Anti-Autoresearch is an open-source tool that audits academic submissions for self-consistency and fabrication, producing a reviewer-ready report by combining an evidence ledger, LLM-proposed findings, and a deterministic adjudicator.
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MIT
2026-07-14
It addresses the growing problem of unreliable machine-generated papers by providing a principled, deterministic audit system that avoids opaque AI-text classification and instead checks factual coherence—backed by the proven ARIS platform (12.5k★) and a published taxonomy of 46 integrity patterns.
天下苦 autoresearch 久矣 —— Anti-Autoresearch 替研究者们一眼看穿不靠谱的工作。
🏆 Built on a battle-tested foundation: ARIS (~12.5k★ · HuggingFace Daily Papers #1 · 78+ skills across 7+ platforms). Anti-Autoresearch points ARIS's production audit DNA (experiment-audit · paper-claim-audit · citation-audit · kill-argument) outward — auditing a third party's submission instead of your own.
Autoresearch has gone mainstream, and a fast-growing share of what reaches the review pile is machine-generated — and a lot of it doesn't hold up: tables that don't match the text, baselines that aren't there, open-sourced code that won't reproduce its own paper. Reviewers, area chairs, and honest authors increasingly need to verify that, not just suspect it.
Regardless of who or what wrote a paper, does the science hold together and reflect its own evidence? Anti-Autoresearch audits a submission for self-consistency and fabrication, and produces a span-anchored, reviewer-ready report. It is not an opaque AI-text classifier (no authorship probabilities, no "AI-written" verdict) and does not judge misconduct — it surfaces discrepancies a human reviewer should investigate. Separately, it lists transparent, itemized AI writing-style impressions in a quarantined, zero-verdict-weight section (a paper can be integrity-
CLEANwhile listing many), because reviewers react to them.
46 integrity patterns across 8 families — the coverage vocabulary every finding cites — plus 13 zero-weight AI writing-style impressions and 2 advisories:
| Family | Catches | |
|---|---|---|
| A | Numeric self-consistency | 数值自洽:table vs text vs delta arithmetic that doesn't add up |
| B | Method & scope | 方法与范围:the described method/scope ≠ wh |
Don't trust an autoresearch paper at face value. Reviewer-side integrity forensics — self-consistency + fabrication checks across 39 hack-patterns (7 families), deterministic verdict. Not an AI-text detector. The dual of ARIS.
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