anti-autoresearch

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End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) → fan out the integrity auditor skills (consistency, citation, baseline, experiment, presentation, proof-derivation, eval-design — each reads the ledger, emits span-anchored findings) + the zero-verdict-weight AIS writing-style track → advisory memos (/adversarial-case-builder + /novelty-duplication-advisory, no verdict weight) → deterministic tools/adjudicate_findings.py (--ledger REQUIRED) → reviewer-ready Integrity Forensics Report. Cross-model (fresh codex per dimension) and reviewer≠adjudicator: the model proposes findings, the deterministic adjudicator decides the verdict. Observability-aware, detect-only, never an opaque AI-text classifier (a separate zero-weight AIS section lists AI writing

AI & Automation 153 stars 8 forks Updated 2 days ago MIT

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# /anti-autoresearch — the orchestrator Run a full substantive-integrity forensic pass on **$ARGUMENTS** and hand a human reviewer / area chair an evidence-first Integrity Forensics Report. > 🔒 **External cadence: the verdict is not a poll.** The pipeline is the > verdict-producing path — its output changes only when the **paper / repo / ledger** > changes, not with the clock. **Do not** wrap `/anti-autoresearch` (or any auditor > sub-skill) in `/loop` / `/schedule` / `CronCreate` to "re-check". A heartbeat may > only *wait on* the **external** steps that precede the verdict (an arXiv download, a > citation web lookup) — never re-fire the adjudicated verdict, and never "decide the > paper is fine now." Re-run the sweep when the inputs change; that is the only honest > trigger. > 🛡️ **The dual of ARIS.** ARIS ships an internal audit stack so its *own* autoresearch > output stays honest; Anti-Autoresearch is that same audit DNA **pointed outward** at a > third party's submission. This is decision **support** for a human — it surfaces > span-anchored discrepancies to investigate. It is **not** an AI-text detector and it > does **not** judge misconduct (`DESIGN.md` §1; `references/`). ## Why this exists A machine-driven research pipeline (or rushed human) writes the abstract, the tables, the method section, the bibliography, and the appendix in separate passes and never reconciles them. The result is a paper that disagrees with itself, cites papers that do not exist or arg...

Details

Author
wanshuiyin
Repository
wanshuiyin/Anti-Autoresearch
Created
2 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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integrity-forensics

Run the Anti-Autoresearch integrity-forensics sweep (span-anchored evidence ledger → GPT auditors propose findings → a rules-only reporter that lists every proposal with what the auditor said about it) against a paper via a SHA-pinned thin launcher — then convert the verdict into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger. Use when user says "integrity forensics", "forensic audit this paper", "投稿前自查诚信", "审这篇论文的诚信", or says "anti-autoresearch" when the upstream repo's own skills are not installed. Also invoked by /paper-writing (submission self-forensics, default ON), /peer-review (forensic appendix), /resubmit-pipeline.

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consistency-audit

Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: "consistency audit", "check the paper against itself", "self-consistency", "内部自洽".

153 Updated 2 days ago
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experiment-forensics

Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the res

153 Updated 2 days ago
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