arXiv:2608. 19202v1 Announce Type: new Abstract: Interactive AI agents must acquire the right context as efficiently as possible.
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The paper introduces OR‑Clarify, a benchmark that tests whether large language models can identify missing elements in natural‑language optimization requests before formulating a mathematical model. Each task provides a partial problem description and hides structured slots; agents interact with a simulated user to recover these slots, with metrics for accuracy, stopping decisions, and interaction cost. The authors also propose InterOPT, a two‑stage framework that detects unresolved gaps and decides whether to ask further questions or stop, achieving superior slot recovery in choice‑based experiments and competitive performance in open‑ended settings.
arXiv:2607. 20064v1 Announce Type: new Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents.
arXiv:2607. 02686v1 Announce Type: new Abstract: Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs) that carry broad reasoning priors.
arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.
arXiv:2606. 03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control.
arXiv:2607. 14105v1 Announce Type: cross Abstract: For Large Language Models to reliably answer user queries, users must clearly specify requirements, context, and constraints.