Agentic retrieval policies in biomedical multi-hop reasoning (SSE-Bio) can be formally verified via probabilistic Datalog to reduce hallucinated protein-disease links by 40%, using attention-derived co-expression signals from ELISA as grounding constraints.
Agentic retrieval policies in biomedical multi-hop reasoning (SSE-Bio) can be formally verified via probabilistic Datalog to reduce hallucinated protein-disease links by 40%, using attention-derived co-expression signals from ELISA as grounding constraints.
Adversarial Debate Score
43% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- SSE-Bio: A Structured Self-Evolving Agent with Agentic Retrieval Policy for Multi-Hop Biomedical Reasoning
Biomedical multi-hop question answering (QA) requires models to connect evidence across intermediate entities such as diseases, drugs, proteins, and phenotypes. Existing agents typically rely on stati...
- ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations...
- MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering
Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. Exist...
- Evidence, Logic, and Compliance: Multi-Agent Structured Graph Reasoning with Expert Arbitration for Medical Referral
Medical referral (directing patients to the appropriate hospital department) is a complex decision-making process requiring the synthesis of multimodal data, including patient narratives, laboratory i...
- Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion
Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus s...
Literature Assessment
An LLM's reading of the literature — not computational verification.
Probabilistic Datalog shows promise but lacks definitive evidence for hallucination reduction.
Method: literature_meta · Result: inconclusive
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.