augmented generation.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Presents an evaluation benchmark rather than techniques for small or efficient models.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on video and audio understanding, not coding or software engineering.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Benchmarking benchmark capability rather than AI alignment, red-teaming, or guardrails.
OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates video grounding and question answering, not agent planning workflows.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated model training workflows rather than retrieval-augmented generation.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with remote sensing imagery pipelines rather than general multimodal language models.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on training workflow automation rather than benchmark methodology.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task automation for satellite imagery rather than AI safety.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on Earth observation satellite data, not factory inspection or manufacturing.
The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or efficient inference serving.
Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic manipulation planning, not industrial visual defect detection.
Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an embodied manipulation framework rather than an evaluation benchmark.
Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic kinematics execution rather than model safety or guardrails.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training framework rather than evaluating benchmark saturation or LLM-as-judge.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concentrates on training autonomous task performance rather than safety guardrails.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent harness training rather than retrieval-augmented generation systems.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Operates on text and shell environments, not multimodal image/video understanding.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general harness tasks rather than evaluating software development coding assistants.
ClawLoop: Self-Improving Agents via Harness-Native Training Loop This paper introduces a training framework that enables harness-based software agents to self-improve without relying on external teacher models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on self-improving post-training loops, not model quantization or distillation.
Correction Training for Diffusion Models This paper introduces a learned correction step to reduce error accumulation and the training-inference gap in iterative diffusion model sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses visual sampling artifacts in diffusion models, not AI deployment safety.
Correction Training for Diffusion Models This paper introduces a learned correction step to reduce error accumulation and the training-inference gap in iterative diffusion model sampling. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on image diffusion training, not autonomous LLM agents.
Correction Training for Diffusion Models This paper introduces a learned correction step to reduce error accumulation and the training-inference gap in iterative diffusion model sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method for diffusion models, not an evaluation benchmark.
Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress This paper updates a long-horizon terminal benchmark to measure sustained agent progress across complex engineering workflows using dense process rewards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Measures task completion capabilities rather than safety risks or guardrails.
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation This paper presents an ecosystem of benchmark datasets and evaluation frameworks to advance machine learning applications in veterinary medicine. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers veterinary datasets, not language model agents or multi-agent planning.
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation This paper presents an ecosystem of benchmark datasets and evaluation frameworks to advance machine learning applications in veterinary medicine. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on assembling domain datasets, not AI safety or deployment robustness.
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical kidney cancer CT scans, not industrial manufacturing.
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates probability calibration on medical datasets, not LLM evaluation benchmarks.
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Fuses medical CT imaging with radiomics, not general multimodal models.
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on statistical calibration for clinical scans, not LLM safety guardrails.
Beyond Discrimination: Evaluating Probability Calibration in Radiomics-Image Fusion for Renal Cell Carcinoma Classification This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers medical cancer classification rather than autonomous LLM agents.
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents medical image classification rather than autonomous agent systems.
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Fuses CT images with tabular radiomics, not general vision-language multimodal models.
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies feature interpretability in medical fusion models, not LLM benchmarks.
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical CT image classification, not manufacturing or industrial inspection.
Beyond Gate Weights: Decision-Level Interpretation of Radiomics-Image Feature Fusion for RCC Classification This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with model interpretability in oncology, not deployment AI safety.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Tests general language and Sudoku, not software development or code generation.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies strictly to discrete text generation, not multimodal inputs.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a generative modeling architecture rather than benchmark evaluation methods.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Introduces a generative model framework without retrieval or external document grounding.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on generation speed and architecture rather than AI safety.
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation This paper introduces a coarse-to-fine text generation method using discrete code hierarchies to accelerate parallel sampling over diffusion language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on core text generation architecture, not autonomous agent systems.
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis This paper presents a synthetic multi-panel chart visual question answering dataset to evaluate how multimodal models interpret complex scientific figures. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Tests multimodal models on chart QA without multi-step agent planning.
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis This paper presents a synthetic multi-panel chart visual question answering dataset to evaluate how multimodal models interpret complex scientific figures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines chart extraction accuracy, not safety guardrails or red teaming.
Do you "see" what I "see"? A Multi-panel Visual Question Answer Dataset for Large Language Model Chart Analysis This paper presents a synthetic multi-panel chart visual question answering dataset to evaluate how multimodal models interpret complex scientific figures. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual chart question answering rather than retrieval-augmented generation.

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