Our approach
Reliable verification should help research move faster, not add another queue. We aim to reduce repeated manual checking by testing claims against evidence and identifying which results can advance, which need repair and which remain unresolved.
Our research investigations ground the approach below. We are extending it toward continuous verification through an API.
Evidence retrieval and claim-evidence mapping
Connect a statement to the relevant literature, data and assumptions. Check whether the source supports the claim, whether stronger counterevidence exists, and what remains unresolved.
Implementation fidelity and executable checks
Examine the method, code and comparisons together. Look for missing controls, data leakage, mismatched evaluation budgets and conclusions that go beyond the measurements.
Adaptive process verification
A tool call, an analytical step and a final conclusion need different checks. The planned system will route routine questions to fast checks and consequential ambiguity to deeper analysis.
Failure localization and dependency analysis
Identify the unsupported transition and trace which later conclusions rely on it. Distinguish a plausible failure explanation from a cause established by testing.
Counterfactual replay and selective backtracking
Where replay is feasible, compare a corrected branch with the original and test whether the repair helps. Recommend a targeted revisit rather than automatically restarting the entire investigation.
Calibration and verifier robustness
Test false alarms, missed errors and misleading reward signals. Measure verification cost and review time alongside correctness. A faster workflow is useful only if important errors are not slipping through.
Proposed API contract
Checks work with observable research artifacts, not hidden model reasoning.
The research system retains control over tool execution. A recommendation to backtrack does not undo an external action.
Validated outcomes could improve check selection, verification depth and recovery policies. This is a development direction, not a claim that a trained proprietary verifier already exists.
Grounded in research investigations
In the CellDuality paper by Yuhang Chen and colleagues, one baseline appears as 94.83% in Table 2 and 95.83% in Table 6. Both report 96.34% for the method: an implied improvement of 1.51 or 0.51 percentage points.
Our next step: reconcile the experiment records before relying on the size of the gain.
Read the finding in contextOur starting point is AI, data science and biotech research.