arXiv:2606. 01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through questioning.
arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.
arXiv:2407. 03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks.
arXiv:2604. 03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns.
arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.
arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.
arXiv:2607. 03093v1 Announce Type: cross Abstract: Thinking has emerged as a critical capability for Large Language Models (LLMs) tackling complex tasks.
arXiv:2608. 12062v1 Announce Type: cross Abstract: Developing dialogue systems capable of engaging in multi-turn, goal-oriented conversations remains a significant challenge, especially in specialized domains with limited data.
arXiv:2608. 01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts.
arXiv:2510. 05150v3 Announce Type: replace-cross Abstract: Recent advances in spoken dialogue language models (SDLMs) reflect growing interest in shifting from turn-based to full-duplex systems, where the models continuously perceive user speech streams while generating responses.
arXiv:2607. 27405v2 Announce Type: replace-cross Abstract: Both expressions of uncertainty and inferences are ubiquitous in natural language, and valid inferences over natural-language expressions of uncertainty are necessary for not only everyday conversations but also for high-stakes domains such as medicine and law.
arXiv:2607. 28894v1 Announce Type: new Abstract: Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior.
arXiv:2606. 12332v1 Announce Type: cross Abstract: Evaluating multi-turn dialogue is challenging because quality emerges across turns rather than within individual responses.