Publications
Research
Hill Research publishes at top-tier venues in AI, machine learning, and medical informatics. Our research is the foundation that TriClick is built on — spanning LLM reasoning, AI agents, retrieval-augmented generation, knowledge graphs, and translational biology.
9 papers at premier venues in 2025–2026 including ACL, AAAI, SIGMETRICS, MLSys, and JAMIA.
Real-Time Clinical Analytics at Scale: A Platform Built on Large Language Models-Powered Knowledge Graphs
Hill Research · 2026
Describes ClinicalMind, the knowledge graph layer underneath TriClick. Initializes from 300 curated authoritative sources, reducing LLM invocation costs by 70%. Processes 110,000 clinical documents and 60,000 EMRs with 1.5M core concepts and 3M relationships.
- 110,000 clinical documents + 60,000 EMRs processed
- 1.5M core concepts, 3M primary relationships
- Average query delay: 1.7 seconds
- BLEU: 0.85, ROUGE: 0.92
From Trajectories to Graphs: Contract-Checked Editing for Verifier-Guided LLM Reasoning
Dr. Jack Li et al. · 2026
Introduces contract-checked graph editing for LLM reasoning. Represents LLM outputs as typed reasoning graphs and runs a deterministic structural gate before the expensive verifier, filtering structurally invalid candidates immediately.
- Verifier-runnable recombination: 41.2% → 92.8%
- Accuracy: +6.1 on MATH, +9.1 on MATH Level 5
- 42% fewer verifier calls
HyperWorld: Hybrid World Models for Grounded Language Agents
Dr. Jack Li et al. · 2026
A hybrid world model combining SSM-based dynamics with entity-centric episodic memory and critic-guided rollout planning. Enables AI agents to simulate the impact of their decisions before committing to action.
- Outperformed GPT-4+ReAct by 11-14 points on ALFWorld, WebShop, SciWorld
- 53% reduction in constraint violations
EviDex: Provenance-Weighted Evidence-Path Indexing for Fresh and Auditable Retrieval under Continuous Updates
Dr. Jack Li · 2026
Replaces periodic RAG refresh with log-structured online compaction over intent-partitioned evidence-path buckets. Every retrieved path carries its provenance for regulatory audit trails.
- Evidence-set violation at 15 min: 1.3% (vs 2.4% baseline)
- Cost: $0.68 per 1k queries — 42% cheaper than adaptive TTL
- 10M docs / 16 nodes: 1,856 queries/sec, p99 latency 2.14s
- Clinical correctness: 0.884 on 800-question physician-rated test
Ontology-Guided Long-Term Memory for Conversational RAG
Dr. Jack Li · 2026
Extracts durable user facts into a lightweight ontology memory graph and routes between graph-first and dense-first retrieval with a budget-aware learnable router. Solves the problem of dense retrieval failing in long multi-session conversations.
- Recall@10: 0.70 (vs 0.58 for dense-only)
- 47% reduction in cross-modality disagreement
- 81% cost reduction vs long-context methods
Med-ICE: Multi-Agent Consensus Framework for Trustworthy Medical AI
Zhiyuan Chen et al. · 2026
A multi-agent LLM framework for high-stakes medical tasks. Multiple agents generate diverse reasoning chains, a semantic consensus module aligns reasoning patterns, and iterative refinement continues until convergence — like a panel of specialists debating a diagnosis.
- 5-8% improvement in factual accuracy
- Fewer unsupported claims across agents
- Transparent disagreement surfacing for clinicians
CSLAN: Cross-Species Latent Alignment Network
Dr. Rui Wu et al. · 2026
A transfer learning framework that bridges mouse and human single-cell datasets. Uses species-invariant genomic features to identify human trauma-related immune cells with minimal human samples.
- 96.67% accuracy with only 240 human samples
- Bridges cross-species data scarcity
- Scalable to disease modeling and drug development
Dynamic Consistency Index for Gene Expression Dynamics
Dr. Rui Wu · 2026
A new metric for measuring how gene expression evolves across immune cell types over time. Enables modeling of gene expression dynamics with high accuracy while estimating uncertainty.
- High-accuracy temporal gene expression modeling
- Built-in uncertainty estimation
- Supports robust biological insights
Consensus-Based Framework for Reducing LLM Hallucinations in Clinical AI
Zhiyuan Chen · 2026
Models from different families challenge each other in a consensus-based framework, improving accuracy and advancing toward transparent, audit-ready clinical AI systems.
- Improved accuracy over single-judge models
- Cross-family model consensus validation
- Designed for audit-ready clinical settings
Interested in Our Research?
We're always looking for collaborators in clinical AI, NLP, and biostatistics. Get in touch.