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arXiv:2602.16012v1 Announce Type: new Abstract: Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under …
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:2602.16039v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems …
arXiv:2602.16050v1 Announce Type: new Abstract: Background: Large language models have demonstrated strong performance on general medical examinations, but subspecialty clinical reasoning remains challenging due to …
arXiv:2602.16066v1 Announce Type: new Abstract: Adapting one's thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. In …
arXiv:2602.16105v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or …
arXiv:2602.16173v1 Announce Type: new Abstract: Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches …
arXiv:2602.16179v1 Announce Type: new Abstract: We show that training AI agents on high-fidelity reinforcement learning environments produces capabilities that generalize beyond the training distribution. We …
arXiv:2602.16192v1 Announce Type: new Abstract: Driven by our mission of "uplifting the world with memory," this paper explores the design concept of "memory" that is …
arXiv:2602.16246v1 Announce Type: new Abstract: Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks …
arXiv:2602.16301v1 Announce Type: new Abstract: Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can …
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 …