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HypoAgent: An Agentic Framework for Interactive Abd... | AI Research

Key Takeaways

  • HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs Abductive reasoning is the process of inferring the mos...
  • Abductive reasoning over knowledge graphs aims to generate logical hypotheses that explain observed entities or facts.
  • To address these limitations, we propose HypoAgent, an Agentic framework for interactive abductive Hypothesis Generation over knowledge graphs.
  • Experiments on commonsense and biomedical domain-specific knowledge graphs demonstrate that HypoAgent achieves state-of-the-art semantic similarity under single-turn, multi-turn, and unconditional settings.
  • Our code is available at this https URL .
Paper AbstractExpand

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 Generation over knowledge graphs. HypoAgent integrates three agents: an Intent Recognition Agent that grounds user utterances and dialogue history into executable KG conditions, a Hypothesis Generation Agent that performs controllable hypothesis generation according to the extracted user intention, and a Root Cause Analysis Agent that diagnoses unreliable hypothesis fragments and leverages KG neighborhood probing to identify supported refinements. Experiments on commonsense and biomedical domain-specific knowledge graphs demonstrate that HypoAgent achieves state-of-the-art semantic similarity under single-turn, multi-turn, and unconditional settings. Our code is available at this https URL .

HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs
Abductive reasoning is the process of inferring the most likely explanation for a set of observed facts. In the context of knowledge graphs (KGs), this involves generating logical hypotheses that explain why certain entities are connected. While existing methods allow users to provide basic instructions to guide this process, they often struggle when user needs evolve during a conversation or when a generated hypothesis is partially incorrect. HypoAgent is a new framework designed to solve these issues by using a team of collaborative AI agents to manage interactive, multi-turn dialogues and perform precise, fragment-level repairs on failed hypotheses.

How HypoAgent Works

HypoAgent coordinates three specialized agents to handle the reasoning process:

  • Intent Recognition Agent: This agent acts as the bridge between the user and the knowledge graph. It analyzes the current user request alongside the history of the conversation to understand evolving goals, even when the user’s language is vague or relies on context from previous turns. It converts these natural-language intents into structured conditions that the system can execute.

  • Hypothesis Generation Agent: Once the intent is clear, this agent uses a lightweight generator to produce a first-order logic hypothesis that satisfies the user's constraints. It maintains a memory of the dialogue, ensuring that the generated output remains consistent with the user's ongoing exploration.

  • Root Cause Analysis Agent: If a hypothesis fails to explain the observations, this agent steps in to diagnose the problem. It breaks the hypothesis into smaller, executable fragments to identify which parts are reliable and which are incorrect. It then probes the knowledge graph for missing information and suggests specific refinements, allowing the system to fix the hypothesis rather than starting from scratch.

Improving Reasoning Through Iteration

A core strength of HypoAgent is its ability to learn from failure. Instead of discarding an entire hypothesis when it doesn't perfectly match the observed data, the Root Cause Analysis Agent identifies the specific logical components that are unsupported. By combining this internal diagnosis with external evidence found by searching the neighborhoods of the observed entities in the knowledge graph, the system can propose targeted corrections. This iterative loop allows users to refine their hypotheses through natural feedback, making the process more transparent and effective.

Performance and Results

The researchers tested HypoAgent across commonsense and biomedical knowledge graphs, including BioKG, PharmKG, and DBpedia50. The results demonstrate that the framework achieves state-of-the-art performance in semantic similarity across various scenarios, including single-turn, multi-turn, and unconditional generation. The experiments specifically highlight the effectiveness of the Root Cause Analysis Agent, which significantly boosts the quality of hypotheses in multi-turn interactions by enabling the system to recover from and repair errors dynamically.

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