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
LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making This paper introduces a multimodal benchmark evaluating AI models on evidence-grounded decision making in chemistry and materials laboratories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking scientific reasoning, not deployment safety or guardrails.
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Covers LiDAR point cloud generation, not language model agents.
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on sensory data synthesis, not deployment safety.
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on synthetic LiDAR from images, not industrial inspection.
LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a generative 3D model, not an evaluation benchmark.
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware This paper presents a decentralized active inference controller for multi-zone building heating and cooling systems on edge hardware. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on building energy optimization, not AI safety guardrails.
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware This paper presents a decentralized active inference controller for multi-zone building heating and cooling systems on edge hardware. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Traditional active-inference control for HVAC, not language model agents.
One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware This paper presents a decentralized active inference controller for multi-zone building heating and cooling systems on edge hardware. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an HVAC control system, not an evaluation benchmark.
StateCraft: Augmenting Video World Models with Abstract-State Reasoning This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression or serving efficiency.
StateCraft: Augmenting Video World Models with Abstract-State Reasoning This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study model safety, robustness, or reliability guardrails.
StateCraft: Augmenting Video World Models with Abstract-State Reasoning This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Concerns generative video interfaces, not industrial visual inspection.
StateCraft: Augmenting Video World Models with Abstract-State Reasoning This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on video world generation, not multi-step agent planning.
StateCraft: Augmenting Video World Models with Abstract-State Reasoning This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an architecture for video synthesis, not benchmarking methodology.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on Theory-of-Mind reasoning in LLMs, not autonomous agents.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Purely text-based reasoning without vision or multimodal components.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Does not address coding assistants or program synthesis.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Uses benchmarks for experiments, does not propose evaluation methods.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers cognitive reasoning in LLMs, not safety or guardrails.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external retrieval or knowledge grounding.
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning This paper trains language models using reinforcement learning to generate explicit perception-to-action rules for theory-of-mind reasoning tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not focused on model compression, distillation, or efficient inference.
ReproBAIT: Can coding agents build their own biological AI tools? This paper introduces a benchmark evaluating whether coding agents can reimplement and train biological AI tools from published scientific papers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not focus on safety guardrails or deployment risks.
ReproBAIT: Can coding agents build their own biological AI tools? This paper introduces a benchmark evaluating whether coding agents can reimplement and train biological AI tools from published scientific papers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Evaluates tool reproduction capabilities, not model compression or efficiency.
VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting This paper presents a compact spatio-temporal forecasting model designed to generalize across shifting spatial domains and varying observation availability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Compact forecasting model, not language model compression or serving.
VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting This paper presents a compact spatio-temporal forecasting model designed to generalize across shifting spatial domains and varying observation availability. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on spatio-temporal forecasting, not autonomous planning agents.
VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting This paper presents a compact spatio-temporal forecasting model designed to generalize across shifting spatial domains and varying observation availability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a forecasting model architecture, not evaluation methodology.
VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting This paper presents a compact spatio-temporal forecasting model designed to generalize across shifting spatial domains and varying observation availability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
One Monitor Does Not Fit All: Formalism-Agnostic Runtime Verification for Agentic AI This paper develops a runtime verification architecture that decouples monitoring formalisms from execution boundaries to safely control AI agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a runtime monitoring architecture, not an LLM benchmark.
ActionOPD: Distilling Decisions, Not Tokens This paper proposes an on-policy distillation method that aligns student agents using semantic actions rather than token-level supervision. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a training distillation method, not an evaluation framework.
ActionOPD: Distilling Decisions, Not Tokens This paper proposes an on-policy distillation method that aligns student agents using semantic actions rather than token-level supervision. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses policy imitation for agents, not safety or guardrails.
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents This paper presents a distillation method where a teacher model minimally edits a student web agent's reasoning traces at failure points. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focus is distillation methodology, not evaluation benchmark design.
Teaching Agents in their Own Style: Blackbox Teacher Distillation for Web Agents This paper presents a distillation method where a teacher model minimally edits a student web agent's reasoning traces at failure points. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with agent distillation efficiency, not safety or guardrails.
LAGO: Language Agents with Reusable Growing Options This paper introduces an architecture that stores and reuses specialized language agents as temporally extended options for complex coding tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on an agent framework evaluated on existing benchmarks, not new evaluation methods.
LAGO: Language Agents with Reusable Growing Options This paper introduces an architecture that stores and reuses specialized language agents as temporally extended options for complex coding tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses agent task performance rather than safety guardrails or red-teaming.
Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon This paper presents a memory-efficient subspace optimization framework using residual correction to stabilize language model training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not examine AI safety, alignment, or deployment guardrails.
Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon This paper presents a memory-efficient subspace optimization framework using residual correction to stabilize language model training. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates optimization algorithms for neural network training, not AI agents.
Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon This paper presents a memory-efficient subspace optimization framework using residual correction to stabilize language model training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops training optimizers, not model evaluation frameworks.
DeltaStreamer: Towards Human-Like Agentic Streaming Video Understanding This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a streaming video method, not an evaluation benchmark.
DeltaStreamer: Towards Human-Like Agentic Streaming Video Understanding This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Studies streaming video comprehension rather than manufacturing defect detection.
DeltaStreamer: Towards Human-Like Agentic Streaming Video Understanding This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on video reasoning capabilities rather than safety risks or guardrails.
Lost in the Fallback: How Reliably Do Typed Decision Models Use Fallback Options? This paper evaluates how reliably typed decision models switch to fallback options and remain consistent under equivalent decision rules. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Studies typed classification models and routing rules, not agent tool execution.
Euler: Towards Human-Centered Multi-Agent Mathematical Discovery This paper introduces a multi-agent mathematical discovery system that organizes proofs, assumptions, and failed attempts into structured research graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Targets research discovery workflows rather than deployment safety or alignment.
Euler: Towards Human-Centered Multi-Agent Mathematical Discovery This paper introduces a multi-agent mathematical discovery system that organizes proofs, assumptions, and failed attempts into structured research graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a multi-agent mathematical framework rather than a general evaluation benchmark.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL training technique, not an LLM evaluation benchmark.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on self-play training, not model quantization, distillation, or lightweight serving.
Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards This paper proposes a self-play reinforcement learning framework that automatically generates tasks and intermediate verifiable rewards for long-horizon agent training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on agent task capability rather than safety or guardrails.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on latent reasoning distillation rather than autonomous agent tool use.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies reasoning efficiency and coverage rather than AI safety or guardrails.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on textual and latent reasoning paths, not multimodal perception.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a distillation method rather than an evaluation benchmark.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Targets mathematical reasoning distillation, not software engineering or code LLMs.
Rationale Distribution Distillation via Flow Matching for Distributional Continuous Latent Reasoning This paper presents a flow matching method to distill diverse continuous latent reasoning distributions from multiple chain-of-thought rationales. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not cover external knowledge retrieval or grounding.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not cover AI safety, guardrails, or alignment.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Addresses continuous mathematical trajectory modeling on populations, not AI agents.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a mathematical inference algorithm, not an evaluation method.
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference The paper presents a continuous trajectory inference method that regularizes population dynamics using neural splines and Wasserstein smoothness. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic optimal transport splines rather than efficient model serving.
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Applies exclusively to text-based reasoning models, not multimodal inputs.
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not involve external document retrieval or grounding.
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an inference aggregation algorithm rather than an evaluation benchmark.
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on test-time aggregation of chain-of-thought samples, not agent tool execution.
Frustratingly Easy Majority Voting Using Length Conditioning The paper proposes weighting majority voting responses using a length-conditioned prior adapted at test time without model fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on reasoning accuracy via voting rather than safety or reliability risks.

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