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
RASSG: Repair-Aware Semantic Survival Gating for Streaming Tool Execution The paper proposes an execution-gating method to safely launch and cancel tool calls early during live speech recognition before user self-corrections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on execution gating algorithms rather than establishing general LLM benchmarks.
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses sign language gesture recognition, not industrial inspection or manufacturing.
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on gesture recognition rather than AI safety guardrails or reliability.
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on sign language recognition architectures, not LLM benchmark evaluation.
A Hybrid Kinematic GCN and Elastic Transformer for Efficient Indian Sign Language Recognition The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on sign language recognition models, not autonomous LLM agents.
DexCast: Learning Real-World Dexterous Control from Human Videos The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on robotic imitation learning from human video rather than LLM agents.
DexCast: Learning Real-World Dexterous Control from Human Videos The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers robotic imitation learning rather than AI safety or deployment risks.
DexCast: Learning Real-World Dexterous Control from Human Videos The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies robotic hand manipulation, not industrial inspection or defect detection.
DexCast: Learning Real-World Dexterous Control from Human Videos The paper presents a framework to learn dexterous robotic hand control directly from human monocular video demonstrations without teleoperation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not propose or analyze language model benchmarks or evaluation methods.
AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Diffusion Adaptation This paper presents a parameter-efficient video diffusion adaptation method combining point tracking with gradient conflict resolution to preserve visual priors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative video adaptation rather than deployment safety or alignment.
AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Diffusion Adaptation This paper presents a parameter-efficient video diffusion adaptation method combining point tracking with gradient conflict resolution to preserve visual priors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Deals with video diffusion parameter adaptation, not LLM agent workflows.
AcrossVAM2.0: Conflict-Aware Point Supervision for Parameter-Efficient Video Diffusion Adaptation This paper presents a parameter-efficient video diffusion adaptation method combining point tracking with gradient conflict resolution to preserve visual priors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on parameter adaptation for video diffusion, not general LLM benchmarking.
AlignAudio: A Unified Dual-Alignment Framework for Noise-Robust Speech-Conditioned Generation This paper proposes a dual-alignment framework to keep speech-conditioned audio generation robust and consistent across clean and noisy acoustic environments. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on audio generation modeling rather than LLM evaluation benchmarks.
AlignAudio: A Unified Dual-Alignment Framework for Noise-Robust Speech-Conditioned Generation This paper proposes a dual-alignment framework to keep speech-conditioned audio generation robust and consistent across clean and noisy acoustic environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech-conditioned generative audio modeling, not autonomous agents.
AlignAudio: A Unified Dual-Alignment Framework for Noise-Robust Speech-Conditioned Generation This paper proposes a dual-alignment framework to keep speech-conditioned audio generation robust and consistent across clean and noisy acoustic environments. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with acoustic noise robustness in audio synthesis, not AI safety risks.
Joint Discrete and SE(3) Diffusion for Protein Interface Design This paper presents a multimodal diffusion framework that jointly designs protein sequences and physical structures to ensure interface complementarity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies structural bioinformatics rather than AI safety or deployment robustness.
Joint Discrete and SE(3) Diffusion for Protein Interface Design This paper presents a multimodal diffusion framework that jointly designs protein sequences and physical structures to ensure interface complementarity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Dataset introduction is specific to protein binder evaluation, not LLM benchmarking.
Joint Discrete and SE(3) Diffusion for Protein Interface Design This paper presents a multimodal diffusion framework that jointly designs protein sequences and physical structures to ensure interface complementarity. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative diffusion for computational biology, not interactive agents.
DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents This paper introduces a benchmark to evaluate how language agents discover, coordinate, and query across multiple distinct databases to answer questions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on capability benchmarking rather than safety guardrails, jailbreaks, or attacks.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on backdoor attack mechanics rather than general benchmark evaluation methodology.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval systems or external document grounding.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Does not study autonomous multi-step agents or tool use workflows.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve software engineering, code generation, or program repair.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on language model security rather than multimodal vision-language systems.
Towards Merging-conditional Backdoor Unalignment against Large Language Models This paper demonstrates a security risk where dormant backdoors trigger safety failures only after merging models that passed individual safety audits. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies backdoor vulnerabilities in model merging, not efficiency, pruning, or serving.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve code generation, software assistants, or program repair.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses strictly on text-based clinical records and medical reasoning tasks.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not study AI safety, guardrails, jailbreaking, or deployment reliability risks.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark evaluation is purely secondary to developing domain-specific clinical models.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on domain-specific medical fine-tuning rather than autonomous agent planning or tool use.
Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine This paper introduces an aligned language model family trained with structured reinforcement learning for clinical reasoning and traditional Chinese medicine prescribing. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation or external knowledge retrieval systems.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark construction artifacts rather than safety, guardrails, or alignment risks.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not address retrieval-augmented generation or external document indexing.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on general aesthetic taste, not industrial inspection or defect detection.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates VLMs on aesthetic judgment, not multi-step agent planning or tool use.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model distillation, quantization, pruning, or efficient inference.
How to Build a Benchmark That Actually Measures Taste This paper introduces a pairwise evaluation benchmark and protocol designed to measure subjective aesthetic taste while controlling for shortcut artifacts. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve coding assistants, program repair, or software engineering.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Deals with user behavior modeling, not code generation or software engineering.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a training framework rather than an LLM evaluation benchmark.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Addresses user history text logs, not multimodal vision-language models.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers user personalization training rather than AI safety or reliability.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on behavior supervision data, not model compression or efficiency.
Vela: User Behavior as Supervision Sparks LLM Intelligence This paper presents a framework that converts long-term real user behavior records into supervisory tasks to train personalization-focused language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on training user behavior modeling, not document retrieval or grounding.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on test-time visual concept discovery, not LLM agent systems.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a class discovery method rather than LLM benchmarking.
Relational Semantic Grounding for Test-Time Discovery This paper proposes a framework using language model attributes to ground and label novel visual classes discovered dynamically at test time. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers open-world visual classification, not deployment safety or guardrails.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a fuzzy control method, not an LLM benchmark.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers classical fuzzy control with inverse RL, not language agents.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies fuzzy reward estimation, not LLM safety or reliability.
Integrating AIRL-Based Reward Estimation into a Control Method Using Fuzzy Inference and Policy Gradient This paper integrates adversarial inverse reinforcement learning into a fuzzy control framework to estimate reward functions directly from demonstration data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on fuzzy RL control, not model efficiency or distillation.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies hierarchical decision models rather than deployment safety or guardrails.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers reinforcement learning world models for gaming, not LLM agents.
MHWA: Multi-timescale Hierarchical World-Action Model This paper introduces a hierarchical multi-timescale world model to reduce compounding rollout errors and drift in sequential decision-making systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an RL architecture rather than language model evaluation.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers retrieval embedding adaptation rather than safety or reliability.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generating synthetic training data for dense retrieval models.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on training data synthesis rather than model compression or efficient serving.
Self-improving data synthesis can scale embedding models across domains and tasks This paper introduces a self-improving synthetic data generation pipeline to train embedding models across diverse retrieval domains without extensive manual annotation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a data synthesis method rather than an evaluation benchmark.
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on problem solving optimization rather than AI safety or guardrails.
Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems This paper presents a framework that uses history tracking and problem-specific tools to help language agents solve complex combinatorial optimization tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an optimization agent framework rather than an evaluation benchmark.
PTGPO: Prefix Tree Group Policy Optimization This paper proposes a critic-free reinforcement learning algorithm using prefix trees to assign step-level credit for interactive multi-step tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL credit assignment rather than model compression or inference efficiency.

8,461–8,520 of 13,754← Previous Next →