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
Learning to Plan by Looking Back: Hindsight Hierarchies for Training Reasoning Models Proposes a training framework that teaches reasoning models to plan by reverse-engineering solution ideas from hindsight examples. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on self-improvement training for theorem proving models rather than multi-step LLM agents or tool use.
Unifying Policy Learning and State Prediction through Spatial Language Modeling Unifies robotic policy learning and future state prediction using an autoregressive Transformer trained on spatial sequence tokens. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not address general LLM benchmarks or evaluation frameworks.
Unifying Policy Learning and State Prediction through Spatial Language Modeling Unifies robotic policy learning and future state prediction using an autoregressive Transformer trained on spatial sequence tokens. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on robotic manipulation policy learning rather than industrial visual inspection or defect detection.
Unifying Policy Learning and State Prediction through Spatial Language Modeling Unifies robotic policy learning and future state prediction using an autoregressive Transformer trained on spatial sequence tokens. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on policy control and trajectory prediction rather than AI safety or reliability guardrails.
Unifying Policy Learning and State Prediction through Spatial Language Modeling Unifies robotic policy learning and future state prediction using an autoregressive Transformer trained on spatial sequence tokens. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses low-level robot manipulation policy learning rather than LLM-based autonomous agent planning.
Recursive Self-Improvement through Multi-Agent Self-Supervision Presents a multi-agent self-supervision method that uses evolutionary workflow optimization and fine-tuning for recursive model self-improvement. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on AI safety, guardrails, or deployment security.
Recursive Self-Improvement through Multi-Agent Self-Supervision Presents a multi-agent self-supervision method that uses evolutionary workflow optimization and fine-tuning for recursive model self-improvement. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on recursive self-improvement training rather than establishing a general evaluation benchmark.
AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Pertains to onboard data compression rather than AI safety, red-teaming, or alignment.
AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction. Peer-reviewed	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on edge computer vision deployment rather than LLM planning, tool use, or agentic workflows.
AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction. Peer-reviewed	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Applies a standard vision detector rather than a multimodal vision-language foundation model.
AI-Based On-Board Maritime Object Detection for Earth Observation Payload Data Reduction on Versal Embedded Hardware Deploys a quantized YOLOX-S detector on embedded hardware for onboard maritime satellite imagery selection and data reduction. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not introduce general LLM evaluation benchmarks or methodologies.
A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization Introduces AgenticBBO-Bench, a benchmark across multiple domains to evaluate LLM agents on black-box optimization under unified finite budgets. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on optimization performance rather than safety, alignment, guardrails, or hallucination mitigation.
A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization Introduces AgenticBBO-Bench, a benchmark across multiple domains to evaluate LLM agents on black-box optimization under unified finite budgets. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not focus on model compression, quantization, distillation, or efficient inference architectures.
When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees Applies confidence sequences to Bayesian neural networks to dynamically allocate Monte Carlo samples per input with statistical guarantees. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses statistical stopping for BNN sampling latency rather than AI safety or guardrails.
When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees Applies confidence sequences to Bayesian neural networks to dynamically allocate Monte Carlo samples per input with statistical guarantees. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops adaptive stopping criteria for sampling, not an LLM benchmark.
When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees Applies confidence sequences to Bayesian neural networks to dynamically allocate Monte Carlo samples per input with statistical guarantees. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on Monte Carlo sample stopping in Bayesian neural networks, not LLM agents.
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study Reports an equal-budget study finding randomized-restart list scheduling outperforms Monte Carlo Tree Search on DAG workflow scheduling. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies classical MCTS vs randomized restarts on DAG workflows, not LLM agents.
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study Reports an equal-budget study finding randomized-restart list scheduling outperforms Monte Carlo Tree Search on DAG workflow scheduling. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Investigates algorithmic search budgets in DAG scheduling, not model efficiency or distillation.
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study Reports an equal-budget study finding randomized-restart list scheduling outperforms Monte Carlo Tree Search on DAG workflow scheduling. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Compares scheduling search algorithms, not language model evaluation.
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study Reports an equal-budget study finding randomized-restart list scheduling outperforms Monte Carlo Tree Search on DAG workflow scheduling. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not touch upon AI safety or alignment.
VibeEdit: Image Editing with Canvas Instructions Presents VibeEdit, an image editor conditioned on spatial canvas annotations to perform targeted object additions, modifications, or removals. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on user-driven image editing, not industrial manufacturing vision.
VibeEdit: Image Editing with Canvas Instructions Presents VibeEdit, an image editor conditioned on spatial canvas annotations to perform targeted object additions, modifications, or removals. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Constructs a benchmark solely for image editing instruction adherence, not LLM evaluation.
VibeEdit: Image Editing with Canvas Instructions Presents VibeEdit, an image editor conditioned on spatial canvas annotations to perform targeted object additions, modifications, or removals. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents an interactive image editing interface, not autonomous multi-step agents.
VibeEdit: Image Editing with Canvas Instructions Presents VibeEdit, an image editor conditioned on spatial canvas annotations to perform targeted object additions, modifications, or removals. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
From Prompting to Composing: A Spatial Canvas Interface for Poster Generation Introduces Compo and a spatial canvas interface enabling users or agents to guide poster layout and image generation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address safety, guardrails, or alignment.
From Prompting to Composing: A Spatial Canvas Interface for Poster Generation Introduces Compo and a spatial canvas interface enabling users or agents to guide poster layout and image generation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates a specific graphic layout interface rather than LLM benchmarks.
From Prompting to Composing: A Spatial Canvas Interface for Poster Generation Introduces Compo and a spatial canvas interface enabling users or agents to guide poster layout and image generation. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to poster layout generation, not industrial inspection or defect detection.
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching Proposes La-Ribo, a geometry-latent flow matching framework designed for simultaneous RNA nucleotide sequence and 3D structure co-design. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses biological generative modeling, not AI safety or deployment risks.
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching Proposes La-Ribo, a geometry-latent flow matching framework designed for simultaneous RNA nucleotide sequence and 3D structure co-design. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Generative model for RNA design, not LLM agents.
La-Ribo: RNA Co-Design via Geometry-Latent Flow Matching Proposes La-Ribo, a geometry-latent flow matching framework designed for simultaneous RNA nucleotide sequence and 3D structure co-design. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Evaluates biological design capabilities, not LLM benchmarks or evaluation.
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting Presents AdaCast, a parameter generation method producing input-specific low-rank updates for foundation models in time-series forecasting. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Generates low-rank updates dynamically for forecasting accuracy, not model compression or serving efficiency.
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting Presents AdaCast, a parameter generation method producing input-specific low-rank updates for foundation models in time-series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on adaptive time series forecasting rather than LLM evaluation.
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting Presents AdaCast, a parameter generation method producing input-specific low-rank updates for foundation models in time-series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Focuses on forecasting adaptation, not deployment safety or guardrails.
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting Presents AdaCast, a parameter generation method producing input-specific low-rank updates for foundation models in time-series forecasting. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents parameter generation for time series models, not autonomous agents.
Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs Introduces Cluster-TNN, a mini-batch graph partitioning framework that enables scalable topological deep learning on large graphs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph neural network mini-batching, not LLM evaluation.
Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs Introduces Cluster-TNN, a mini-batch graph partitioning framework that enables scalable topological deep learning on large graphs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not study model safety, alignment, or guardrails.
Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs Introduces Cluster-TNN, a mini-batch graph partitioning framework that enables scalable topological deep learning on large graphs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses graph lifting scalability in topological neural networks, not agents.
Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods Benchmarks classical planning against learning-based methods in real-time motion planning under deterministic and stochastic rotating hazard fields. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Benchmarks motion planning algorithms, not language model capabilities.
Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods Benchmarks classical planning against learning-based methods in real-time motion planning under deterministic and stochastic rotating hazard fields. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on planar kinematic motion planning, not computer vision or inspection.
Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods Benchmarks classical planning against learning-based methods in real-time motion planning under deterministic and stochastic rotating hazard fields. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Compares classical and RL motion planning in dynamic hazards, not LLM agents.
Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods Benchmarks classical planning against learning-based methods in real-time motion planning under deterministic and stochastic rotating hazard fields. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses robot navigation planning rather than AI safety or LLM deployment risks.
How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders Audits audio encoders by reconstructing music using a latent diffusion model to quantify signal properties preserved across training objectives. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Investigates audio encoder feature retention, not LLM agents.
How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders Audits audio encoders by reconstructing music using a latent diffusion model to quantify signal properties preserved across training objectives. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Audits audio representation retention via latent diffusion, not LLM evaluation.
How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders Audits audio encoders by reconstructing music using a latent diffusion model to quantify signal properties preserved across training objectives. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Investigates representation fidelity in audio encoders, not deployment safety or guardrails.
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings Proposes Syn-Omni, a framework combining modality-expert LoRA paths and progressive routing to balance shared and modality-specific omnimodal embeddings. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not examine AI safety, alignment, or deployment reliability.
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings Proposes Syn-Omni, a framework combining modality-expert LoRA paths and progressive routing to balance shared and modality-specific omnimodal embeddings. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on general multimodal embeddings across vision/audio, not industrial vision tasks.
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings Proposes Syn-Omni, a framework combining modality-expert LoRA paths and progressive routing to balance shared and modality-specific omnimodal embeddings. Preprint	Retrieval-augmented generation (RAG) via arxiv · 12 Oct, 16:28	Not relevant Studies multimodal embeddings rather than retrieval-augmented generation pipelines.
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings Proposes Syn-Omni, a framework combining modality-expert LoRA paths and progressive routing to balance shared and modality-specific omnimodal embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Presents a model architecture evaluated across tasks, not an LLM benchmark study.
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings Proposes Syn-Omni, a framework combining modality-expert LoRA paths and progressive routing to balance shared and modality-specific omnimodal embeddings. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on omnimodal representation learning, not autonomous LLM agents.
DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception Introduces Dynamic Vector Decoding to efficiently map 2D and 3D perception coordinates into compact discrete tokens for multimodal LLMs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on perceptual decoding architecture, not language model evaluation.
DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception Introduces Dynamic Vector Decoding to efficiently map 2D and 3D perception coordinates into compact discrete tokens for multimodal LLMs. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Evaluates general 2D/3D perception benchmarks, not industrial inspection or manufacturing.
DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception Introduces Dynamic Vector Decoding to efficiently map 2D and 3D perception coordinates into compact discrete tokens for multimodal LLMs. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Proposes a perception tokenization scheme, not multi-step language model agents.
DVD: Dynamic Vector Decoding for Efficient MLLM-based Perception Introduces Dynamic Vector Decoding to efficiently map 2D and 3D perception coordinates into compact discrete tokens for multimodal LLMs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address safety, guardrails, or model alignment.
Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants Proposes ARGUS, an agentic framework separating deterministic genomic models from LLM reasoning to interpret noncoding single nucleotide variants reliably. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on genomic interpretation framework rather than general LLM evaluation benchmarks.
Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants Proposes ARGUS, an agentic framework separating deterministic genomic models from LLM reasoning to interpret noncoding single nucleotide variants reliably. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not address model compression, distillation, or efficient inference.
Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants Proposes ARGUS, an agentic framework separating deterministic genomic models from LLM reasoning to interpret noncoding single nucleotide variants reliably. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Applies agents to genomic analysis, not AI deployment safety or alignment.
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery Surveys recent developments in physics-integrated machine learning applications across high-energy nuclear physics experiments and simulations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not focus on LLM safety, reliability guardrails, or alignment.
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery Surveys recent developments in physics-integrated machine learning applications across high-energy nuclear physics experiments and simulations. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Surveys machine learning in nuclear physics, not autonomous agent workflows.
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery Surveys recent developments in physics-integrated machine learning applications across high-energy nuclear physics experiments and simulations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Physics domain review, not an LLM benchmark or evaluation study.
Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction Presents ME-World, an egocentric world model generating synchronized multi-agent first-person video streams with fine-grained physical interactions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces consistency metrics specifically for its own generative world model, not general LLM benchmarking.

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