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arXiv:2603.02495v1 Announce Type: new Abstract: We consider a setting where agents take action by following their role models in a social network, and study strategies …
arXiv:2603.02504v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance on natural language tasks but remain unreliable in mathematical reasoning, frequently generating fluent …
arXiv:2603.02528v1 Announce Type: new Abstract: Accurate classification of autonomous vehicle (AV) driving behaviors is critical for safety validation, performance diagnosis, and traffic integration analysis. However, …
arXiv:2603.02540v1 Announce Type: new Abstract: Large language models (LLMs) exhibit a unified "general factor" of capability across 10 benchmarks, a finding confirmed by our factor …
arXiv:2603.02542v1 Announce Type: new Abstract: Autonomous driving systems require comprehensive evaluation in safety-critical scenarios to ensure safety and robustness. However, such scenarios are rare and …
arXiv:2603.02586v1 Announce Type: new Abstract: As large language models grow more capable, general AI agents have become increasingly prevalent in practical applications. However, existing benchmarks …
arXiv:2603.02599v1 Announce Type: new Abstract: In multi-model LLM serving, decode execution remains inefficient due to model-specific resource partitioning: since cross-model batching is not possible, memory-bound …
arXiv:2603.02601v1 Announce Type: new Abstract: Autonomous AI agents are deployed at unprecedented scale, yet no principled methodology exists for verifying that an agent has not …
arXiv:2603.02626v1 Announce Type: new Abstract: Autonomous web navigation requires agents to perceive complex visual environments and maintain long-term context, yet current Large Language Model (LLM) …
arXiv:2603.02668v1 Announce Type: new Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike …
arXiv:2603.02680v1 Announce Type: new Abstract: While Large Language Models (LLMs) form the cornerstone of sequential decision-making agent development, they have inherent limitations in high-frequency decision …
arXiv:2603.02688v1 Announce Type: new Abstract: To achieve general-purpose utility, we argue that robots must evolve from passive executors into active Information Retrieval users. In strictly …