arXiv AI
Jul 8
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
arXiv:2607. 06413v1 Announce Type: cross Abstract: Large language model coding agents increasingly perform open-ended data modeling and analysis.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 8
A toy framework for single and multi-agent human-AI curiosity ecosystems
arXiv:2607. 06214v1 Announce Type: new Abstract: This paper offers a toy framework for considering curiosity as an ecosystem.
arXiv AI
Jul 8
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
arXiv:2607. 06435v1 Announce Type: new Abstract: Data from Singapore indicated that about 31% of the population had evidence of Helicobacter pylori infection.
arXiv Machine Learning
Jul 8
Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
arXiv:2605. 24709v2 Announce Type: replace Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.
arXiv AI
Jul 8
Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors
arXiv:2607. 05663v1 Announce Type: cross Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments.
arXiv AI
Jul 8
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
arXiv:2607. 06522v1 Announce Type: new Abstract: Vision-language models (VLMs) struggle to generalize in interactive physical reasoning, particularly under unseen tasks and environments.
arXiv AI
Jul 8
Tangent classes of matroids and wonderful compactifications
arXiv:2607. 05835v1 Announce Type: cross Abstract: For every loopless matroid $M$ and every Feichtner--Yuzvinsky building set $\mathcal{G}$ containing the top flat, we construct an integral tangent class $T_{M,\mathcal{G}}^{\mathbb{Z}}\in K_{\mathbb{Z}}(M,\mathcal{G})$; in the realizable case it specializes to the class of the tangent bundle of the corresponding wonderful compactification, it recovers the Hilbert series of the Chow ring through Hirzebruch--Riemann--Roch, and it satisfies the expected Chern-alpha lower bounds.
arXiv AI
Jul 8
Proof of Execution: Runtime Verification for Governed AI Agent Actions
arXiv:2607. 05397v1 Announce Type: cross Abstract: Agent systems increasingly execute rather than advise.
arXiv AI
Jul 8
Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning
arXiv:2607. 05458v1 Announce Type: cross Abstract: Large language model (LLM) agents are usually improved by changing prompts, models, or hand-written workflows, while the execution harness around the model is treated as fixed infrastructure.
arXiv AI
Jul 8
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
arXiv:2607. 05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and operate over extended horizons.
arXiv AI
Jul 8
Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
arXiv:2607. 05577v1 Announce Type: new Abstract: Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narration that revealed it, whether a setup paid off, and how a relationship shifted.
arXiv AI
Jul 8
Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents
arXiv:2607. 05690v1 Announce Type: new Abstract: Language agents run a loop - observe, reason, act - but the memory they reason over sits outside it: a store queried at most once per turn.
arXiv AI
Jul 8
PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
arXiv:2607. 05915v1 Announce Type: new Abstract: PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers.
arXiv AI
Jul 8
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
arXiv:2607. 05970v1 Announce Type: cross Abstract: Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems.
arXiv AI
Jul 8
Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
arXiv AI
Jul 8
From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
arXiv:2607. 06080v1 Announce Type: cross Abstract: Putnam's Social Capital Theory is a foundational framework for collective action and community prosperity.
arXiv Machine Learning
Jul 8
Statistically Meaningful Geometry and Gauge Symmetry Breaking: A Geometric Foundation for Scientific Discovery and Intelligence Emergence
arXiv:2607. 05436v1 Announce Type: new Abstract: The rapid scaling of over-parameterized machine learning architectures, particularly LLMs, raises a profound crisis: do these systems exhibit genuine intelligence, or are they merely sophisticated statistical pattern matchers?
arXiv AI
Jul 8
UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods
arXiv:2607. 06202v1 Announce Type: cross Abstract: The deployment of Mixture-of-Experts (MoE) models on production high-bandwidth superpods, such as NVIDIA's NVL72/576 and Huawei's CloudMatrix384, introduces critical challenges beyond raw interconnect bandwidth.
arXiv AI
Jul 8
Quantifying Frontier LLM Capabilities for Container Sandbox Escape
arXiv:2603. 02277v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, creating novel security risks.
arXiv Machine Learning
Jul 8
Scalable Perturbation Learning for Online Self-Supervised Echo State Networks
arXiv:2607. 06079v1 Announce Type: new Abstract: Intelligent systems should not only solve tasks but also adapt under real-world constraints.