Search: “framework”

BAT-NO: A Boundary-Condition-Aware Transformer Neural Operator for Crashworthiness Prediction of Vehicle Components

arXiv:2610.03854v1 Announce Type: new Abstract: High-fidelity finite-element simulations provide accurate crashworthiness predictions, but their cost limits iterative design exploration. Deep learning

local-aiarxiv-cs-lg

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Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?

DGX agent

arXiv:2610.03769v1 Announce Type: new Abstract: Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor'

model-releasesarxiv-cs-lg

6 Oct 2026

Tutorials

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

tutorialsarxiv-cs-lg

BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series

arXiv:2610.05409v1 Announce Type: new Abstract: Action-conditioned time-series forecasting requires accounting for how future actions and exogenous forcings influence multiple targets through partiall

Beyond In-Distribution Preservation: Recovering Generalization in Quantized VLAs via Vulnerability-Oriented Tuning

arXiv:2610.05745v1 Announce Type: new Abstract: Post-training quantization has been shown to preserve VLA performance under standard evaluation conditions, but whether it preserves the full-precision

Beyond the Good, the Bad, and the Ugly: Colormap Assessment through Data-Aware Perceptual Metric

arXiv:2610.03720v1 Announce Type: cross Abstract: Continuous colormaps are widely used to visualize scalar fields, and their quality is typically evaluated using measures such as discriminative power

researcharxiv-cs-cv

Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction

arXiv:2610.03822v1 Announce Type: new Abstract: Tokens have become a unified interface for multimodal foundation models, making visual-token communication a natural paradigm for efficient image delive

safetyarxiv-cs-cv

Applications

Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation

arXiv:2610.06809v1 Announce Type: new Abstract: Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free

applicationsarxiv-cs-lg

BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables

arXiv:2610.03759v1 Announce Type: cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases

Tutorials

Broaden Your Views for Self-Supervised Video Learning

arXiv:2103.16559v3 Announce Type: cross Abstract: Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art met

tutorialsarxiv-cs-lg

ChronoWorld: Camera-Controlled Consistent 4D World Generation via Spatiotemporal Cues and Geometric Reflections

arXiv:2610.06687v1 Announce Type: new Abstract: While existing camera-controllable video generation models can produce visually compelling sequences, preserving intrinsic 4D spatiotemporal coherence r

researcharxiv-cs-cv

ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity

arXiv:2610.04020v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingl

safetyarxiv-cs-cv

CleanMDM: Clean Motion Diffusion Model for Multimodal Motion Cleanup

arXiv:2610.05411v1 Announce Type: new Abstract: Motion capture data is rarely directly usable, as they typically exhibit missing segments, jitter, drift and contact artifacts. Traditionally, corrupted

researcharxiv-cs-cv

Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM

arXiv:2510.07971v2 Announce Type: replace Abstract: Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loop

CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video

arXiv:2610.05324v1 Announce Type: cross Abstract: Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while

Code2Games: Enabling Coding Agents for Gaming World Generation

arXiv:2610.05033v1 Announce Type: new Abstract: Generating a high-quality gaming world from a natural-language game intent requires joint reasoning about scene structure, spatial layout, gameplay obje

CodeForge-MA: Execution-Verified Multi-Agent Learning with Language-Conditioned LoRA for Multilingual Code Generation

arXiv:2610.05481v1 Announce Type: cross Abstract: Large language models for code generation often fail on execution, multilingual coverage, and contamination control, especially under frozen backbone

Applications

Communication-Efficient Relative Pose Estimation with Vision Foundation Models for Ephemeral Collaborative Perception

arXiv:2607.14539v2 Announce Type: replace Abstract: Relative pose estimation is a fundamental capability for collaborative perception and coordination in multi-robot systems. However, robots encounter

applicationsarxiv-cs-ro

Compact set-valued deep ensembling in multi-class classification

arXiv:2610.05332v1 Announce Type: new Abstract: This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and

Component-Level Evaluation of Adaptive PINN Training for CFD-Oriented Crystal Growth Simulation

arXiv:2610.05127v1 Announce Type: new Abstract: Physics-informed neural network (PINN) training minimizes a weighted combination of partial differential equation (PDE), boundary-condition, and initial

Conditional Trajectory Peaks: Single-Pass Multimodal Policies over Action Chunks

arXiv:2610.06104v1 Announce Type: new Abstract: Multimodal imitation learning requires diverse executable futures under the same observation and consistent behavior across replanning cycles. We presen

safetyarxiv-cs-ro

ConEx: Human-Interpretable Saliency Maps via Concept-Aware Attribution

arXiv:2610.04605v1 Announce Type: new Abstract: Many visual explanation methods in computer vision highlight pixel importance but struggle to link these low-level cues to semantically meaningful conce

safetyarxiv-cs-cv

Conformal Prediction with Paraphrase-Aware Scoring for LLM Uncertainty Quantification

arXiv:2610.04239v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) for large language models (LLMs) aims to provide reliable measures of predictive confidence, yet current methods are o

Contrastive Time Series Forecasting with Anomalies

arXiv:2512.11526v2 Announce Type: replace Abstract: Time series forecasting predicts future values from past data. In real-world settings, some anomalous events have lasting effects and influence the

Controllable and Photorealistic Pedestrian Risky Motion Generation for End-to-End Driving Safety Evaluation

model-releasesarxiv-cs-ro

arXiv:2610.06171v1 Announce Type: new Abstract: Evaluating end-to-end autonomous driving under rare, safety-critical vehicle-pedestrian interactions requires photorealistic, sensor-level scenarios. Ho

Controllable Road Marking Generation

arXiv:2610.05771v1 Announce Type: new Abstract: Lane and road markings provide critical guidance for vehicle navigation and multi-agent coordination, yet authoring them at scale remains a manual workf

Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery

arXiv:2610.04559v1 Announce Type: new Abstract: External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We int

Cross-Modal Contrastive Learning for the Retrieval of Immunotherapy-Associated Molecular Signatures from Histopathology

arXiv:2610.05157v1 Announce Type: new Abstract: Gastric Adenocarcinoma is a leading cause of cancer mortality. Although 'Inflamed/Non-Inflamed' subtypes have been proposed to predict immunotherapy res

CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery

arXiv:2610.04851v1 Announce Type: new Abstract: Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a froz

Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging

arXiv:2610.06726v1 Announce Type: new Abstract: Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse pro

local-aiarxiv-cs-lg

Deep Prior Learning for Embodied Perception

arXiv:2610.05531v1 Announce Type: new Abstract: Embodied systems need geometric perception that exploits available observations beyond images alone. Recent feed-forward 3D models incorporate geometric

DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection

arXiv:2511.04086v2 Announce Type: replace Abstract: With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGA

Tutorials

DexForge: High-Fidelity Physics-Informed Dexterous Retargeting

arXiv:2610.06331v1 Announce Type: new Abstract: Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reprodu

tutorialsarxiv-cs-ro

Agents

Difference Feature Map Distillation: Transferring Inter-Sample Relational Knowledge Towards Efficient Transformer-Based Tracking

arXiv:2610.05707v1 Announce Type: new Abstract: In autonomous driving perception, visual object tracking systems must satisfy stringent latency and power constraints while remaining robust in complex

agentsarxiv-cs-cv

Agents

Differentiable Ternary Temporal Logic Semantics with Polynomial Surrogate Networks

arXiv:2610.04662v1 Announce Type: cross Abstract: Temporal logic provides designers a formal reasoning tool for specifying complex spatio-temporal behaviors and tasks for autonomous systems. Many tech

agentsarxiv-cs-ro

dIon: Fragmentation-Based Invariance for Self-Supervised Learning of Tandem Mass Spectra

arXiv:2610.06282v1 Announce Type: new Abstract: We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo seque

Discovered, Not Designed: Population Evolution for Collaborative and Compute-Intensive Model Discovery

arXiv:2610.05950v1 Announce Type: new Abstract: LLM-driven evolution enables iterative model development, but two practical goals remain underexplored: finding model designs that transfer across relat

local-aiarxiv-cs-lg

Dissecting Quantization Error: A Concentration-Alignment Perspective

arXiv:2603.04359v2 Announce Type: replace Abstract: Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-

DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting

arXiv:2610.04819v1 Announce Type: new Abstract: One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on

local-aiarxiv-cs-cv

Agents

DUET: Co-Evolving Solver and Grader Agents

arXiv:2610.04087v1 Announce Type: new Abstract: Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deploy

agentsarxiv-cs-lg

DV-Lens: Revealing the Functional Organization of Language Model Parameters

arXiv:2610.04489v1 Announce Type: cross Abstract: Understanding parameter functions helps elucidate the internal mechanisms of large language models (LLMs). However, how to connect parameters from dif

Dynamic Minimax Regret Optimization for Robust LLM Post-Training

arXiv:2610.06329v1 Announce Type: new Abstract: Modern LLM training increasingly relies on heterogeneous data sources spanning different domains, tasks, preference distributions, and difficulty levels

EchoDino: A pediatric foundation model for transferable echocardiographic analysis across the lifespan

arXiv:2610.05603v1 Announce Type: new Abstract: Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, localiz

local-aiarxiv-cs-cv

EEGDM: Label-Efficient EEG Representation Learning with Generative Diffusion Model

arXiv:2508.14086v4 Announce Type: replace Abstract: Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learn

Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts

arXiv:2610.06147v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adapta

EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning

arXiv:2511.19935v3 Announce Type: replace Abstract: Deploying domain-specialized large language models on resource-constrained hardware motivates reducing both adaptation cost and the number of retain

Encoded but Not in Control: Revealing the Grounding Gap in Vision-Language Robot Policies

arXiv:2610.06235v1 Announce Type: cross Abstract: Instruction following is central to language-conditioned robot policies: language should determine what to do when the same scene permits multiple val

End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction

arXiv:2610.05900v1 Announce Type: new Abstract: Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic asses

local-aiarxiv-cs-cv