Pre-Flight
Validator for Research.
Define the institutional standard. ARGUS-Thesis functions as an adversarial pre-flight check. It generates a Technical Governance Report evaluating your manuscript against the structural patterns of high-impact research.
Trusted By
PhD Candidates
Stress-testing defenses
Lab Directors
Standardizing output quality
Grant Writers
Validating core impact
Technical Governance: The system generates a timestamped Audit Artifact documenting the logical stress-test results. This is a computational benchmark, not a peer review replacement.
Calibrated for Researchers at Top Universities
The Adversarial Compiler.
Research is not written; it is forged. ARGUS-Thesis treats your manuscript as code, compiling it against strict logical axioms and novelty requirements.
1. Structural Extraction
Decomposing text into a map of core claims, evidence, and logical connectives.
2. Adversarial Logic Engine
A proprietary multi-model system attacks the argument structure from conflicting perspectives to expose fallacies.
3. Consensus Verification
Only claims that survive the adversarial convergence process are stamped with a Validity Key.
Powered by Google Gemini 2.5
Your thesis is analyzed using Google's most advanced AI. We never store your manuscript in our database—analysis happens in secure, ephemeral sessions and is discarded immediately after.
- No database storage of your manuscript text.
- AI-powered adversarial critique in minutes.
fn verify_signal(input: Signal) -> Result:
let vectors = engine.extract(input);
match engine.adversarial_check(vectors) {
Ok(score) => sign_certificate(score),
Err(e) => reject_with_trace(e),
}
System Constraints
Logical Consistency Only
ARGUS-Thesis verifies internal logic and novelty structure. It does not verify external empirical data correctness vs the real world.
No Authorship
The system is a critic, not a writer. It will never generate manuscript prose, only structural critique.