[ PanLang ]
Grammar-Constrained Scientific Sequence Modeling via Paninian Derivation States
Abstract
PanLang is an R&D track exploring whether grammar-constrained tokenization can improve model reliability in highly structured domains such as genomics, chemistry, and formal notation. The current work is experimental and focused on reproducible evaluation.
Theoretical Foundations
Grammar Specification
Define domain grammars with explicit rule sets, constraints, and dependency relationships that can be checked during sequence generation.
Token Efficiency Hypothesis
Investigate whether rule-aware vocabularies can reduce token overhead and invalid generations compared with unconstrained baselines.
Architecture
A grammar controller combines model hidden states with rule embeddings to score legal transitions and expose interpretable traces for each prediction step.
- Derivation-state tokenization: encode symbol, role, active rule, scope, and derivation depth.
- Rule-aware gating: combine learned semantics with explicit legality constraints.
- Traceable output: capture step-level transition traces for analysis and debugging.
Empirical Progress
Early internal benchmarks indicate promising behavior on constrained datasets, including lower invalid-sequence rates in selected tasks. Results are directional and not yet peer-reviewed.
Research Positioning
PanLang is positioned around three practical research goals:
- Improve reliability for rule-heavy scientific sequence tasks.
- Provide interpretable rule traces alongside model output.
- Keep experiments reproducible on accessible hardware tiers.
This page describes an active research track. Public claims are intentionally limited to validated internal observations.