Every paper checked — nothing hidden

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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
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant User study on voice cloning perception, not an LLM benchmark.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies human perception of cloned voices, not autonomous LLM agents.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on perceptual psychology of voice cloning rather than multimodal foundation models.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Deals with voice cloning psychology, not model efficiency or quantization.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio and virtual characters, not industrial vision inspection.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Voice cloning study, completely unrelated to retrieval-augmented generation.
Known-Voice Cloning and the Limits of Personality Transfer for Character Design This paper investigates whether cloning a familiar voice systematically transfers personality impressions to an embodied virtual character. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on synthetic speech and character perception, not software code.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies medical vocabulary representations, not code generation or repair.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Develops ontology embeddings rather than a retrieval-augmented generation framework.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical ontology aggregation, not model compression or efficiency.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text embeddings and medical ontologies, not multimodal models.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an embedding representation framework, not an evaluation benchmark.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses representation quality for clinical codes, not safety guardrails or jailbreaks.
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation The authors propose a training-free method constructing ontology-grounded semantic identifiers to preserve hierarchy in medical concept embeddings. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses clinical concept representation, not autonomous agent planning or actions.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Investigates text-only transformer attention mechanisms, not multimodal models.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Mechanistic interpretability study of attention heads, not agent planning or tool use.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on interpretability and attention head ablation, not benchmark design.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies attention head mechanisms rather than model compression or efficient inference.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Analyzes attention heads during in-context retrieval rather than RAG architectures.
From Relation-Specific Heads to Shared Interventions: Contextual Entrainment in Language Models This paper investigates attention heads responsible for contextual entrainment and evaluates whether their ablation transfers across different semantic relations. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies factual prompt completion in LLMs, not code generation or review.
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.
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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on decision-making proposal distributions, not model quantization or distillation.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL credit assignment algorithms, not model compression or efficient serving.
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.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Autonomous driving 3D perception paper, not an LLM agent.
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	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	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	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Designed for autonomous driving occupancy prediction, explicitly outside industrial inspection focus.
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 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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses representation learning for spatial tokens, not model compression or efficiency.
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	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 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.
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.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies robotic world-action models rather than lightweight or quantized LLMs.
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.
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.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on physical robot diffusion policies rather than LLM-based agents.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Mathematical optimization analysis rather than LLM benchmarking or evaluation methods.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Abstract theoretical analysis of SGD convergence rates, not model efficiency.

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