A deterministic verifier for grammar, path maps, and high-stakes output.
The corpus argues for one thing repeatedly: content should not merely sound correct. It should be reduced into an explicit rulebook, then checked against that rulebook with no hidden leaps.
Core thesis
The research is about turning text into a rulebook, then using that rulebook to verify target content deterministically.
Grammar generation first
The corpus is centered on extracting a deterministic grammar or rulebook from expert text, not just classifying content.
Verification over scoring
The verifier must explain exactly which rule broke, which assumption was unsupported, and which step became invalid.
Path maps as one use case
Path maps are useful because they make feasibility testable, but the verifier is broader than planning alone.
Grammar pipeline
What the corpus says the system must do
- 1
Ingest a specialized corpus, such as physics, planning traces, or domain procedures.
- 2
Extract entities, prerequisites, relations, and conditional rules into a grammar file.
- 3
Apply the grammar deterministically to target content and record the exact violation path.
- 4
Return structured output: errors, warnings, assumptions, and missing bridges.
Diagnostic output
Error
OutputA rule is directly violated or a claim contradicts the corpus grammar.
Warning
OutputThe statement is plausible, but a term or dependency is ambiguous or underspecified.
Assumption
OutputA law or rule is used without a clue in the prompt that justifies it.
Omission
OutputA necessary bridge, constraint, or step is missing from the reasoning chain.
Application domains
Physics validation
A solution can only use conservation laws when the prompt supplies the exact physical clue that activates them.
Behavioral path maps
A planned path can be tested against prior habits, current commitments, and realistic state transitions.
Human and model text
The verifier is meant to analyze any target content, not just model output, so the method stays general-purpose.
Verification target
The verifier should explain what is wrong, not just that something is wrong.
The corpus repeatedly stresses output categories like errors, warnings, assumptions, missing bridges, and structural inconsistencies. That makes the system useful for both model output and human-authored content, because the feedback is deterministic and traceable.
Research direction
Build the grammar generation layer carefully, then make the verifier strict enough to flag unsupported assumptions in physics problems, planning prompts, and other high-stakes content.