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Ask-before-Plan: Proactive Language Agents for Real-World Planning
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The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs in proactive planning, we propose a novel multi-agent framework, Clarification-Execution-Planning (\texttt{CEP}), which consists of three agents specialized in clarification, execution, and planning. We introduce the trajectory tuning scheme for the clarification agent and static execution agent, as well as the memory recollection mechanism for the dynamic execution agent. Extensive evaluations and comprehensive analyses conducted on the Ask-before-Plan dataset validate the effectiveness of our proposed framework.
Forward citations
Cited by 6 Pith papers
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Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution
An eight-agent question-asking system that front-loads intent clarification produced more complete prompts, higher-rated outputs, and single-turn task completion in a four-person pilot, with unstable effect sizes.
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Clarify Before Executing: A Self-Evolving Agent for Resolving Intent Asymmetry in 3D Tool Orchestration
A clarification-first 3D agent, trained by simulated multi-turn dialogue, reaches 60.4% and 43.3% success on single- and multi-step 3D tool tasks, more than doubling prior baselines.
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HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation
VLAC-Cut-guided multi-robot HITL post-training reaches 80–95% success and 1.7–4.2× throughput over the base VLA, outperforming HITL-only under the same human budget.
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ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents
A unified evaluation framework for proactive dialogue agents, built with 328 synthetic environments across six domains, shows that thinking modes improve target planning but not dialogue guidance in a 22-model comparison.
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ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent
An LLM-based UI agent monitors visual analytics interactions, detects when users struggle, infers their intent, and proactively provides context-aware guidance.
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Bridging the Gap: From Ad-hoc to Proactive Search in Conversations
Conv2Query fine-tunes an LLM to convert conversational context into ad-hoc queries, enabling off-the-shelf retrievers to work effectively on proactive search in conversations.
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