Matched comparisons.
Use the same base model, task set and available tools. Compare a strong baseline with the checking layer enabled, disabled and simplified. Declare retry limits and budgets.
IGLR · Invariant-Gated Long Reasoners
Experimental researchWhen the reasoning changes,
what must remain true?
The interesting question is not how much an AI can say. It is what an AI system can be required to preserve.
The research question
Preserve what matters.
Detect what conflicts.
Know when to stop.
These are design goals, not reported benchmark results. IGLR explores explicit constraints, bounded revision and escalation around reasoning workflows.
A named rule is not proof that the rule holds. The checking mechanism must itself be tested, and its limits must remain visible.
Concept visual · not a live test
Proposed evaluation
Cinema at the entrance.
Evidence behind the door.
A controlled study should show what changes when checking is enabled—not assume that a dramatic demonstration establishes superiority.
Score the trade-offs.
Measure correct completions, accepted incorrect outputs, appropriate and unnecessary abstentions, time and cost. Include cases where the checker fails.
Record the conditions.
Keep the task, constraints, implementation version, configuration, attempts and outcomes. Separate observed tool actions and checks from claims about private model reasoning.
What is not established.
This page reports a research direction, not a completed controlled evaluation. It does not claim that IGLR solves all reasoning problems, never hallucinates or outperforms every language model.