# HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs

arXiv: https://arxiv.org/abs/2605.31370 · HTML: https://mlsanity.com/paper/2605.31370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 29 May 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 19 of 19)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://mlsanity.com/paper/2012.13048/) (2020)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning](https://mlsanity.com/paper/2010.05906/) (2020)
- [ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT](https://mlsanity.com/paper/2304.11107/) (2023)
- [True Detective: A Deep Abductive Reasoning Benchmark Undoable for GPT-3 and Challenging for GPT-4](https://mlsanity.com/paper/2212.10114/) (2022)
- [UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations](https://mlsanity.com/paper/2311.08469/) (2023)
- [Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation](https://mlsanity.com/paper/2312.15643/) (2023)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Self-Consistent Narrative Prompts on Abductive Natural Language Inference](https://mlsanity.com/paper/2309.08303/) (2023)
- [Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs](https://mlsanity.com/paper/2505.20948/) (2025)
- [Towards Learning Abductive Reasoning using VSA Distributed Representations](https://mlsanity.com/paper/2406.19121/) (2024)
- [Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection](https://mlsanity.com/paper/2412.08457/) (2024)
- [Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model](https://mlsanity.com/paper/2510.11462/) (2025)
- [Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive Reasoning](https://mlsanity.com/paper/2404.18982/) (2024)
- [Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference](https://mlsanity.com/paper/2202.10408/) (2022)
- [NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training](https://mlsanity.com/paper/2602.21597/) (2026)
- [KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning](https://mlsanity.com/paper/2605.14907/) (2026)

## Abstract (arXiv)

Abductive reasoning over knowledge graphs (KGs) seeks a first-order logic hypothesis whose answer set explains a given set of observed entities. Since many hypotheses can explain the same observations, controllable hypothesis generators condition generation on entities, relations, or logical patterns, but they treat generation as a single step. A generated hypothesis may be well-formed and satisfy the given conditions yet still fail to explain the observations, and the model has no mechanism to detect or correct this mismatch. Closing the abductive cycle requires revising a discrete, structured hypothesis, which large language models cannot do reliably, even though they iterate readily in natural language. We propose HypoAgent, an agentic hypothesis refinement framework that treats condition signals not only as expressions of user intent but also as operators that steer generation. A Hypothesis Proposal Agent calls a small trained generator to propose a hypothesis. Root-cause analysis then uses fragment-level coverage to locate branches worth inspecting and gathers neighborhood evidence from the training graph. A Hypothesis Refiner Agent combines this diagnosis with previously generated hypotheses to produce updated conditions and directly revised hypotheses. HypoAgent outperforms one-shot generation in single-turn and multi-turn settings on BioKG, PharmKG8k, and DBpedia50, and in the unconditional setting on DBpedia50. Our code is available at https://github.com/HKUST-KnowComp/HypoAgent.