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
When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following The paper analyzes how answering via a separate model call improves evidence adherence when reasoning traces conflict with provided context. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Does not evaluate or target software engineering or code generation.
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges. Preprint	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Operates purely over structured data and text DSLs.
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on inductive DSL synthesis, not external retrieval.
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on capability extension rather than AI safety.
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an agent induction method, not an evaluation benchmark.
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering The paper presents an agent that expands its domain-specific language for exact answering without external rewards or judges. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Focuses on operator induction, not model compression or serving.
Temporal Specification Guidance for Text-to-Video Generation The paper introduces an inference-time guidance method that enforces temporal logic constraints during text-to-video generation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on text-to-video generation guidance, not LLM agents.
Temporal Specification Guidance for Text-to-Video Generation The paper introduces an inference-time guidance method that enforces temporal logic constraints during text-to-video generation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Deals with video generation sampling, not deployment safety guardrails.
Temporal Specification Guidance for Text-to-Video Generation The paper introduces an inference-time guidance method that enforces temporal logic constraints during text-to-video generation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a generation guidance method, not an evaluation framework.
Temporal Specification Guidance for Text-to-Video Generation The paper introduces an inference-time guidance method that enforces temporal logic constraints during text-to-video generation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Text-to-video generation, not industrial visual inspection.
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.
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	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on language adaptation, not retrieval-augmented generation.
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.
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	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Text reasoning distillation, not multimodal models.
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	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	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on chain-of-thought distillation, not agent tool workflows.
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.
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.
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	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	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	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant General test-time reasoning control, not code-specific assistants.
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 agents via arxiv · 17 Oct, 06:17	Not relevant Studies test-time search budgeting, not multi-step agent tool use.
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	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.
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.
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.
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	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	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	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.
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.
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.
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.
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.
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	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	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Studies gradient behavior in RL, not model compression.
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	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	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.
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	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	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Robotic manipulation paper, not LLM evaluation or benchmarking.
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.

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