arXiv AI
Jun 24
OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility
arXiv:2606. 24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 24
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
arXiv:2512. 03476v3 Announce Type: replace-cross Abstract: Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck.
arXiv AI
Jun 24
Skills for the future software profession: beyond agentic AI!
arXiv:2606. 21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers?
arXiv AI
Jun 24
The $\mathbf{P}$-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs
arXiv:2601. 18747v2 Announce Type: replace-cross Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows.
arXiv AI
Jun 24
LemonHarness Technical Report
arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.
arXiv AI
Jun 24
IPO Finance Agent: Evaluation of LLM Financial Analysts beyond Finance Agent v2, with Automated Rubric Generation -- the Case of the SpaceX (SPCX) IPO
arXiv:2606. 23032v2 Announce Type: replace Abstract: Finance Agent v2 (by Vals AI) has emerged as the reference benchmark for evaluating both Anthropic Claude and OpenAI ChatGPT frontier language models on financial tasks.
arXiv AI
Jun 24
Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
arXiv:2606. 24026v1 Announce Type: new Abstract: Mechanistic interpretability has made substantial progress in automatically localizing circuits, but explaining what localized components do remains labor-intensive and difficult to standardize.
arXiv AI
Jun 24
ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection
arXiv:2606. 24112v1 Announce Type: new Abstract: Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image framing errors.
arXiv AI
Jun 24
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
arXiv:2606. 24429v1 Announce Type: cross Abstract: Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood.
arXiv AI
Jun 24
Beyond Trajectory Imitation: Strategy-Guided Policy Optimization for LLM Reasoning
arXiv:2606. 24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferring what to answer rather than how to reason.
arXiv AI
Jun 24
HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).
arXiv AI
Jun 24
UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.
arXiv AI
Jun 24
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
arXiv:2606. 24515v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) execute high-level user goals by perceiving and acting directly within graphical user interfaces.
arXiv AI
Jun 24
Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation
arXiv:2606. 24096v1 Announce Type: cross Abstract: Robust perception underpins autonomous driving, and most recent progress comes from scaling the model-larger backbones, foundation models, and cooperative multi-agent fusion.
arXiv AI
Jun 24
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
arXiv AI
Jun 24
Subjective-Graph LLM Agents for Simulating Uncertainty in Classroom Social Perception
arXiv:2603. 20750v2 Announce Type: replace Abstract: Social actors do not observe a common social world: each individual forms judgments from a partial and potentially distorted view of the surrounding network.
arXiv Machine Learning
Jun 24
Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations
arXiv:2406. 07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity.
arXiv AI
Jun 24
Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
arXiv:2606. 23832v1 Announce Type: cross Abstract: The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations.
arXiv AI
Jun 24
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
arXiv:2407. 18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.
arXiv AI
Jun 24
OpenThoughts-Agent: Data Recipes for Agentic Models
arXiv:2606. 24855v1 Announce Type: new Abstract: Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents.