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An End-to-End Automated Pipeline for Controllable Crack Data Synthesis

arXiv:2609.12431v1 Announce Type: new Abstract: Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing

researcharxiv-cs-cv

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An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation

DGX agent

arXiv:2609.12574v1 Announce Type: new Abstract: Ultra-widefield (UWF) swept-source optical coherence tomography angiography (SS-OCTA) enables large-area retinal vascular imaging, yet vessel segmentati

researcharxiv-cs-cv

14 Sept 2026

AnchorVLN: Geometry-Anchored Vision-Language Grounding Reasoning for Open-Vocabulary Navigation

arXiv:2609.12285v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) in unseen indoor environments is useful in real-world robotics, where an agent must follow natural-language instructi

AnyView: Synthesizing Any Novel View in Dynamic Scenes

arXiv:2601.16982v2 Announce Type: replace Abstract: Modern generative video models excel at producing convincing, high-quality outputs, but struggle to maintain multi-view and spatiotemporal consisten

AquaCubeAI-Powered Monitoring Turbidity on-board {Phi}sat-2

arXiv:2609.12744v1 Announce Type: new Abstract: Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground pr

ARC: Autonomous Robotics Compliance A Three-Layer Governance Architecture for Deployed Autonomous Systems

arXiv:2609.12932v1 Announce Type: new Abstract: Proposed governance framework for autonomous robotic systems, introducing a three-layer compliance architecture (ARC) instantiated through model safety

ArtManip: Category-Level Articulated In-Hand Manipulation

arXiv:2609.12498v1 Announce Type: new Abstract: Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stem

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

arXiv:2609.13083v1 Announce Type: new Abstract: In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered bu

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

model-releasesarxiv-cs-lg

arXiv:2507.01663v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-collocated

Applications

ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting

arXiv:2502.20386v2 Announce Type: replace Abstract: We address the challenge of task-oriented navigation in unstructured and unknown environments, where robots must incrementally build and reason on i

applicationsarxiv-cs-ro

Attention Quantization for Tabular Foundation Models

arXiv:2609.13031v1 Announce Type: new Abstract: With the recent rise and adoption of tabular foundation models, optimizing their inference performance becomes an emerging field for efficiency research

Automated Detection and Structuring of Social Tipping Point Evidence in Climate related Documents: A Modular AI Framework

model-releasesarxiv-cs-cl

arXiv:2609.12254v1 Announce Type: new Abstract: The climate literature has grown faster than review teams can read it. That gap matters most for a concept like the environmental social tipping point,

Agents

Autonomous Precision Milling of Biological Structures via Generic Anatomical Priors and Active Boundary Perception

arXiv:2609.12530v1 Announce Type: new Abstract: Autonomous precision milling of biological structures is challenged by incomplete knowledge of target geometry, local material thickness, and critical i

agentsarxiv-cs-ro

Agents

Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval

arXiv:2609.13073v1 Announce Type: cross Abstract: Recent breakthroughs in LLM-based systems and their abilities in problem solving and coding have allowed progress in the AI for Science paradigm, pote

agentsarxiv-cs-lg

Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring

arXiv:2609.12830v1 Announce Type: new Abstract: Continuous emotion control in text-to-image generation requires a model to improve affective alignment without changing the objects, layout, or scene de

safetyarxiv-cs-cv

Local Ai

Battery-Aware Predictive Trajectory Planning and Control for Multirotors Under Disturbances

arXiv:2609.12188v1 Announce Type: new Abstract: This paper presents a battery-aware predictive trajectory-planning and control framework for multirotors operating under spatially localized disturbance

local-aiarxiv-cs-ro

Before the Tipping Point: Force-Guided Active Perception for Shape-Agnostic Estimation of 3D Centers of Mass

model-releasesarxiv-cs-ro

arXiv:2609.12894v1 Announce Type: new Abstract: Estimating the 3D center of mass of unknown objects is challenging when grasping is infeasible, geometry is irregular, or mass distribution is uneven. W

Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents

model-releasesarxiv-cs-lg

arXiv:2609.12896v1 Announce Type: new Abstract: LLM-based agents rely on heterogeneous interaction capabilities to accomplish complex tasks. Existing approaches often distribute these capabilities acr

Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models

arXiv:2602.00165v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is a practical way to reduce the memory footprint of large language models, but low-bit quantization is sensitive t

Benign Loss Landscapes Can Coexist with Worst-Case Hardness

arXiv:2609.13057v1 Announce Type: new Abstract: Deep neural networks are expressive enough to contain worst-case targets that can be evaluated in polynomial time but cannot be learned in polynomial ti

tutorialsarxiv-cs-lg

Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation

arXiv:2609.12892v1 Announce Type: new Abstract: Accurate biomedical image segmentation requires not only high global overlap but also reliable delineation of clinically meaningful boundaries. In blood

researcharxiv-cs-cv

Beyond Ambiguous Visual Cues: Studying Physiological Disruptions and Cross-Modal Inconsistencies in Deepfake Videos

arXiv:2609.12668v1 Announce Type: new Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks l

tutorialsarxiv-cs-cv

Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation

arXiv:2609.12099v1 Announce Type: new Abstract: Classical classifier-combination work distinguishes score-level fusion from hard decision-level voting. We revisit this distinction where independently

researcharxiv-cs-cv

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

arXiv:2609.12403v1 Announce Type: cross Abstract: Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to repre

researcharxiv-cs-cl

Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?

arXiv:2609.12437v1 Announce Type: new Abstract: Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signa

Bias-Corrected Data Synthesis for Imbalanced Learning

arXiv:2510.26046v3 Announce Type: replace-cross Abstract: Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic

Block-Norm Geometries for Online Mirror Descent with Sparse Losses

arXiv:2602.13177v2 Announce Type: replace-cross Abstract: The performance of online mirror descent depends critically on the geometry induced by its mirror map, yet standard algorithms largely rely on

BodhiPromptShield: Pre-Inference Prompt Mediation for Surface-Form Privacy Propagation in LLM Agent Pipelines

arXiv:2604.05793v2 Announce Type: replace-cross Abstract: In LLM agent pipelines, prompt privacy risk propagates beyond a single model call: raw user content enters retrieval queries, memory writes, t

Breaking the Token Ceiling: Distilling Smaller, Stronger Byte Models

model-releasesarxiv-cs-cl

arXiv:2609.12303v1 Announce Type: new Abstract: Small models are made more capable through distillation from a larger one that shares their tokenization scheme. However, do distilled byte and token mo

Applications

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

arXiv:2609.12641v1 Announce Type: new Abstract: Robot foundation models achieve strong in-distribution performance but often degrade under visual distribution shifts. When learning to generate actions

applicationsarxiv-cs-ro

Hardware

BRIDGE-EEG: Bridging Self-Supervised Pretraining and Efficient Deployment for Cross-Dataset EEG Classification

arXiv:2609.12218v1 Announce Type: cross Abstract: The growing use of electroencephalography (EEG) motivates automated analysis that is accurate, transferable, and deployable on constrained hardware. R

hardwarearxiv-cs-lg

Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images

arXiv:2609.12454v1 Announce Type: new Abstract: Vision-Language Models such as CLIP enable effective few-shot medical anomaly detection (AD) via strong image-text semantic alignment. However, their gl

Calibrated Ambiguity in Multimodal Language Models: Humans reach for cultural references, while models describe the picture

arXiv:2609.12575v1 Announce Type: new Abstract: Ambiguity is often treated as a bug for AI systems to resolve---but in human communication and culture, ambiguity can also be a generative resource. Fro

researcharxiv-cs-cl

Can LLMs in Draft-Verify-Revise Pipelines Resolve Deictic Ambiguity?

model-releasesarxiv-cs-cl

arXiv:2609.12162v1 Announce Type: cross Abstract: Draft-verify-revise is a common LLM orchestration pattern for scaling inference-time compute. One LLM drafts, a second critiques the draft and provide

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases

arXiv:2504.07133v2 Announce Type: replace-cross Abstract: We revisit the problem of estimating k linear regressors with self-selection bias in d dimensions with the maximum selection criterion, as int

Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration

arXiv:2609.12002v1 Announce Type: new Abstract: LLMs are increasingly used as automated judges for model training and evaluation, yet individual judges exhibit systematic biases that undermine reliabi

Agents

CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models

arXiv:2609.13060v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use

agentsarxiv-cs-lg

Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?

arXiv:2606.23189v2 Announce Type: replace-cross Abstract: Computer-use agents (CUAs) now act on a user's behalf across personal applications such as email, calendars, and to-do lists. This cross-appli

safetyarxiv-cs-cl

Certified AI Triage of ICU Alarms

model-releasesarxiv-cs-lg

arXiv:2609.12365v1 Announce Type: new Abstract: In the VTaC benchmark 71% of ventricular-tachycardia alarms are false, but silencing a real one can delay recognition of a dangerous arrhythmia. We refr

Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning

arXiv:2609.12014v1 Announce Type: new Abstract: Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparin

Certifying Concept Unlearning in Text-to-Image Diffusion Models

arXiv:2609.12163v1 Announce Type: new Abstract: Existing evaluations of concept unlearning in text-to-image (T2I) diffusion models primarily rely on attack success rates obtained through automated adv

Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure

arXiv:2609.12221v1 Announce Type: new Abstract: Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating

ChitraMiti: Benchmarking Visual Grounding and Modality Reliance in Bengali Geometric Reasoning

arXiv:2609.12509v1 Announce Type: new Abstract: Evaluation of vision-language models (VLMs) for multimodal mathematical reasoning remains limited for low-resource languages and for geometry problems t

Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding

arXiv:2609.12243v1 Announce Type: new Abstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-tra

researcharxiv-cs-cl

Classical and quantum kernel fusion for two-sample testing

arXiv:2511.20941v2 Announce Type: replace-cross Abstract: Two-sample tests have been extensively employed in various scientific fields and machine learning to discriminate whether two sets of samples

tutorialsarxiv-cs-lg

Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning

arXiv:2609.12584v1 Announce Type: new Abstract: Instruction-tuning datasets for large language models (LLMs) are often large, redundant, and imbalanced, limiting efficient adaptation. Naive large-batc

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterog

tutorialsarxiv-cs-cl

Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies

arXiv:2609.13011v1 Announce Type: new Abstract: Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As