CLEAT Framework Released: Revolutionizing Interactive Clarification for AI Agents via Value-Guided Co-Training

Summary:The arXiv has introduced CLEAT (CLarification through valuE-guided Agent co-Training), a novel framework that enhances AI agents' performance in interactive tasks by co-training a clarification-value controller and task policy based on shared interaction outcomes. CLEAT predicts the value of clarifications and selects useful questions, enabling agents to better understand user intent and adjust their behavior strategies. Experimental results demonstrate that CLEAT outperforms existing baselines

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Technical Mechanism Analysis

The core of the CLEAT framework lies in its value-guided co-training mechanism, which integrates clarification-value prediction with task policy optimization. Its main components include:

1. Clarification-Value Controller: This component predicts the potential value of each clarification question by analyzing interaction history and task objectives to assess the necessity of clarification.
2. Task Policy: Based on the output of the clarification-value controller, the task policy selects the most useful clarification questions and adjusts its behavior decisions after receiving answers.
3. Paired Rollouts: CLEAT uses a paired rollouts mechanism to estimate the value of clarification by comparing the results of conducting and not conducting clarification under the same interaction history.

Engineering Trade-offs and Performance

The CLEAT framework demonstrates strong performance across multiple benchmarks, particularly in handling complex tasks. Here are the main advantages and trade-offs of CLEAT:

• Advantages:
  • Enhanced Task Policy Performance: Even when the controller is removed during inference, CLEAT's co-training mechanism improves the task policy's performance.
  • Adaptability: CLEAT can dynamically adjust clarification guidance based on the agent's learning process, maintaining efficiency in changing task environments.
• Trade-offs:
  • Computational Overhead: The paired rollouts mechanism increases computational overhead, which may affect real-time performance, especially when handling long interaction histories.
  • Data Requirements: CLEAT requires large amounts of high-quality interaction data to train the clarification-value controller and task policy, posing higher demands on data collection.

Developer Implementation and Deployment Recommendations

For developers, implementing the CLEAT framework requires considering the following factors:

1. Data Preparation: Ensure sufficient interaction data to train the CLEAT model, and pay attention to the quality and diversity of the data.
2. Computing Resources: The co-training mechanism of CLEAT has certain requirements for computing resources. It is recommended to deploy in an environment with GPU acceleration.
3. Model Optimization: During deployment, model compression and quantization techniques can be used to reduce CLEAT's computational overhead and improve real-time performance.
4. Continuous Evaluation and Iteration: Regularly evaluate CLEAT's performance in specific application scenarios and continuously iterate and optimize the model based on feedback.

The CLEAT framework provides a new technical path for the performance of agents in interactive tasks, with broad application prospects in scenarios that require a high level of understanding of user intent and dynamic adjustment of behavior.

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Source: ArXiv AI (cs.AI) (2026-10-12)

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Tags: #CLEAT Follow #Intelligent Agents Follow #Interactive AI Follow #Reinforcement Learning Follow #Value-Guided Follow