IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inference
arXiv:2603.03325v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user's underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users' accumulated intent patterns that could provide valuable references for more accurate and generalizable understanding. To address this gap, we propose IntPro, a proxy agent that learns to adapt to individual users via retrieval-conditioned intent inference. We design intent explanations that abstract how contextual signals connect to expressed intents, and store them in an
arXiv:2603.03325v1 Announce Type: cross Abstract: Large language models (LLMs) have become integral to modern Human-AI collaboration workflows, where accurately understanding user intent serves as a crucial step for generating satisfactory responses. Context-aware intent understanding, which involves inferring user intentions from situational environments, is inherently challenging because it requires reasoning over both the immediate context and the user's underlying motivations that drive their behavior. Moreover, existing approaches often treat intent understanding as a static recognition task, overlooking users' accumulated intent patterns that could provide valuable references for more accurate and generalizable understanding. To address this gap, we propose IntPro, a proxy agent that learns to adapt to individual users via retrieval-conditioned intent inference. We design intent explanations that abstract how contextual signals connect to expressed intents, and store them in an individual intent history library for retrieval. We train IntPro through supervised fine-tuning on retrieval-conditioned trajectories and multi-turn Group Relative Policy Optimization (GRPO) with tool-aware reward functions, enabling the agent to learn when to leverage historical intent patterns and when to infer directly. Experiments across three diverse scenarios (Highlight-Intent, MIntRec2.0, and Weibo Post-Sync) demonstrate that IntPro achieves strong intent understanding performance with effective context-aware reasoning capabilities across different scenarios and model types.
Executive Summary
This article introduces IntPro, a proxy agent that utilizes retrieval-conditioned inference to improve context-aware intent understanding in human-AI collaboration workflows. IntPro learns to adapt to individual users by storing their intent patterns in an individual intent history library for retrieval. The agent is trained through supervised fine-tuning and multi-turn Group Relative Policy Optimization (GRPO) with tool-aware reward functions. Experiments across three diverse scenarios demonstrate IntPro's strong intent understanding performance and effective context-aware reasoning capabilities. The proposed approach addresses the limitations of existing approaches by leveraging users' accumulated intent patterns and adapting to individual users. The results have significant implications for improving human-AI collaboration and enhancing the accuracy of intent understanding in various applications.
Key Points
- ▸ IntPro proposes a novel proxy agent for context-aware intent understanding using retrieval-conditioned inference.
- ▸ IntPro learns to adapt to individual users by storing their intent patterns in an individual intent history library.
- ▸ Experiments demonstrate IntPro's strong intent understanding performance and effective context-aware reasoning capabilities.
Merits
Strength
The proposed approach effectively addresses the limitations of existing approaches by leveraging users' accumulated intent patterns and adapting to individual users.
Methodological Innovation
The use of retrieval-conditioned inference and tool-aware reward functions in the training process is a novel and effective approach to improving context-aware intent understanding.
Scalability
The design of IntPro allows for efficient adaptation to individual users, making it a scalable solution for real-world applications.
Demerits
Limitation
The proposed approach may require significant computational resources and training data for large-scale applications.
Generalizability
The results may not be generalizable to all domains and applications, and further experimentation is needed to assess its robustness.
Expert Commentary
The article presents a significant contribution to the field of human-AI collaboration and natural language processing. The proposed approach is well-designed and effectively addresses the limitations of existing approaches. However, the computational resources and training data required for large-scale applications are significant limitations. Further experimentation is needed to assess the robustness and generalizability of the proposed approach. The implications for policy-making and practical applications are significant, and the results have the potential to improve human-AI collaboration and enhance the accuracy of intent understanding in various applications.
Recommendations
- ✓ Further experimentation is needed to assess the robustness and generalizability of the proposed approach.
- ✓ The proposed approach should be applied to various applications to evaluate its effectiveness and scalability.