HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs
arXiv:2605.31370v1 Announce Type: new Abstract: Abductive reasoning over knowledge graphs aims to generate logical hypotheses that explain observed entities or facts. Existing controllable hypothesis generation methods allow users to guide this process with explicit conditions, but they remain limited in interactive settings: they struggle to ground evolving natural-language intents across multi-turn dialogues and provide little fine-grained diagnosis when generated hypotheses fail. To address these limitations, we propose HypoAgent, an Agentic framework for interactive abductive Hypothesis Ge
The development of more sophisticated AI models and the increasing demand for intuitive human-AI interaction in complex problem-solving environments necessitate advanced frameworks for abductive reasoning.
This framework significantly advances AI's ability to engage in multi-turn, natural-language dialogues for hypothesis generation, crucial for scientific discovery and intelligent automation.
AI systems can now better understand and adapt to evolving user intents in interactive hypothesis generation, moving beyond rigid, condition-based methods.
- · AI agents developers
- · Knowledge graph platforms
- · Scientific research (AI-assisted)
- · Complex problem-solving sectors
- · AI systems with limited interactive reasoning
- · Manual hypothesis generation processes
HypoAgent enables more effective and user-friendly interaction with AI for abductive reasoning over complex data structures like knowledge graphs.
This improved interaction could accelerate discovery and problem-solving in fields requiring deep inference, leading to breakthroughs in diverse domains.
The proliferation of such agentic frameworks might further automate white-collar tasks reliant on complex reasoning and contextual understanding, potentially reshaping professional landscapes.
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Read at arXiv cs.AI