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
Wolof-Adapted Industry-Ready Open-Weight Language Models The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on low-resource language modeling, not coding assistants.
Wolof-Adapted Industry-Ready Open-Weight Language Models The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Language models for text understanding, not multimodal.
Wolof-Adapted Industry-Ready Open-Weight Language Models The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on low-resource language adaptation, not AI safety.
Wolof-Adapted Industry-Ready Open-Weight Language Models The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Releases adapted models rather than novel evaluation methodologies.
Wolof-Adapted Industry-Ready Open-Weight Language Models The paper presents open-weight language models adapted for the Wolof language using instruction tuning, preference alignment, and model merging. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents language models for Wolof, not multi-step agents.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on distillation data generation, not evaluation methodology.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Evaluates on mathematics benchmarks, not code generation.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on chain-of-thought distillation, not agent tool workflows.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on student accuracy distillation, not AI safety.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Distillation of reasoning traces, not retrieval-augmented generation.
CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation The paper proposes a method to translate reverse reasoning paths into verified forward chains of thought for distillation. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Text reasoning distillation, not multimodal models.
Amortizing Sequential Experimental Design in Reasoning LLMs The paper trains reasoning models to perform sequential experimental design by optimizing information-gain rewards during action selection. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on active learning acquisition, not deployment safety.
Amortizing Sequential Experimental Design in Reasoning LLMs The paper trains reasoning models to perform sequential experimental design by optimizing information-gain rewards during action selection. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies Bayesian experimental design training, not model compression.
Amortizing Sequential Experimental Design in Reasoning LLMs The paper trains reasoning models to perform sequential experimental design by optimizing information-gain rewards during action selection. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on policy training for information gathering, not benchmarking.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on inference control methods rather than benchmark datasets.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Deals with reasoning LLM compute, not multimodal models.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Addresses reasoning expansion, not retrieval-augmented generation.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Optimizes inference resource allocation, not safety or guardrails.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies test-time search budgeting, not multi-step agent tool use.
Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning The paper introduces a confidence-calibrated controller to optimize inference compute and test-time expansion decisions in reasoning models. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General test-time reasoning control, not code-specific assistants.
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General agent skill-policy dynamics, not specific to coding.
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Does not address multimodal models or perception.
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Analyzes agent policy drift, not efficient inference or quantization.
Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on agent skill banks, not RAG.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant General VLM negation probing, not industrial defect inspection.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Studies vision-language attention, not retrieval-augmented generation.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Focuses on visual negation, not software or code.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Probes attention patterns, not model compression or efficiency.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Probes vision-language attention, not multi-step agent actions.
Probing Negation Understanding in Vision-Language Models With Object Attention The paper analyzes how vision-language models process negation by comparing model attention mechanisms with human eye-tracking data. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Analyzes internal attention mechanisms, not deployment safety guardrails.
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Autonomous driving traffic simulation, explicitly outside industrial inspection.
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Traffic simulation study, not AI safety in deployment.
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on autonomous driving simulation, not language-model agents.
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Traffic policy simulation, not LLM benchmarking.
SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations The paper presents a self-play reinforcement learning framework to simulate interactions between vehicles, pedestrians, and cyclists without human demonstrations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Multi-agent traffic simulation, not efficient model serving.
Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories The paper proposes a hybrid reasoning framework combining Answer Set Programming and energy-based models to predict interacting trajectory futures. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on trajectory reasoning rather than LLM evaluation.
Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories The paper proposes a hybrid reasoning framework combining Answer Set Programming and energy-based models to predict interacting trajectory futures. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents an ASP motion planner, not language-model agents.
Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories The paper proposes a hybrid reasoning framework combining Answer Set Programming and energy-based models to predict interacting trajectory futures. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on trajectory prediction, not AI deployment safety.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Theoretical RL study, not retrieval-augmented generation.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies RL gradient dynamics, not agent planning or tools.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses RL optimization dynamics, not deployment safety or robustness.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General RL gradient analysis, not specific to code generation.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Focuses on training reward dynamics, not multimodal models.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Analyzes training dynamics rather than model evaluation methods.
Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL The paper analyzes why reinforcement learning with majority-vote rewards provides effective training gradients despite noisy individual labels. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies gradient behavior in RL, not model compression.
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant General robotic grasping task, not industrial defect or inspection.
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Robotics grasping method, not model quantization or distillation.
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotic affordance grasping, not AI safety.
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotic arm grasping, not language-model agents.
TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping The paper presents a zero-shot vision-language method for generating task-oriented robotic grasps in 6 degrees of freedom. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Robotic manipulation paper, not LLM evaluation or benchmarking.
Replicable Dueling Bandits The paper develops replicable algorithms with sublinear regret bounds for the dueling bandits problem. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Theoretical bandit algorithm analysis, not deployment safety.
Replicable Dueling Bandits The paper develops replicable algorithms with sublinear regret bounds for the dueling bandits problem. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on bandit algorithms rather than LLM benchmarking methods.
Replicable Dueling Bandits The paper develops replicable algorithms with sublinear regret bounds for the dueling bandits problem. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Theoretical study on dueling bandit algorithms, not agents.
Replicable Dueling Bandits The paper develops replicable algorithms with sublinear regret bounds for the dueling bandits problem. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Theoretical online learning work, not model compression or efficiency.
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models The paper evaluates how forecast lead times from AI weather models affect the scheduling of utility crew restoration deployments. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates weather forecast lead times, not language models.
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models The paper evaluates how forecast lead times from AI weather models affect the scheduling of utility crew restoration deployments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Weather forecasting study, not AI safety or reliability deployment.
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models The paper evaluates how forecast lead times from AI weather models affect the scheduling of utility crew restoration deployments. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Applies weather models to utility operations, not LLM agents.
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety The paper proposes a multi-tiered governance model regulating AI system access based on user purpose and model capability. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Discusses governance framework rather than technical LLM benchmark methods.
A Multi-Tiered AI Accessibility Model (MAIAM): Proportionate Access and Capability Limits for Comprehensive Frontier AI Safety The paper proposes a multi-tiered governance model regulating AI system access based on user purpose and model capability. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Proposes an access policy model rather than agent architectures.
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.

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