Abstract
A system for token prediction using grammar estimation includes a sequence intake component configured to receive a token sequence and organize the token sequence into ordered progression units retaining positional and temporal structure. A context conditioning component associates the token sequence with contextual dimensions comprising user-specific interaction patterns, session-local behavior, and environmental state. A grammar estimation component maintains a state-transition graph comprising states representing progression conditions inferred from observed token behavior and transitions accumulating evidence of how token sequences evolve. A state merge component computes a merge score for state pairs based on token co-occurrence frequency, similarity of transition neighborhoods, downstream continuation alignment, and temporal proximity, and consolidates states when the merge score exceeds a threshold. A prediction frontier selector component derives a next-token region from the state-transition graph and restricts prediction to candidates supported by an active grammar frontier. A feedback adaptation component observes prediction outcomes and refines transition strengths, merge sensitivity, and contextual weighting.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mohan, Shivam, "TOKEN PREDICTION USING GRAMMAR ESTIMATION", Technical Disclosure Commons, (August 26, 2026)
https://www.tdcommons.org/dpubs_series/11488