LLMs know a lot and sound convincing-yet in clinical diagnosis, chemical synthesis, or industrial formulation they still hallucinate, inventing reasoning that violates guidelines or even basic physics. Scientific rules guide human experts beautifully, so why can't a model actually use them?

Introducing SciDC, from @TsinghuaNLP (OpenBMB member) with Nanjing University and Northeastern University. It turns flexible scientific knowledge into decoding constraints-so a locally-deployed domain model can only generate within the feasible region defined by expert rules.

1⃣️ Knowledge becomes constraints, not prompts. A strong general LLM (GLLM) acts as a one-time knowledge compiler, parsing a natural-language knowledge document into standardized, executable rules; a smaller domain LLM (DLLM) then generates under those rules-keeping domain data local for privacy and cutting adaptation cost.

2⃣️ Three layers of control. Top-layer rules govern macro reasoning structure (enforce a step sequence, e.g. extract findings before diagnosing); middle-layer rules enforce conditional multi-hop logic with dynamic backtracking when inconsistencies appear; bottom-layer rules mask decoder logits at the token level to guarantee syntactic and numerical feasibility.

3⃣️ +12% on average, no tuning. Across industrial formulation design, clinical tumor diagnosis, and retrosynthesis, SciDC lifts overall accuracy by +11.6 for both Qwen3-4B (42.5→54.1) and Qwen3-14B (51.4→63.0)-with no parameter tuning or hand-crafted edits, just the knowledge document.

4⃣️Harder tasks, bigger gains. On real (not simulated) medical records Qwen3-14B jumps 33.5%→45.1% exact match, reaching 70%+ with light expert prompting; even the domain model ChemDFM improves retrosynthesis hit@1 from 16.2%→29.0%-showing rules and domain knowledge are synergistic, not redundant.

arXiv: https://arxiv.org/abs/2604.06603 Code: https://github.com/Maotian-Ma/SciDC #AI #THUNLP #OpenBMB #LLM #AI4Science #Hallucination #NLP