Return Aurelius's admission policy (accepted vs restricted research domains).

Screen a research topic against the restricted-domain policy before drafting.

Returns a heuristic flag plus the full policy; you make the final accept/reject call.

Return a standard academic outline scaffold (Abstract..References) for a topic.

Compute a section-by-section word-count budget for a target page count.

Use for long-form papers (e.g. 20-80 pages): call this first, then draft and verify section by section, appending each via save_draft(..., append=True).

Save (or, with append=True, append to) a Markdown research draft. Use append=True for long-form papers so you never resend the whole accumulated draft.

Save a fact-checking report (first line 'STATUS: VERIFIED'/'STATUS: REJECTED').

Search the web (Tavily) for evidence about a factual claim or question.

Verify an academic citation against OpenAlex and Crossref (falls back to a web search if the work isn't indexed there). Surfaces retraction status explicitly via is_retracted -- a retracted paper is never quietly treated as verified.

Batch-verify a list of citations and/or factual claims in one call. Produces a scored evidence ledger (verification_score, per-item verdicts + sources) and a save_report-ready Markdown summary with unverified/retracted items struck through.

Verify an entire References/Bibliography block at once. Splits it into individual citations, verifies each (DOI-precise, retraction- and author-aware), and returns a scored ledger plus a cleaned DOI-backed corrected_bibtex and corrected_references.

Verify a numeric/statistical claim (e.g. 'US GDP grew 2.5% in 2023') against World Bank primary data. Pass country/year/claimed_value (and optionally a World Bank indicator code) for precision; falls back to web search if the data source is unavailable. Returns verdict verified/contradicted/unverified with the actual value.

Improve prose style/readability of an ALREADY FACT-CHECKED draft -- never alters citations, numbers, or claims. This is a readability pass, not an AI-detector evasion tool. Default returns guidelines for you to apply yourself; set use_llm=True to have Aurelius rewrite it (needs an LLM key; falls back to guidelines if none is configured).

Return a Mermaid syntax scaffold (flowchart | architecture | sequence) to complete and embed in a ```mermaid fenced block.

Return a compile-ready LaTeX skeleton (Abstract..References) + a BibTeX entry stub. template: "article" (single-column preprint), "twocolumn" (conference/IEEE-style), or "report" (chaptered long-form).

Save LaTeX (.tex) or BibTeX (.bib) source to the Aurelius output directory.

Search the global scholarly literature across languages via OpenAlex (keyless).

languages are ISO 639-1 codes (default: zh, es, de, ja). With an LLM key configured the query is translated per language for better native recall; without one the original query is used (fewer, but still real, results). Use to surface non-English work (Chinese, Spanish, German, Japanese...) that an English-only search misses.

Re-check previously verified citations for retractions or verification drift.

Pass citation strings to re-verify them now, or omit references to scan every saved Proof-of-Rigor bundle and re-check all citations recorded there. Returns alerts for anything newly retracted or no longer verifiable — a paper cited last month can be retracted today.

Recall past Aurelius research sessions relevant to a topic (episodic memory).

Returns up to k past episodes — hypothesis, verification score, methodology risk, and derived lessons — so new research can build on successes and avoid repeating failures. Episodes are recorded automatically by the research graph.

Run the FULL research loop autonomously (screen -> draft -> fact-check -> revise).

Requires an LLM API key with quota in the environment (OPENAI_API_KEY / ANTHROPIC_API_KEY / GOOGLE_API_KEY). Use this only when you want Aurelius to drive its own model instead of the host app's model. May take a few minutes.

Run the multi-stage research DAG (agent swarm) instead of the linear loop.

Chains specialized agents — literature mining, a parallel hypothesis swarm, feasibility screening, experiment design/code generation, a hardened sandbox + static methodology (p-hacking/data-dredging) audit, citation verification (reusing the retraction-aware verifier), adversarial review, drafting, LaTeX, and a signed cryptographic proof-of-rigor attestation (SHA-256 content hash + signature, optional IPFS/on-chain anchoring) — logging every agent action to an audit trail and checkpointing each stage.

Requires an LLM API key with quota for the reasoning agents (OPENAI_API_KEY / ANTHROPIC_API_KEY / GOOGLE_API_KEY); citation verification and proof signing are keyless. Set enable_sandbox=True to actually execute the generated analysis code in a hardened, network-less Docker container (off by default since the code is model-written; requires Docker). check_compliance, publish_preprints, and patent_freedom remain honest placeholders. Optionally pass breakpoints (stage names) to pause for human approval.

Returns {status, session_id, checkpoint, final_state, audit_trail}.