Search: “framework”

A Perception-Manipulation Robotics System for Food Cutting

arXiv:2607.04367v1 Announce Type: new Abstract: In the development of cooking robots, mastering the task of cutting is crucial. A significant challenge lies in the diverse properties of food, which ne

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A Random Matrix Theory Perspective on the Consistency of Diffusion Models

DGX agent

arXiv:2602.02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise

researcharxiv-cs-ai

7 Jul 2026

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

arXiv:2607.02941v1 Announce Type: new Abstract: Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and ass

applicationsarxiv-cs-ai

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

arXiv:2607.03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving

arXiv:2509.08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily fo

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507.04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Poli

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

arXiv:2606.16933v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributiona

ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning

arXiv:2607.04162v1 Announce Type: cross Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures

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ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

arXiv:2607.04426v1 Announce Type: new Abstract: Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, mon

agentsarxiv-cs-ro

ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy

arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make to

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields

arXiv:2507.23033v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (

Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning

model-releasesarxiv-cs-lg

arXiv:2607.03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional ell_1-regularized methods are often brittle under observatio

Agent-driven Long-tail Simulation for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2607.04331v1 Announce Type: cross Abstract: Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on l

Local Ai

Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry

arXiv:2607.03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effective

local-aiarxiv-cs-ai

Agent Step Value: State-Transition Measurement with State-Grounded LLM Evaluators

arXiv:2607.04419v1 Announce Type: new Abstract: Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagno

AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection

arXiv:2603.23115v2 Announce Type: replace Abstract: The realism of AI-generated images (AIGI) poses increasing challenges for reliable forensic detection, where heterogeneous expert detectors may prod

agentsarxiv-cs-cv

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

arXiv:2607.05174v1 Announce Type: new Abstract: Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for

Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

arXiv:2602.24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder t

agentsarxiv-cs-lg

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions

arXiv:2607.03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intel

agentsarxiv-cs-ai

Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents

arXiv:2607.04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligen

agentsarxiv-cs-ai

Local Ai

Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks

arXiv:2607.04290v1 Announce Type: cross Abstract: Large Language Models (LLMs) are proposed as control interfaces for next-generation networks, but their latency, hallucinations, and lack of control g

local-aiarxiv-cs-ai

Agentic Very Long Video Understanding

arXiv:2601.18157v3 Announce Type: replace Abstract: The advent of always-on personal AI assistants, enabled by all-day wearable devices such as smart glasses, demands a new level of contextual underst

agentsarxiv-cs-cv

AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence

arXiv:2607.03516v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generati

Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning

arXiv:2607.04254v1 Announce Type: cross Abstract: Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devic

researcharxiv-cs-lg

Aligning Language Models with Selective Prediction

arXiv:2607.03528v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reli

Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO

arXiv:2602.06422v2 Announce Type: replace Abstract: Deploying GRPO on Flow Matching models has proven effective for text-to-image generation. However, existing paradigms typically propagate an outcome

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

arXiv:2504.18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a g

researcharxiv-cs-lg

Analytical Standard Errors for Exploratory Factor Solutions

arXiv:1811.05336v2 Announce Type: replace-cross Abstract: Inference for factor models is often hampered by the lack of tractable and accurate variance estimates, which can materially distort downstrea

researcharxiv-cs-lg

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

arXiv:2607.03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable con

AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion

arXiv:2408.11553v5 Announce Type: replace Abstract: Fashion image editing aims to modify a person's appearance based on a given instruction. Existing methods require auxiliary tools like segmenters an

applicationsarxiv-cs-cv

AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression

arXiv:2512.13956v4 Announce Type: replace-cross Abstract: Cloud-native systems have made operational work both more powerful and harder to automate: incidents unfold across microservices, logs and met

agentsarxiv-cs-ai

Aperture-aware Dispersion 5-D Light-field Imaging Spectrometer

arXiv:2607.04635v1 Announce Type: new Abstract: Enhancing perceptual dimensions while miniaturizing imaging systems presents significant challenges for high-dimensional visual sensing. Conventionally,

applicationsarxiv-cs-cv

AquaStereo: Enabling Underwater Stereo Matching via Depth-Conditioned Diffusion and Geometry Self-Distillation

arXiv:2607.04303v1 Announce Type: new Abstract: Learning-based stereo matching models struggle in underwater environments due to scarce in-domain data and the difficulty of extracting discriminative c

safetyarxiv-cs-cv

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

arXiv:2601.07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. Howe

ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language

arXiv:2607.05123v1 Announce Type: new Abstract: Recent advances in large language models and programmatic CAD have significantly improved Text-to-CAD generation for individual parts. However, producti

applicationsarxiv-cs-ai

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

arXiv:2512.18176v2 Announce Type: replace Abstract: Accurate segmentation of anatomical structures in medical images is essential for diagnosis and treatment planning. While recent interactive segment

applicationsarxiv-cs-cv

Attention is Just Another Name for Coupling? A Fast-Slow ODE Perspective on Hierarchical Pretraining

arXiv:2606.16730v2 Announce Type: replace-cross Abstract: We re-interpret Transformer pretraining as a fast-slow, singularly perturbed flow along depth, with untied weights as its non-autonomous featu

Local Ai

AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection

arXiv:2603.08387v2 Announce Type: replace Abstract: Micro-expression Action Unit (AU) detection identifies localized AUs from subtle facial muscle activations, providing a foundation for decoding affe

local-aiarxiv-cs-cv

Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment

arXiv:2607.04311v1 Announce Type: new Abstract: Subject-driven and multi-element video generation are central to controllable video synthesis, but existing methods still struggle to preserve identity

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Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation

arXiv:2607.02968v1 Announce Type: new Abstract: Massive Activations (MAs) have been widely observed in Transformer-based models, yet their structure and functional roles in Diffusion Transformers (DiT

Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation

arXiv:2607.03007v1 Announce Type: cross Abstract: Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains oft

BanglaMemeEvidence: A Multimodal Benchmark Dataset for Explanatory Evidence Detection in Bengali Memes

arXiv:2607.03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas. While sub

BAT3R: Bootstrapping Articulated 3D Reconstruction from 2D Image Collections

arXiv:2607.03891v1 Announce Type: new Abstract: 3D reconstruction of articulated objects from a single image is challenging because large training datasets with paired image and 3D supervision are dif

Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

arXiv:2607.05090v1 Announce Type: new Abstract: Lumbar spine degeneration is a major contributor to chronic low back pain and is routinely assessed on MRI using ordinal grading systems, e.g. normal, m

Best-of-Better-N: Generating Pre-Aligned Responses with In-Context Learning

arXiv:2607.03453v1 Announce Type: cross Abstract: Inference-time alignment methods, such as Best-of-N, offer a flexible alternative to training-based alignment by using reward models to select high-qu

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations

arXiv:2603.06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic

Beyond Isolated Objects: Relationship-aware Open Vocabulary Scene Understanding via 3D Scene Graph Analysis

arXiv:2607.05348v1 Announce Type: new Abstract: Open-vocabulary 3D scene understanding aims to segment 3D scenes beyond predefined categories by transferring semantic knowledge from vision-language mo

Beyond Modality Fusion: Deep Ensembles for Multimodal Classification

model-releasesarxiv-cs-cv

arXiv:2607.05019v1 Announce Type: cross Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When moda