arXiv AI
Jun 30
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
arXiv:2606. 30602v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used to automate complex, distributed workflows.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv Machine Learning
Jun 30
STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
arXiv Machine Learning
Jun 30
Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
arXiv:2606. 29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots.
arXiv AI
Jun 30
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
arXiv:2606. 29354v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned with efficient machine reasoning.
arXiv AI
Jun 30
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
arXiv:2606. 29867v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance.
arXiv AI
Jun 30
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
arXiv:2603. 05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
arXiv AI
Jun 30
The Verbose Context Problem in Medical Records
arXiv:2606. 29503v1 Announce Type: cross Abstract: The verbose context problem occurs when structured concepts have token-inefficient textual representations.
arXiv AI
Jun 30
Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models
arXiv:2601. 05366v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls.
arXiv AI
Jun 30
Direct Causation in International Humanitarian Law and the Challenge of AI-Mediated Civilian Cyber Operations
arXiv:2606. 29175v1 Announce Type: new Abstract: International humanitarian law protects civilians from direct attack unless and for such time as they take direct part in hostilities, with the ICRC's 2009 Interpretive Guidance operationalising this rule through a three-criterion cumulative test.
arXiv AI
Jun 30
Selective Memory Retention for Long-Horizon LLM Agents
arXiv:2606. 29178v1 Announce Type: new Abstract: When does retention matter for memory-augmented LLM agents?
arXiv AI
Jun 30
Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives
arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.
arXiv AI
Jun 30
SWE-MeM: Learning Adaptive Memory Management for Long-Horizon Coding Agents
arXiv:2606. 28434v1 Announce Type: cross Abstract: Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets.
arXiv AI
Jun 30
Agent libOS: A Runtime Substrate for Capability-Controlled Self-Evolving LLM Agents
arXiv:2606. 03895v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents are becoming long-running software actors rather than fixed tool users.
arXiv AI
Jun 30
The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
arXiv:2606. 29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case.
arXiv AI
Jun 30
Hierarchical Experimentalist Agents
arXiv:2606. 29315v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to take actions in the real world and support human decision-making, yet most agents rely on parametric knowledge, fixed post-training data, retrieval, or search.
arXiv AI
Jun 30
Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv:2606. 29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision.
arXiv AI
Jun 30
It Lied to a Doctor to Buy Poison Ingredients: Quantifying Real-World Misuse of Phone-use Agents
arXiv:2606. 27944v1 Announce Type: cross Abstract: Phone-use Agents can execute complex tasks end to end across real mobile applications.
arXiv AI
Jun 30
GUICrafter: Weakly-Supervised GUI Agent Leveraging Massive Unannotated Screenshots
arXiv:2606. 29705v1 Announce Type: new Abstract: Data, as the fundamental substrate of modern intelligence, has greatly driven the development of current foundation models.
arXiv AI
Jun 30
LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving
arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.