SCHEMA: A Topology-Aware Framework for Detecting Hallucinations in Scientific AI Agents
A novel evaluation framework, SCHEMA, has been launched to tackle hallucinations in large language model (LLM) agents utilized in scientific research. Detailed in a paper on arXiv (ID: 2608.00711), this marks the first evaluation system that is both evidence-grounded and topology-aware for scientific agents. SCHEMA automatically generates scientific concept graphs from benchmark seeds and literature, creating graph-grounded tasks that encompass claim verification, multi-hop reasoning, open-ended explanations, and experimental code generation. The framework addresses the limitation of existing hallucination benchmarks, which often treat facts in isolation and apply uniform accuracy metrics, neglecting the interconnectedness of scientific knowledge. By considering this topological structure, SCHEMA aims to provide a more accurate evaluation of agent performance, crucial in scientific contexts where a single flawed claim can derail entire research paths. The paper was submitted to arXiv as a 'new' announcement.
Key facts
- SCHEMA is the first evidence-grounded, topology-aware evaluation framework for hallucinations in scientific agents.
- It automatically constructs scientific concept graphs from benchmark seeds and literature evidence.
- It synthesizes graph-grounded tasks including claim verification, multi-hop reasoning, open-ended explanation, and experimental code generation.
- Existing hallucination benchmarks operate at the surface level, treating facts in isolation.
- The framework addresses the propagation of errors through multi-step reasoning in scientific research.
- The paper is available on arXiv with ID 2608.00711.
- The announcement type is 'new'.
- The framework aims to improve reliability of LLM agents in scientific research.
Entities
Institutions
- arXiv