Search: “framework”

CoRe-VLA: Preserving Cross-View Coordination in VLAs under Camera Shifts

arXiv:2609.37150v1 Announce Type: new Abstract: VLAs combine pretrained vision-language representations with action generation to enable language-guided control across diverse tasks, becoming a mainst

safetyarxiv-cs-ro

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Corruption-Robust Sparse Linear Contextual Bandits with Knapsack Constraints

DGX agent

arXiv:2609.37189v1 Announce Type: new Abstract: We study sparse linear contextual bandits with knapsack constraints under joint reward and consumption corruption. Consumption corruption creates a chal

model-releasesarxiv-cs-lg

30 Sept 2026

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation

arXiv:2609.38172v1 Announce Type: cross Abstract: Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots. Howe

Local Ai

CRASM-Gate: Deterministic-First Constraint- and Role-Aware Semantic Mapping with Selective Model Assistance Across Heterogeneous Industrial Standards

arXiv:2609.37458v1 Announce Type: new Abstract: Industrial standards often encode the same engineering concept through incompatible hierarchies, identifiers, roles, and structural constraints, so the

local-aiarxiv-cs-ai

CurvSpec: Adaptive Multi-Curvature Learning for Partial Relevant Video Retrieval

arXiv:2609.36815v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) seeks to retrieve untrim-med videos containing a moment that matches a text query, without temporal annotation

Dagger: Decoupling-based Model Stealing Attack against Graph Neural Networks

arXiv:2609.37972v1 Announce Type: cross Abstract: As Graph Neural Networks (GNNs) are widely deployed as Machine Learning-as-a-Service (MLaaS) APIs, model stealing attacks have emerged as a critical s

DARE to Mitigate Hallucination: Dual-path Auto-Regressive-aware Editing

arXiv:2609.36440v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have recently achieved remarkable progress across multimodal tasks, yet object hallucination remains a persistent c

Decoding Affective Nuances: Enhancing MLLMs via Hierarchical Emotion Reasoning and Contrastive Discriminative Pruning

arXiv:2609.36782v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) have demonstrated exceptional capabilities in objective understanding tasks, their performance in affecti

Demistifying Data and Simulator Assumptions in Supervised Causal Discovery

arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pair

Diagnosing and Improving Probabilistic Reasoning in Large Language Models

arXiv:2609.38005v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit d

Hardware

DIET: Deletion-response Expert Trimming for Video Diffusion Transformers

arXiv:2609.37829v1 Announce Type: new Abstract: Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storag

hardwarearxiv-cs-ai

Tutorials

DiffReID: Discriminative Diffusion Model for Object Re-Identification

arXiv:2609.36894v1 Announce Type: new Abstract: As a fundamental image processing task, object Re-Identification (ReID) aims to retrieve objects across non-overlapping cameras. Recently, with the deve

tutorialsarxiv-cs-cv

Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing

arXiv:2609.36901v1 Announce Type: cross Abstract: Modeling the response of driven many-body quantum systems from input--output data is difficult: the dynamics are nonlinear, history dependent, and exp

Disentangling Spurious Correlations in Vision-Language-Action Models via Predicting Domain-Invariant Latent Lookahead

arXiv:2609.37165v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models remain brittle under visual distribution shifts, often relying on spurious correlations tied to domain-specific fact

safetyarxiv-cs-ro

DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting

arXiv:2609.36940v1 Announce Type: new Abstract: Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are essen

Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses

arXiv:2609.36630v1 Announce Type: new Abstract: Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limit

agentsarxiv-cs-ai

Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation

arXiv:2609.36520v1 Announce Type: cross Abstract: RGB-only end-to-end visual navigation policies remain vulnerable to collisions in real-world dynamic environments, motivating a dedicated safety layer

Distilling What Matters: Confidence-Aware Selective Distillation for Large Language Models

arXiv:2609.36734v1 Announce Type: new Abstract: Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implici

safetyarxiv-cs-cl

Distinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion

arXiv:2609.36741v1 Announce Type: new Abstract: Under irreversible resource depletion, an agent can spend resources to distinguish among latent fault models, or to change the system state so that the

safetyarxiv-cs-ai

Applications

DRHeC: Differentiable Rendering for Hand-Eye Calibration with RGB-Based Gradients

arXiv:2609.36779v1 Announce Type: cross Abstract: Accurate hand-eye calibration is crucial for precision manipulation. Traditional methods rely on markers, with their precision dependent on marker acc

applicationsarxiv-cs-cv

DSPO: Diversity-aware Subjective Policy Optimization for Robust Emotional Reasoning

arXiv:2609.36775v1 Announce Type: new Abstract: Reinforcement Learning has significantly advanced the complex reasoning capabilities of MLLMs. However, prevailing RL algorithms suffer a severe failure

DynamicHOI: Coupled Dynamics for Physics-aware HOI Reconstruction

arXiv:2609.36454v1 Announce Type: new Abstract: We study hand-object interaction (HOI) reconstruction from monocular RGB videos, where partial observations can produce visually plausible yet mechanica

EASE: Behavior-Adaptive Skill Curation for Self-Evolving Agents

arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. How

Applications

Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment

arXiv:2501.18157v2 Announce Type: replace-cross Abstract: Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions fre

applicationsarxiv-cs-cv

EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation

arXiv:2609.38046v1 Announce Type: new Abstract: Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with miss

safetyarxiv-cs-ro

EgoHumanoid-V2: Human-to-Humanoid Transfer of Coordinated Whole-Body Skills for Loco-Manipulation

arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has em

safetyarxiv-cs-ai

Elastic ODYN: Differentiable Optimization for Infeasible Control and Learning in Robotics

model-releasesarxiv-cs-ro

arXiv:2606.16564v2 Announce Type: replace Abstract: Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs)

End-to-End Self-Supervised RGB-T Tracking without Modality Misleading

arXiv:2609.37162v1 Announce Type: new Abstract: RGB-T object tracking leverages the complementary characteristics of visible and thermal infrared modalities to improve robustness under adverse conditi

EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory

arXiv:2609.37923v1 Announce Type: new Abstract: Agents can learn from past executions, but enabling different agents to reuse and build on one another's experience remains challenging. We introduce Ep

agentsarxiv-cs-cv

Eternal Sunshine of the Spotless Mind: Systematically Erasing LLM's Memories

model-releasesarxiv-cs-cl

arXiv:2609.36414v1 Announce Type: new Abstract: We consider persistent LLMs that accumulate memories of their interactions with a user over time. Such LLMs maintain memories using external storage, wh

Evaluating Alignment of Behavioral Dispositions in LLMs

arXiv:2602.11328v2 Announce Type: replace Abstract: As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral disp

safetyarxiv-cs-cl

Evaluating Test-Time Scaling of General LLM Agents

arXiv:2602.18998v2 Announce Type: replace Abstract: LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavior

Event-based Scene Synthesis via Inter-Frame Residual Alignment

arXiv:2512.17323v2 Announce Type: replace Abstract: Event-based scene synthesis reconstructs target RGB frames from sparse image observations and asynchronous event streams, encompassing both video fr

Applications

EVO-WAM: Evolving World Action Models through Video-Action Verification

arXiv:2609.38057v1 Announce Type: new Abstract: Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action mo

applicationsarxiv-cs-cv

EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis

arXiv:2601.15951v2 Announce Type: replace Abstract: Novel view synthesis (NVS) of static and dynamic urban scenes is essential for autonomous driving simulation, yet existing methods often struggle to

agentsarxiv-cs-cv

Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes

arXiv:2609.37056v1 Announce Type: cross Abstract: Evolutionary program search driven by large language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and b

researcharxiv-cs-ai

Executor-aware Candidate Selection via a Feasibility Certificate

arXiv:2609.36597v1 Announce Type: new Abstract: Modular robotic systems often separate motion planning from a downstream executor that enforces state-dependent hard constraints. A candidate that is ge

researcharxiv-cs-ro

Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents

arXiv:2609.37810v1 Announce Type: cross Abstract: Vision-language-action and world-action models have demonstrated impressive capabilities in robotics, yet generalization to unseen tasks remains chall

agentsarxiv-cs-ai

FACT: Fidelity-Aware Construction of Articulated Twins

arXiv:2609.37067v1 Announce Type: cross Abstract: Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Constru

Hardware

FastVR: Efficient Streaming Video Restoration with One-Step Diffusion

arXiv:2609.36757v1 Announce Type: new Abstract: Diffusion-based video restoration recovers realistic details, but its practical deployment is limited by two efficiency bottlenecks: costly VAE encoding

hardwarearxiv-cs-cv

Federated Clustering with Unknown Local and Global Cluster Cardinalities

arXiv:2609.36762v1 Announce Type: new Abstract: Federated clustering methods that do not require the global number of clusters K still assume that each client knows its local number K_g. This assumpti

researcharxiv-cs-lg

FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning

arXiv:2609.37033v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but stat

FigAct: Turning Scientific Figures into Active Canvases for Explanation

arXiv:2609.36190v1 Announce Type: new Abstract: Scientific figures are designed to communicate information visually, yet MLLMs typically explain them by translating their visual content back into text

FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation

arXiv:2609.36670v1 Announce Type: new Abstract: A critical prerequisite of generative recommendation is designing semantic identifiers (SIDs) that are both scalable to large item sets and efficiently

safetyarxiv-cs-ai

FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance

arXiv:2609.36474v1 Announce Type: new Abstract: In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failu

safetyarxiv-cs-cl

Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models

arXiv:2609.36458v1 Announce Type: cross Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model prediction

researcharxiv-cs-cl

FloCA: Towards Faithful and Logically Consistent Flowchart Reasoning

arXiv:2602.14035v2 Announce Type: replace Abstract: Flowchart-oriented dialogue (FOD) systems aim to guide users through multi-turn decision-making or operational procedures by following a domain-spec

agentsarxiv-cs-ai

FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models

model-releasesarxiv-cs-lg

arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the