arXiv:2604.04426v1 Announce Type: new
Abstract: Existing research on LLM agent security mainly focuses on prompt injection and unsafe input/output behaviors. However, as agents increasingly rely on third-party tools and MCP servers, a new class of sup…
arXiv:2302.00797v4 Announce Type: replace
Abstract: Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents’ strategies, and exploiting this opponent model by playing a best response. However, exist…
arXiv:2604.04804v1 Announce Type: new
Abstract: Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover si…
arXiv:2604.04604v1 Announce Type: cross
Abstract: AI agents – i.e. AI systems that autonomously plan, invoke external tools, and execute multi-step action chains with reduced human involvement – are being deployed at scale across enterprise functions …
arXiv:2604.03374v1 Announce Type: new
Abstract: Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that connect seemi…
arXiv:2502.13388v3 Announce Type: replace
Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Larg…
arXiv:2509.20349v2 Announce Type: replace
Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consistency required in reg…
arXiv:2510.09901v2 Announce Type: replace
Abstract: Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to a…
arXiv:2604.03260v1 Announce Type: new
Abstract: We introduce Focus, a method that learns which token pairs matter rather than approximating all of them. Learnable centroids assign tokens to groups; distant attention is restricted to same-group pairs w…
arXiv:2604.04839v1 Announce Type: new
Abstract: Neural machine translation (NMT) from Chinese to low-resource Southeast Asian languages remains severely constrained by the extreme scarcity of clean parallel corpora and the pervasive noise in existing …