arXiv AI
Jul 17
Provable Coordination for LLM Agents via Message Sequence Charts
arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 17
VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
arXiv AI
Jul 17
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
arXiv:2607. 15272v1 Announce Type: cross Abstract: Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts.
arXiv AI
Jul 17
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
arXiv:2605. 25170v2 Announce Type: replace-cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models.
arXiv AI
Jul 17
Interventional Causal Circuits for Safe Robot Action Testing and Failure Recovery
arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.
arXiv AI
Jul 17
When Words Are Safe But Actions Kill: Probing Physical Danger Beyond Text Safety in Hidden-State Risk Space
arXiv:2607. 15218v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become unsafe once grounded in the physical world.
arXiv AI
Jul 17
Knowing You at First Glance: Inferring Apparent Personality from Faces
arXiv:2607. 14631v1 Announce Type: cross Abstract: Inferring apparent personality from facial images is important in social scenarios for embodied agents in human-robot interaction.
arXiv AI
Jul 17
Building Agent Harnesses for Scientific Curation from Multimodal Sources
arXiv:2606. 21005v2 Announce Type: replace Abstract: Scientific discovery workflows often depend on structured curation from the literature.
arXiv AI
Jul 17
SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
arXiv:2607. 14494v1 Announce Type: new Abstract: Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executable logical form.
arXiv AI
Jul 17
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
arXiv:2607. 14890v1 Announce Type: new Abstract: Autonomous coding agents increasingly execute multi-step software work, but lifecycle states such as reviewed, tested, DONE, and ready-to-merge remain claims unless supported by current evidence.
arXiv AI
Jul 17
An offline approach to fNIRS-guided reinforcement learning for robot behavior
arXiv:2607. 14393v1 Announce Type: cross Abstract: Human-in-the-loop Reinforcement Learning has become a popular approach to training, finetuning, and aligning robot behavior with user preferences.
arXiv AI
Jul 17
ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System
arXiv:2607. 14178v1 Announce Type: new Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored.
arXiv AI
Jul 17
Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
arXiv:2607. 14387v1 Announce Type: new Abstract: Validating autonomous driving systems requires diverse, regulation-compliant test scenarios.
arXiv AI
Jul 17
A framework for single and multi-agent human-AI curiosity ecosystems
arXiv:2607. 06214v2 Announce Type: replace Abstract: This paper offers a framework for considering curiosity as an ecosystem.
arXiv Machine Learning
Jul 17
LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget
arXiv:2607. 14952v1 Announce Type: new Abstract: A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment.
arXiv AI
Jul 17
Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation
arXiv:2607. 14165v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked.
arXiv AI
Jul 17
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
arXiv:2607. 15001v1 Announce Type: cross Abstract: Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise required to turn research motivation into reliable computing workflows.
arXiv AI
Jul 17
SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon Strategy Game Planning
arXiv:2606. 29932v3 Announce Type: replace Abstract: Long-horizon strategic planning in complex strategy games requires coordinating tightly coupled decision domains, including technology, economy, diplomacy, and military, across hundreds of turns under imperfect information.
arXiv AI
Jul 17
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
arXiv:2607. 15205v1 Announce Type: cross Abstract: Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated mostly as a text-only task.
arXiv AI
Jul 17
Structured Feedback Improves Repair in an LLM Agent Loop
arXiv:2607. 14167v1 Announce Type: cross Abstract: LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified.