arXiv:2607. 01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation.
arXiv:2609.37588v1 Announce Type: new Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the...
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
arXiv:2606. 30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified.
The paper introduces PlanPool, a method for agentic Text-to-SQL systems to manage clarification questions by maintaining a mutable question pool. By requiring agents to explicitly ask or drop each planned question, PlanPool improves ambiguity coverage and reduces silent failures compared to unconstrained or prompt-based approaches. Experiments on benchmarks derived from BIRD-Interact and Spider show that PlanPool achieves competitive execution accuracy while better handling underspecification.
Conversational agents can retrieve information directly or delegate retrieval to a subagent. This design choice affects how retrieval history is maintained, what context each model call receives, and...
arXiv:2611.07683v1 Announce Type: cross Abstract: Users of egocentric AI assistants often ask short, ambiguous questions whose intended referents cannot be uniquely resolved from video and dialogue h...
The paper investigates how different retrieval designs affect conversational agents. Three approaches are compared: skill-based retrieval in the main agent, a non‑persistent retrieval subagent that generates findings, and a persistent retrieval subagent that keeps history and returns evidence to the main agent. The persistent design reduces token usage and API cost by 43.3% and 45.0% respectively, cuts response time by 9.1%, and allows the main agent to request corrections from the subagent.
The paper presents a tri‑agent framework for evaluating large language models’ question‑clarification abilities. It involves a Question Clarifying Agent that identifies ambiguities and asks follow‑up questions, a Respondent Agent that simulates human replies, and an Evaluator Agent that judges the dialogue using metrics such as ambiguity handling, question quality, dialogue efficiency, language appropriateness, and intent alignment. The authors illustrate the approach with synthetic supply‑chain data and discuss validating the evaluator against human judgments.
arXiv:2605.06223v4 Announce Type: replace Abstract: Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical a...
The paper introduces VagueBench, a benchmark that degrades seed queries into vague variants to evaluate large language models’ ability to ask follow‑up questions when user queries are incomplete. It proposes DialogGRPO, an offline reinforcement learning approach that models multi‑turn elicitation as a dialogue‑state directed acyclic graph, using dense turn‑level rewards to guide question selection. DialogGRPO achieves over 88% success in eliciting missing information on vague queries, improves answer coverage and recovery efficiency, and outperforms prompting and test‑time scaling baselines while generalizing zero‑shot to other dialogue datasets.
Large Language Models (LLMs) are increasingly deployed in interactive systems where understanding user intent precisely is paramount. A key capability for such systems is effective question clarificat...