Foundation models often underperform when confronted with domain-specific scientific formalism because their training objectives and data sources are misaligned with the requirements of formal scientific reasoning. These models are optimized for next-token prediction across broad, heterogeneous corpora, which promotes fluency but not fidelity to formal syntax, rigorous semantics, or the procedural correctness demanded in scientific disciplines. Tom B. Brown at OpenAI and colleagues have shown that scaling improves general capabilities while still leaving gaps in reliable structured reasoning. Emily Bender at University of Washington has highlighted how large, general language datasets can embed biases and idiosyncrasies that reduce trustworthiness in specialized contexts.
Causes
A major cause is data representation. Scientific formalism uses precise symbols, compact notation, and structured objects such as mathematical proofs, chemical SMILES strings, or domain-specific programming interfaces. Tokenization and statistical pattern learning treat these artifacts as surface tokens rather than as compositional, rule-governed systems. Nuance matters because a small syntactic mistake in a formula can invalidate an entire derivation. Another cause is scarcity of high-quality, curated corpora. Domain experts often produce texts behind paywalls, datasets with restricted access, or content encoded in figures and tables that general web scraping fails to capture. Dan Hendrycks at UC Berkeley and collaborators have demonstrated that models trained on general benchmarks can be fragile when evaluated on specialized or adversarially constructed datasets.
Consequences and contextual nuances
When foundation models misapply formalism the consequences range from wasted researcher time to safety-critical errors in engineering or medicine. In environmental modeling, a mis-specified parameter or unit error can alter projected impacts on ecosystems and local communities. Cultural and territorial factors shape scientific communication as well. Different research traditions favor distinct notations and argument styles, so a model trained predominantly on anglophone journal articles may struggle with formalism common in other scientific cultures. Yoshua Bengio at Mila and others argue that combining neural methods with symbolic or rule-based components can better capture the inductive biases inherent in formal domains.
Improving performance requires targeted data curation, hybrid architectures that respect symbolic structure, and evaluation paradigms driven by domain experts. Training alone at scale is insufficient without aligning model representations to the formal, procedural nature of scientific knowledge.