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
Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression The paper introduces a training-free token pruning and merging framework with new robustness metrics for vision transformers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces metrics for token pruning robustness rather than LLM benchmarks.
Scaling Vision Transformers Without Training: Prune-and-Merge Token Compression The paper introduces a training-free token pruning and merging framework with new robustness metrics for vision transformers. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on token compression in vision transformers, not LLM agents.
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics The paper proposes a neural operator and state-space framework for high-fidelity 3D turbulent flow reconstruction from coarse data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on physics simulation rather than LLM benchmarking.
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics The paper proposes a neural operator and state-space framework for high-fidelity 3D turbulent flow reconstruction from coarse data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns physics flow reconstruction rather than AI deployment safety risks.
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics The paper proposes a neural operator and state-space framework for high-fidelity 3D turbulent flow reconstruction from coarse data. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses fluid dynamics turbulence modeling, not language model agents.
High-fidelity Three-Dimensional Flow Field Reconstruction Driven by Turbulent Fluid Dynamics The paper proposes a neural operator and state-space framework for high-fidelity 3D turbulent flow reconstruction from coarse data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate language model efficiency, distillation, or quantization.
Beyond Black Boxes: Visual Grounding for Text Embeddings The paper investigates transforming text embeddings into visual sketches to improve model explainability and evaluate their utility in text retrieval. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on embedding representation via sketches, not AI agents.
Beyond Black Boxes: Visual Grounding for Text Embeddings The paper investigates transforming text embeddings into visual sketches to improve model explainability and evaluate their utility in text retrieval. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not establish benchmarks or evaluation methodologies for LLMs.
Beyond Black Boxes: Visual Grounding for Text Embeddings The paper investigates transforming text embeddings into visual sketches to improve model explainability and evaluate their utility in text retrieval. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates visual embeddings for retrieval rather than AI deployment safety.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on membership inference auditing, not interactive LLM agents.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Evaluates text models, not multimodal vision-language architectures.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a privacy audit technique rather than a general LLM evaluation benchmark.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Audits LoRA fine-tuning rather than addressing efficient inference or quantization.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation, program repair, or coding assistants.
inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs This paper presents an intermediate-representation method to audit whether specific private data was used in LoRA fine-tuning of language models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not concern retrieval-augmented generation or external databases.
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning The authors prove and demonstrate empirically that a two-layer attention Transformer can learn and implement in-context Q-learning algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models on practical benchmarks.
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning The authors prove and demonstrate empirically that a two-layer attention Transformer can learn and implement in-context Q-learning algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Explores theoretical emergence of Q-learning, not safety or guardrails.
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning The authors prove and demonstrate empirically that a two-layer attention Transformer can learn and implement in-context Q-learning algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on reinforcement learning theory rather than efficient inference or distillation.
From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning The authors prove and demonstrate empirically that a two-layer attention Transformer can learn and implement in-context Q-learning algorithms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical study of in-context Q-learning emergence in small Transformers, not LLM agents.
Uncertainty Evaluation: Bridging Machine Learning and Measurement Science The paper examines conceptual parallels between measurement science and machine learning uncertainty evaluation to propose a more unified assessment framework. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Discusses measurement theory for ML uncertainty, not autonomous LLM agents.
Uncertainty Evaluation: Bridging Machine Learning and Measurement Science The paper examines conceptual parallels between measurement science and machine learning uncertainty evaluation to propose a more unified assessment framework. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not concern model compression, distillation, or efficient inference.
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution WorkRover introduces a framework to train long-horizon cowork agents by recursively evolving contexts, tasks, and verifiers based on execution feedback. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses training interactive agents, not deployment safety or security risks.
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution WorkRover introduces a framework to train long-horizon cowork agents by recursively evolving contexts, tasks, and verifiers based on execution feedback. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address small models, pruning, or serving efficiency.
WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution WorkRover introduces a framework to train long-horizon cowork agents by recursively evolving contexts, tasks, and verifiers based on execution feedback. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on environment construction for agent training rather than LLM benchmarking.
AReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement This paper introduces an agent coordination framework for scaling recursive self-improvement research teams without updating model weights. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on recursive self-improvement coordination rather than safety or guardrails.
AReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement This paper introduces an agent coordination framework for scaling recursive self-improvement research teams without updating model weights. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent coordination methods rather than introducing evaluation benchmarks.
ResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch This paper introduces an evaluation framework grounded in published papers to assess continuous multi-stage execution in automated research agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on workflow benchmarking rather than safety, guardrails, or alignment.
ResearchFlow-Eval: Tracing the Execution Gap in End-to-End Multi-Claim Autoresearch This paper introduces an evaluation framework grounded in published papers to assess continuous multi-stage execution in automated research agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
One-Sided Verification Under Limited Expert Review This paper proposes decoupling error proposing from error validation to filter invalid candidates under budget constraints for expert human review. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on filtering candidate scientific solutions, not autonomous multi-step agents.
LifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity This paper introduces a benchmark with expert-written rubrics to evaluate deep research agents on real-world life and productivity tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates agent task capability rather than safety, jailbreaks, or guardrails.
APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation This paper introduces an agentic post-training framework that coordinates multiple agents to evaluate and adaptively train text-to-image models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Focuses on text-to-image generation, not industrial inspection or defect detection.
APT: Feedback-Driven Agentic Post-Training for Text-to-Image Generation This paper introduces an agentic post-training framework that coordinates multiple agents to evaluate and adaptively train text-to-image models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on capability improvement rather than AI safety or guardrails.
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies internal reasoning faithfulness rather than agentic tool use and execution.
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address software engineering, coding assistants, or program repair.
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not involve multimodal models or vision-language tasks.
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not investigate retrieval-augmented generation or external document grounding.
Chain of Thought Monitoring Can Fail Even When CoT Is Necessary This paper shows chain-of-thought monitoring can fail even when reasoning is necessary, proposing a multi-level hierarchy to measure monitorability. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Not focused on model size reduction, quantization, or serving efficiency.
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge This paper presents a post-hoc multicalibration framework to correct systematic biases such as position and model family in LLM evaluations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, pruning, or efficient inference.
A CaLLM Judge is a Better Judge: Mitigating Bias in LLM-as-a-judge This paper presents a post-hoc multicalibration framework to correct systematic biases such as position and model family in LLM evaluations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses evaluation bias mitigation, not multi-step planning or tool use.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses graph neural networks with missing features, not LLM agents.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on GNN feature imputation rather than language model evaluation.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not study model compression, distillation, or efficient inference.
When Missingness Follows the Graph: Beyond Feature Propagation for Missing Features This paper formalizes graph-structured missing features where missingness spreads across edges, testing existing graph propagation methods under this setting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Concerns tabular and graph feature missingness, not deployment safety or guardrails.
Certifying Almost-Sure Reachability in Unknown MDPs This paper investigates theoretical limits and conditions for verifying reachability policies in unknown Markov decision processes using sample access. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on theoretical verification of Markov Decision Processes, not LLM agents.
Certifying Almost-Sure Reachability in Unknown MDPs This paper investigates theoretical limits and conditions for verifying reachability policies in unknown Markov decision processes using sample access. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not evaluate language models or introduce LLM benchmarks.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on diffusion compression, not language model agents or planning.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on data compression rather than AI safety or reliability.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Addresses generative compression, not industrial defect detection or visual quality control.
H-DDCM: Hadamard Codebooks for Fast Generative Image and Video Compression This paper presents a generative compression framework using implicit randomized Hadamard dictionaries to speed up diffusion-based image and video coding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Does not propose benchmarks or evaluations for language models.
Fresh at Decision, Stale at Action: Routing New Information to LLM Decisions Already Made This paper introduces a timestamp theory and persistent ledger to route updated information to pending LLM decisions before action. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a temporal decision ledger rather than an evaluation benchmark.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Does not explore retrieval mechanisms or grounding on databases.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Analyzes internal looped model mechanics, not autonomous tool-use agents.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on mechanistic interpretability analysis rather than safety guardrails.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language models rather than multimodal architectures.
Monitoring latent reasoning in looped language models This paper presents an interpretability protocol to monitor latent reasoning dynamics and stability across multiple looped language model families. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies model internal representations rather than software code tasks.
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs This paper proposes an inference-time intervention method to detect and correct modality misrouting that causes multimodal hallucinations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a hallucination mitigation technique rather than a benchmark.
Mitigating Cross-Modal Hallucinations by Correcting Modality Misrouting in Omnimodal LLMs This paper proposes an inference-time intervention method to detect and correct modality misrouting that causes multimodal hallucinations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates multimodal inference steering rather than multi-step agent planning.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a serving scheduler rather than an evaluation benchmark.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on serving latency optimization rather than deployment safety.
RadixFlow: Filling Execution Bubbles in Prefix-Cached Hybrid LLM Serving This paper introduces a scheduler that decouples prefix matching from state readiness to eliminate execution bubbles in hybrid LLM serving. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses low-level inference serving schedulers, not autonomous agents.

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