Tag: cs.MA
arXiv:2602.21251v1 Announce Type: cross Abstract: Legacy JavaScript systems lack type safety, making maintenance risky. While TypeScript can help, manually adding types is expensive. Previous automated …
arXiv:2602.22302v1 Announce Type: new Abstract: Traditional software relies on contracts -- APIs, type systems, assertions -- to specify and enforce correct behavior. AI agents, by …
arXiv:2602.21262v1 Announce Type: new Abstract: With increasing integration of Large Language Models (LLMs) into areas of high-stakes human decision-making, it is important to understand the …
arXiv:2602.21515v1 Announce Type: new Abstract: Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. Unfortunately, existing approaches …
arXiv:2602.17875v1 Announce Type: cross Abstract: We present MultiVer, a zero-shot multi-agent system for vulnerability detection that achieves state-of-the-art recall without fine-tuning. A four-agent ensemble (security, …
arXiv:2602.15055v1 Announce Type: cross Abstract: In the artificial intelligence space, as we transition from isolated large language models to autonomous agents capable of complex reasoning …
arXiv:2602.16037v1 Announce Type: new Abstract: Autonomous agentic workflows that iteratively refine their own behavior hold considerable promise, yet their failure modes remain poorly characterized. We …
arXiv:2602.16424v1 Announce Type: new Abstract: Multiagent AI systems require consistent communication, but we lack methods to verify that agents share the same understanding of the …
arXiv:2602.16435v1 Announce Type: new Abstract: Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods …
arXiv:2602.15909v1 Announce Type: cross Abstract: Deep learning-based respiratory auscultation is currently hindered by two fundamental challenges: (i) inherent information loss, as converting signals into spectrograms …
arXiv:2602.16196v1 Announce Type: new Abstract: Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the …
arXiv:2602.15198v1 Announce Type: cross Abstract: Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a …
