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
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on an interactive simulation toolkit, not autonomous multi-step LLM agents.
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Robotic physics simulation toolkit, not industrial visual quality control.
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses simulation contact dynamics, not AI safety or red-teaming.
RoboSimStudio: An Interactive Toolkit for Rapid Real2Sim of Robot Manipulation Tasks The paper presents an interactive simulation toolkit that enables humans and code agents to iteratively calibrate robot manipulation physics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents simulation software, not LLM evaluation methods or benchmarks.
Fast Rates for Offline Q-Learning with KL Regularization The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Mathematical proof of Q-learning bounds, not LLM evaluation or benchmarks.
Fast Rates for Offline Q-Learning with KL Regularization The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical offline RL sample complexity analysis, not practical LLM agents.
Fast Rates for Offline Q-Learning with KL Regularization The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Theoretical reinforcement learning paper, not deployment safety or guardrails.
Fast Rates for Offline Q-Learning with KL Regularization The paper analyzes sample complexity rates for offline Q-learning algorithms using Kullback-Leibler regularization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning convergence rates, not model efficiency or distillation.
Who Improves the Robot? Toward Physical Recursive Self-Improvement The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Surveys physical recursive self-improvement in robotics, not software LLM agent workflows.
Who Improves the Robot? Toward Physical Recursive Self-Improvement The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Discusses physical self-improvement frameworks, not model quantization or pruning.
Who Improves the Robot? Toward Physical Recursive Self-Improvement The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic self-improvement, not industrial visual defect detection.
Who Improves the Robot? Toward Physical Recursive Self-Improvement The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Conceptual framework survey, not an empirical benchmark for language models.
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models The paper introduces a benchmark to measure vision-language-action model robustness against hardware, optical, and physical robot setup variations. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Benchmarking robotic arm perturbations, not industrial manufacturing defect inspection.
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models The paper introduces a benchmark to measure vision-language-action model robustness against hardware, optical, and physical robot setup variations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates robotic hardware variations, not LLM agent planning or tool execution.
MFG: Multi-turn Feedback Guided Molecular Optimization The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on molecular optimization via RL, not general tool-use agents.
MFG: Multi-turn Feedback Guided Molecular Optimization The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses drug design RL, not model compression or efficient serving.
MFG: Multi-turn Feedback Guided Molecular Optimization The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Applies RL to chemistry tasks rather than general LLM evaluation benchmarks.
MFG: Multi-turn Feedback Guided Molecular Optimization The paper presents a reinforcement learning framework that trains language models to optimize molecules across multiple turns using iterative feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on drug molecule generation, not AI deployment safety or robustness.
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards The paper develops a framework explaining why large rollout batches in reinforcement learning reduce sample efficiency despite maintaining drift thresholds. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning batch scaling dynamics, not model compression or serving.
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards The paper develops a framework explaining why large rollout batches in reinforcement learning reduce sample efficiency despite maintaining drift thresholds. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Investigates optimization theory rather than benchmark design or LLM evaluation.
Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards The paper develops a framework explaining why large rollout batches in reinforcement learning reduce sample efficiency despite maintaining drift thresholds. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies RL optimization dynamics, not autonomous LLM tool-use agents.
Hybrid Reasoning Agents The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses latent reasoning mechanisms, not deployment safety or robustness guardrails.
Hybrid Reasoning Agents The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Explores internal text-latent representations, not vision or multimodal sensory models.
Hybrid Reasoning Agents The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates on existing benchmarks rather than proposing an LLM evaluation framework.
Hybrid Reasoning Agents The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Explores general agent reasoning, not software engineering or program repair.
Hybrid Reasoning Agents The paper proposes an agent architecture that dynamically switches between latent reasoning and text interaction while maintaining external tool access. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on hybrid latent reasoning, not retrieval-augmented grounding.
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards The paper presents a method for generating visual artifact code by combining text prompts with generated image guidance and cross-modal rewards. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on code generation with multimodal guidance, not multi-step autonomous planning agents.
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards The paper presents a method for generating visual artifact code by combining text prompts with generated image guidance and cross-modal rewards. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on cross-modal code generation rather than standard LLM evaluation methodologies.
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards The paper presents a method for generating visual artifact code by combining text prompts with generated image guidance and cross-modal rewards. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses aesthetic visual artifact generation, not AI deployment safety or jailbreaks.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates reasoning efficiency rather than developing an LLM evaluation framework.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on decoding strategies, not retrieval-augmented generation systems.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on inference token efficiency, not safety or guardrails.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Introduces contrastive decoding to shorten reasoning tokens, not multi-step agent planning.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Applies to text-based reasoning chains, not multimodal foundation models.
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning The paper introduces a training-free contrastive decoding method where a small model guides a larger model toward shorter reasoning chains. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Evaluates on competitive programming but main focus is decoding brevity.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Develops synthetic multi-domain task environments, not industrial vision quality control.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Provides general long-horizon agent training environments, not software code tooling.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses agentic environment reinforcement learning, not multimodal foundation model architectures.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent training recipes, not deployment safety or jailbreak defense.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent environment design rather than retrieval-augmented generation.
Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reward shaping and environments, not small model distillation.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies data quality conditioning, not model compression or efficient serving.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates VLA pretraining data weighting rather than LLM multi-step agents.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines demonstration quality weighting, not AI safety or deployment guardrails.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies general robotic manipulation rather than industrial visual inspection or defect detection.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Applies to robotic primitive planning rather than software engineering code.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses general agentic robotics, not industrial visual inspection or defect detection.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic action chunking, not document retrieval or RAG.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies execution efficiency and chunking rather than AI safety guardrails.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an execution framework rather than a general LLM evaluation benchmark.
ChunkAct: Action Chunking for Agentic Robotics The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on execution chunking rather than multimodal foundation model architectures.
Circuit and Electromagnetic Modeling of Bulk Current Injection Test Setups Involving Complex Wiring Harnesses The paper presents circuit and electromagnetic models to simulate bulk current injection testing across complex wiring harnesses. Peer-reviewedOlder paper (2018) Older paper (2018) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Electromagnetic circuit modeling, not AI safety or deployment reliability.
High Frequency Injection Sensorless Control for a Permanent Magnet Synchronous Machine Driven by an FPGA Controlled SiC Inverter The paper implements high-frequency injection sensorless control on an FPGA-driven silicon carbide inverter for permanent magnet synchronous machines. Peer-reviewedOlder paper (2021) Older paper (2021) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Electrical motor control engineering, completely unrelated to AI safety.
Security for Industrial Communication Systems The paper examines security risks and protocol vulnerabilities when deploying commercial IT networking technologies in industrial communication systems. Peer-reviewedOlder paper (2005) Older paper (2005) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Industrial network security overview, not AI safety or LLM deployment.
Stabilizing Ultrafast Light Sources by Squeezed Light Injection The paper demonstrates reducing laser amplitude and phase noise by injecting femtosecond squeezed vacuum into ultrafast light sources. Peer-reviewedOlder paper (2025) Older paper (2025) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Optics and laser physics, not AI safety or LLMs.
Storage reliability evaluation of engine control circuit module The paper evaluates the storage reliability of an engine control circuit module. Peer-reviewedOlder paper (2011) Older paper (2011) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Electronic hardware circuit reliability, not AI safety or deployment.
A zero-failure reliability demonstration approach based on degradation data The paper presents a zero-failure reliability demonstration method using degradation data for systems. Peer-reviewedOlder paper (2012) Older paper (2012) — discussed this week	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Not relevant Hardware reliability engineering, not AI safety or deployment robustness.
A new method to automatically determine parting line in injection mold design \| IET Conference Publication \| IEEE Xplore Summary pending (next check). Could not confirmPreprint	AI safety and reliability in deployment via ieee · 14 Oct, 06:19	Waiting for check
Real Estate Attribute Value Extraction Using Large Language Models This paper presents an annotated real estate dataset and evaluates language models for extracting structured attributes from unstructured text. Peer-reviewed	Multimodal models via ieee · 14 Oct, 06:19	Not relevant Addresses text-based attribute extraction, not multimodal vision-language models.
FedJam: Multimodal Federated Learning Framework for Jamming Detection This paper introduces a multimodal federated learning framework that combines spectrograms and network metrics for on-device wireless jamming detection. Peer-reviewedOlder paper (2026) Older paper (2026) — discussed this week	Multimodal models via ieee · 14 Oct, 06:19	Not relevant Focuses on RF jamming detection, not general multimodal models.

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