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
Limitations on Autoregressive Token Generation via Sequential Sampling The paper analyzes theoretical computational limits of sequential sampling circuits in autoregressive language model token generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical circuit complexity study, not empirical LLM evaluation or benchmarking.
Limitations on Autoregressive Token Generation via Sequential Sampling The paper analyzes theoretical computational limits of sequential sampling circuits in autoregressive language model token generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical sampling bounds rather than practical multi-step agents.
EarthAlign: Learning Language-Aligned Representations of Earth System Data The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to Earth climate data, not manufacturing or industrial inspection.
EarthAlign: Learning Language-Aligned Representations of Earth System Data The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a domain data alignment pipeline, not general LLM benchmark evaluation.
EarthAlign: Learning Language-Aligned Representations of Earth System Data The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model compression, quantization, or efficient serving.
EarthAlign: Learning Language-Aligned Representations of Earth System Data The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on geospatial representation learning, not AI safety or deployment guardrails.
EarthAlign: Learning Language-Aligned Representations of Earth System Data The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on Earth science representation learning rather than autonomous agent planning.
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement The paper presents a unified taxonomy and design space organizing recursive self-improvement methods for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant A taxonomy and survey on self-improvement, not an evaluation benchmark study.
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement The paper presents a unified taxonomy and design space organizing recursive self-improvement methods for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on self-improvement design frameworks rather than safety guardrails or alignment.
PEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a reinforcement learning algorithm rather than benchmark or evaluation methodology.
PEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents The paper introduces a reinforcement learning approach for multi-turn agents that identifies recurring decision patterns to improve intermediate step credit assignment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent policy optimization rather than safety guardrails or alignment.
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech The paper evaluates a framework that replaces spoken patient identifiers and alters voice identity while preserving clinical conversational evidence. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech de-identification pipelines rather than autonomous agent planning.
AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech The paper evaluates a framework that replaces spoken patient identifiers and alters voice identity while preserving clinical conversational evidence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates speech redaction rather than language model benchmarks or LLM-as-judge.
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation The paper studies expert width and dimension balance in latent mixture-of-experts architectures to optimize parameter capacity across model layers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies architectural efficiency rather than AI safety or reliability risks.
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation The paper studies expert width and dimension balance in latent mixture-of-experts architectures to optimize parameter capacity across model layers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses model architecture scaling rather than evaluation methodologies or benchmarks.
Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation The paper studies expert width and dimension balance in latent mixture-of-experts architectures to optimize parameter capacity across model layers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates MoE architecture design rather than interactive multi-step agents.
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies video synthesis rather than deployment safety, guardrails, or alignment.
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on generative video diffusion training, not interactive AI agents.
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on video diffusion architecture rather than language model evaluation.
Prospective Forcing: Learning to Look Ahead for Autoregressive Long Video Diffusion The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with video synthesis rather than industrial defect detection or inspection.
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 agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on sign language recognition models, not autonomous LLM agents.
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 safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on gesture recognition rather than AI safety guardrails or reliability.
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	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	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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Studies backdoor vulnerabilities in model merging, not efficiency, pruning, or serving.
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	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not involve software engineering, code generation, 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	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	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not study retrieval-augmented generation or external knowledge retrieval systems.
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	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.
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 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	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	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	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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers user personalization training rather than AI safety or reliability.

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