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
Model-Based Online Decision Making via Generative Trajectory Planning The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Robotic trajectory planning algorithm, not LLM safety, guardrails, or alignment.
Model-Based Online Decision Making via Generative Trajectory Planning The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on decision-making proposal distributions, not model quantization or distillation.
Model-Based Online Decision Making via Generative Trajectory Planning The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a control planning algorithm, not an evaluation benchmark.
Model-Based Online Decision Making via Generative Trajectory Planning The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic trajectory optimization, not industrial inspection or defect detection.
Model-Based Online Decision Making via Generative Trajectory Planning The authors introduce a decision-making framework that samples candidate action sequences from a diffusion model and refines them using learned dynamics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Applies trajectory planning to continuous control and robotics, not LLM agents.
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant RL optimization technique for agents, not deployment safety or jailbreak prevention.
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an RL training algorithm for agents rather than an evaluation benchmark.
ProAS: A Progress-Aware Advantage Shaping Framework for Multi-Turn Agentic Reinforcement Learning This paper proposes a progress-aware credit assignment framework for multi-turn reinforcement learning in language model agents without using critic models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL credit assignment algorithms, not model compression or efficient serving.
Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents The authors introduce a benchmark to evaluate whether personal agents can distinguish accepted user arrangements from unacknowledged proposals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent grounding and memory, not model quantization or compression.
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Autonomous vehicle sensor processing, not LLM deployment safety or guardrails.
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a perception benchmark for autonomous driving, not LLMs.
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Designed for autonomous driving occupancy prediction, explicitly outside industrial inspection focus.
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Deals with radar and point clouds for driving, not vision-language models.
RadarGSOcc: Doppler-Aware 3D Semantic Occupancy Prediction Using 4D Radar Tensor with Geometric-Semantic Feature Extraction This paper proposes a framework and benchmark for 3D semantic occupancy prediction in autonomous driving using four-dimensional radar tensors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Autonomous driving 3D perception paper, not an LLM agent.
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant General image-token pruning method, not industrial inspection or defect detection.
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on vision token compression rather than planning or tool use.
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on token reduction efficiency rather than safety or guardrails.
Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning The authors present an image-token compression technique using variable-resolution merging for semantic regions and learned relevance pruning inside the model. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Compression technique paper rather than an evaluation benchmark.
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on geometric spatial reasoning rather than safety guardrails or alignment.
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general 3D spatial reasoning, not industrial quality control or manufacturing.
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a model architecture for spatial QA, not an evaluation benchmark.
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on vision-language model spatial reasoning, not agent workflows.
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models This paper introduces a framework that trains lightweight readers to convert latent 3D geometric representations into tokens readable by vision-language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses representation learning for spatial tokens, not model compression or efficiency.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Architecture for robot action models rather than LLM benchmarks or evaluation.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies robotic world-action models rather than lightweight or quantized LLMs.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotics world and action diffusion models rather than LLM agents.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Robotics action modeling, not LLM safety, guardrails, or alignment.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Embodied robotic action framework, not a general vision-language multimodal foundation model.
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models The authors propose a mixture-of-experts diffusion framework that unifies world prediction and action generation models across shared physical dynamics. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic manipulation and world models, not industrial defect inspection.
Unlocking Token Rewards via Training-Free Reward Attribution This paper presents a training-free, gradient-based method to extract token-level reward signals directly from process reward models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Method for computing reward gradients in RL, not safety or guardrails.
Unlocking Token Rewards via Training-Free Reward Attribution This paper presents a training-free, gradient-based method to extract token-level reward signals directly from process reward models. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on token credit assignment in reward models, not agent planning.
Unlocking Token Rewards via Training-Free Reward Attribution This paper presents a training-free, gradient-based method to extract token-level reward signals directly from process reward models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL reward modeling algorithms rather than evaluation benchmarks.
Steertuning Policies from a Few Human Videos The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Robotic policy imitation learning, not deployment safety or LLM guardrails.
Steertuning Policies from a Few Human Videos The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a robot policy tuning method rather than an LLM evaluation benchmark.
Steertuning Policies from a Few Human Videos The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Targets general robot manipulation in homes and labs, not industrial defect detection.
Steertuning Policies from a Few Human Videos The authors introduce a system that enables robots to learn new tasks from five human video demonstrations via fast policy finetuning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical robot diffusion policies rather than LLM-based agents.
A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Abstract theoretical analysis of SGD convergence rates, not model efficiency.
A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical optimization paper on gradient descent, not an agent system.
A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Pure mathematical optimization theory, not safety guardrails or jailbreaks.
A Rate Transfer Method: Convergence Rates in Stochastic Gradient Descent via Stochastic Calculus This paper presents a theoretical rate-transfer method using stochastic calculus to establish finite-time convergence bounds for stochastic gradient algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Mathematical optimization analysis rather than LLM benchmarking or evaluation methods.
SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts The paper introduces a benchmark using fuzzing and transaction replays to evaluate the behavioral correctness of generated smart contracts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on smart contract correctness rather than autonomous planning agents.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes bug-fix editing precision rather than safety guardrails or alignment.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents an RL training algorithm for code repair, not general agent frameworks.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Studies text-based code generation and debugging without any multimodal aspects.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on code debugging via RL, not external document retrieval.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL training objectives for code, not model compression or serving.
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards The paper introduces a debugging training algorithm that rewards localized semantic code edits alongside unit test execution. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a post-training technique for code models rather than an evaluation benchmark.
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated code verification rather than multi-step tool-using agents.
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback The paper introduces a benchmark and model for evaluating program correctness and generating diagnostic counterexamples for competitive programming code. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates competitive programming code verifiers rather than safety risks or jailbreaks.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a model architecture for fast classification decisions, not planning or tool-using agents.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Architecture design for low-latency scoring, not safety guardrails or red-teaming.
Jeva: System One Models from First Principles The paper proposes an alternative architecture for fast decision-making that handles missing choices, option permutation, and candidate scaling natively. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a decision architecture rather than benchmarking or evaluating existing LLMs.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a diagnostic attribution framework, not a general evaluation benchmark.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-based multi-agent execution graphs, not multimodal models.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Addresses general multi-agent workflows rather than software engineering or coding assistants.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies multi-agent fault attribution, not model compression or efficient inference.
BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems The paper introduces an auditing framework that identifies failure steps in multi-agent systems by evaluating how information is utilized across handoffs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent execution handoffs, not document retrieval or knowledge grounding.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Manages task state history rather than model distillation or weight quantization.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Optimizes agent context state rather than safety, guardrails, or alignment.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents The paper presents a runtime system that structures long-horizon agent memory around reusable progress objects rather than raw interaction history. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on runtime context architecture for agents rather than multimodal reasoning.

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