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
LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Addresses novel view synthesis for graphics/AR rather than industrial inspection or manufacturing perception.
LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on 3D view synthesis rendering rather than LLM agent workflows or planning.
LVS: Local View Synthesis from Relative Camera Pose by Reusing Previous Views Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement. Preprint	Multimodal models via arxiv · 12 Oct, 16:28	Not relevant Deals with 3D Gaussian Splatting and RGB-D rendering rather than multimodal foundation models.
An Investigation of Model Coherence: Narrow Finetunes Contradict Themselves Under Resampling Evaluates model coherence using 175 specific questions to measure internal contradictions and identity conflation under answer resampling. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Studies model coherence and finetuning consistency rather than multi-step agent planning or tool execution.
OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Applies multimodal agents to medical prediction rather than proposing LLM evaluation benchmarks.
OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression Introduces an evidence-grounded multi-agent framework combining multimodal medical data and clinical literature for knee osteoarthritis progression prediction. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Includes human-in-the-loop review but focuses on clinical prediction rather than deployment AI safety.
Q-Shaped Options for Hierarchical Reinforcement Learning Develops an abstraction approach for hierarchical reinforcement learning to balance temporal option boundaries with state-dependent value representation. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on classical reinforcement learning options and abstraction rather than LLM-based agents.
Q-Shaped Options for Hierarchical Reinforcement Learning Develops an abstraction approach for hierarchical reinforcement learning to balance temporal option boundaries with state-dependent value representation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Develops a reinforcement learning algorithm rather than an LLM evaluation methodology.
Q-Shaped Options for Hierarchical Reinforcement Learning Develops an abstraction approach for hierarchical reinforcement learning to balance temporal option boundaries with state-dependent value representation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses HRL policy learning without focusing on AI deployment safety, guardrails, or alignment.
Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization Introduces a budget-adaptive tokenization framework that balances token allocation with input length for raw RF signal understanding. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Addresses multimodal signal tokenization rather than LLM agent planning, tool execution, or workflows.
Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization Introduces a budget-adaptive tokenization framework that balances token allocation with input length for raw RF signal understanding. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety, guardrails, robustness, or deployment alignment risks.
Instruction-Conditioned Electromagnetic Spectrum Understanding via Budget-Adaptive Signal Tokenization Introduces a budget-adaptive tokenization framework that balances token allocation with input length for raw RF signal understanding. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on multimodal signal modeling rather than LLM evaluation methodology or benchmark contamination.
Scalable Hierarchical Graph Generation via Soft Community Structure Presents a three-stage hierarchical generation method for large attributed graphs using recursive soft community decomposition. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on attributed graph generative modeling rather than LLM evaluation or benchmark reliability.
Scalable Hierarchical Graph Generation via Soft Community Structure Presents a three-stage hierarchical generation method for large attributed graphs using recursive soft community decomposition. Preprint	AI agents via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph generation models rather than LLM agents, tool use, or planning systems.
Scalable Hierarchical Graph Generation via Soft Community Structure Presents a three-stage hierarchical generation method for large attributed graphs using recursive soft community decomposition. Preprint	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Studies scalable graph generation algorithms, not language model compression or efficient LLM inference.
Scalable Hierarchical Graph Generation via Soft Community Structure Presents a three-stage hierarchical generation method for large attributed graphs using recursive soft community decomposition. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not address AI safety, jailbreaking, hallucinations, or deployment guardrails.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Introduces a training loop methodology rather than an LLM evaluation benchmark or framework.
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 safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Addresses mathematical reasoning training rather than safety, guardrails, or deployment risks.
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	Small and efficient models via arxiv · 12 Oct, 16:28	Not relevant Does not study model distillation, quantization, pruning, or efficient serving.
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	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	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 agents via arxiv · 12 Oct, 16:28	Not relevant Addresses low-level robot manipulation policy learning rather than LLM-based autonomous agent planning.
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.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Does not introduce general LLM evaluation benchmarks or methodologies.
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	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.
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.
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.
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.
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.
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	AI safety and reliability in deployment via arxiv · 12 Oct, 16:28	Not relevant Does not touch upon AI safety or alignment.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Compares scheduling search algorithms, not language model 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.
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	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Focuses on user-driven image editing, not industrial manufacturing vision.
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	Computer vision for industrial applications via arxiv · 12 Oct, 16:28	Not relevant Applies to poster layout generation, not industrial inspection or defect detection.
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.
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	AI agents via arxiv · 12 Oct, 16:28	Not relevant Presents parameter generation for time series models, not autonomous agents.
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	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.
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.
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	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:28	Not relevant Focuses on graph neural network mini-batching, not LLM evaluation.
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	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	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.
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.

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