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Continuous-Space Roadmap Generation for Mobile Robot Fleets with Distance Constraints and Geometry-Aware Discretization

arXiv:2511.07175v2 Announce Type: replace Abstract: Efficient routing of mobile robot fleets requires roadmaps with high redundancy, short path lengths, and sufficient node and edge clearance for conf

agentsarxiv-cs-ro

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Agents

Contrastive Reflection for Iterative Prompt Optimization

DGX agent

arXiv:2606.30840v1 Announce Type: new Abstract: LLM agents are becoming central to information retrieval: they issue retrieval queries, synthesize answers, and increasingly serve as judges for IR eval

agentsarxiv-cs-ai

1 Jul 2026

CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization

model-releasesarxiv-cs-cv

arXiv:2606.31219v1 Announce Type: new Abstract: Cellular vehicle-to-everything (C-V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However,

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

arXiv:2602.20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal. In real environments, however, clutter may block all r

applicationsarxiv-cs-ai

Corruption Robust Offline Reinforcement Learning with Human Feedback

arXiv:2402.06734v2 Announce Type: replace-cross Abstract: We study data corruption robustness for reinforcement learning with human feedback (RLHF) in an offline setting. Given an offline dataset of p

safetyarxiv-cs-ai

Local Ai

CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations

arXiv:2606.31033v1 Announce Type: new Abstract: In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallu

local-aiarxiv-cs-cl

Creating Intelligence: A Computational Foundation for AGI

arXiv:2606.31819v1 Announce Type: new Abstract: This work introduces a new computational theory of mind grounded in set theory and hyperdimensional computing. Whereas traditional neural networks rely

Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring

arXiv:2606.30676v1 Announce Type: cross Abstract: Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity.

researcharxiv-cs-lg

Cross-Domain Feature Expansion for Tabular Medical Data via Knowledge Graphs Injection

arXiv:2606.31171v1 Announce Type: new Abstract: Acquiring comprehensive cross-domain biomedical profiles is often costly and time-consuming, resulting in severe data scarcity in medical research. To a

Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian

arXiv:2606.31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross

Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation

arXiv:2606.30647v1 Announce Type: cross Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the availa

Cross-Resolution Distribution Matching for Diffusion Distillation

arXiv:2603.06136v2 Announce Type: replace Abstract: Diffusion distillation is central to accelerating image and video generation, yet existing methods are fundamentally limited by the denoising proces

Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers

arXiv:2606.32020v1 Announce Type: new Abstract: Modern one-step diffusion models achieve impressive quality through distribution-based timestep distillation. Yet, they rely on a critical assumption: T

safetyarxiv-cs-cv

CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM

arXiv:2606.31332v1 Announce Type: new Abstract: Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity.

applicationsarxiv-cs-ai

CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs

arXiv:2606.31309v1 Announce Type: cross Abstract: While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such meth

CSTrader: A Testbed for Language-Grounded Trading in a Community-Driven Virtual Asset Market

arXiv:2606.31461v1 Announce Type: new Abstract: Niche asset markets, such as Counter-Strike 2 (CS2) weapon skins, are small, volatile, and heavily driven by community discussions and platform rules. T

Curvature-Guided Module Localization for Low-Rank Detoxification of Backdoored Large Language Models

arXiv:2606.30899v1 Announce Type: cross Abstract: Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious beh

CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors

arXiv:2606.31557v1 Announce Type: cross Abstract: In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses. Alth

DA-Studio: An Agentic System for End-to-End Data Analysis

arXiv:2606.31423v1 Announce Type: cross Abstract: Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer. A practical system should auto

agentsarxiv-cs-ai

DANTE-W: Diffuse Albedo Neural Texturing in the Wild

model-releasesarxiv-cs-cv

arXiv:2606.30677v1 Announce Type: cross Abstract: Classical mesh texturing techniques blend captured multi-view images directly, which inevitably suffer from baked-in shading and casted shadows that c

DataEvolver: Self-Evolving Multi-Agent Data Construction for Text-Rich Image Generation

arXiv:2606.31537v1 Announce Type: new Abstract: Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic imag

safetyarxiv-cs-cv

Dataset Construction for Training LLM to Learn Analog Circuit Knowledge

arXiv:2508.10409v3 Announce Type: replace-cross Abstract: This paper constructs a textual dataset for training large language models (LLMs) to learn analog circuit knowledge and customizes LLM trainin

DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction

arXiv:2606.31085v1 Announce Type: new Abstract: Drug-drug interaction (DDI) prediction is essential for medication safety, yet it requires reasoning over heterogeneous biomedical evidence whose releva

safetyarxiv-cs-ai

Decodable Is Not Grounded: A Vision-Ablation Arbiter for VLM Spatial Reasoning

arXiv:2606.31257v1 Announce Type: new Abstract: The standard way to read latent knowledge out of a model, a linear probe confirmed by a steering recovery, can systematically overstate what a vision-la

applicationsarxiv-cs-cv

Tutorials

Deductive Logic in Language Models: Horizontal vs Vertical Reasoning

arXiv:2510.09340v2 Announce Type: replace Abstract: Recent language models exhibit significant logical reasoning abilities, yet the mechanisms supporting deductive inference remain poorly understood.

tutorialsarxiv-cs-ai

Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry

arXiv:2606.31291v1 Announce Type: new Abstract: Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics

safetyarxiv-cs-lg

Deep Spectral Models for Robust Dental Shape Generation

arXiv:2606.31293v1 Announce Type: new Abstract: Accurate modeling of dental crown morphology is fundamental for diagnosis, orthodontic planning, and computer-aided restoration design. However, dataset

applicationsarxiv-cs-cv

Delta-JEPA: Learning Action-Sensitive World Models via Latent Difference Decoding

arXiv:2606.31232v1 Announce Type: new Abstract: Learning visual world models for planning requires compact latent dynamics that remain sensitive to actions, yet reconstruction-free joint-embedding obj

DEMUN: Fast and accurate discovery of music notation in very large collections

arXiv:2606.31956v1 Announce Type: cross Abstract: Much of written musical heritage is preserved and digitised at memory institutions: libraries, museums, and archives. Owing to their collection struct

Local Ai

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos

arXiv:2606.31007v1 Announce Type: new Abstract: Vision foundation models such as SAM 3 can provide transferable object-level structure across diverse surgical video conditions, but segmentation output

local-aiarxiv-cs-cv

Design and Implementation of Agentic Orchestrations and Orchestration of Agents

arXiv:2606.31518v1 Announce Type: new Abstract: Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents,

agentsarxiv-cs-ai

Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences

arXiv:2604.06382v2 Announce Type: replace Abstract: Visual navigation is a fundamental capability of mobile service robots, yet the onboard cameras required for such navigation can capture privacy-sen

safetyarxiv-cs-ro

Local Ai

Detecting Audio Deepfakes on the Edge:Lightweight SSL-Based Detection in a Browser Plugin

arXiv:2606.30780v1 Announce Type: cross Abstract: Audio deepfakes are a growing challenge for the general public, as well as for journalists and fact-checkers. The latter need reliable tools to verify

local-aiarxiv-cs-ai

DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection

arXiv:2603.23455v2 Announce Type: replace Abstract: Multi-Modal LLMs (MLLMs) demonstrate strong visual grounding capabilities on popular object detection benchmarks like OdinW-13 and RefCOCO. However,

DeXposure-Claw: An Agentic System for DeFi Risk Supervision

arXiv:2606.19501v2 Announce Type: replace Abstract: Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read

agentsarxiv-cs-ai

DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks

arXiv:2602.03981v2 Announce Type: replace-cross Abstract: Credit exposure in Decentralized Finance (DeFi) is often implicit and token-mediated, creating a dense web of inter-protocol dependencies. Thu

applicationsarxiv-cs-ai

Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks

model-releasesarxiv-cs-lg

arXiv:2606.31700v1 Announce Type: new Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this c

Diffusion-based 4D Trajectory Prediction and Distributed Control for UAV Swarms

arXiv:2606.31197v1 Announce Type: new Abstract: Accurate 4D trajectory prediction and closed-loop tracking are essential for Unmanned Aerial Vehicle (UAV) swarms to achieve safe and efficient operatio

agentsarxiv-cs-ro

Diffusion-Based Material Regularization for Physics-Based Inverse Rendering

arXiv:2606.31065v1 Announce Type: new Abstract: Reconstructing physics-based 3D assets -- geometry, materials, and illumination -- from multi-view images is a core problem in computer graphics and vis

Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation

arXiv:2604.14790v2 Announce Type: replace Abstract: Interactive Evolutionary Computation (IEC) provides a powerful framework for optimizing subjective criteria such as human preferences and aesthetics

Diffusion-warm sampling of the XY model enables fast thermalization at scale

arXiv:2606.30773v1 Announce Type: cross Abstract: We introduce a novel technique for scalable sampling of spin-system states with continuous symmetries using diffusion models. By applying our approach

researcharxiv-cs-lg

DigitalCoach: Communication and Grounding Gaps in Human and Agentic Computer Use Coaching

arXiv:2606.31980v1 Announce Type: new Abstract: Agents are increasingly capable of automating software tasks, but can they teach humans how to use software themselves? We introduce DigitalCoach, a mul

agentsarxiv-cs-cl

Direction-Magnitude Decomposition for Low-Rank Matrix Optimization: Faster Convergence and Saddle-to-saddle Dynamics

arXiv:2606.31390v1 Announce Type: cross Abstract: Low-rank matrix optimization is often carried out via the Burer-Monteiro (BM) formulation, but choosing the factorization rank r is delicate and can s

researcharxiv-cs-lg

DISCOVER: A Solver for Distributional Counterfactual Explanations

arXiv:2603.16436v2 Announce Type: replace Abstract: Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions. Most existing method

safetyarxiv-cs-lg

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

arXiv:2508.01273v3 Announce Type: replace Abstract: Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language M

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

model-releasesarxiv-cs-cv

arXiv:2606.20196v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. Howe

Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation

arXiv:2606.31198v1 Announce Type: cross Abstract: Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D metho

Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation

arXiv:2512.21002v3 Announce Type: replace-cross Abstract: Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of re