Best Practices

CoAutoResearch is Co as much as Auto: the agent does the heavy lifting, but you stay the PI — you bring the context, steer the direction, and own the result. The autonomy is a means; the goal is research you can understand, revise, and defend. These patterns get the most out of that.

Best ways to start

The agent works best when you give it real research context to ground in, not just a one-line prompt. Two starting points work especially well — both are uploaded through the composer + button and filed under your project’s resources/.

Bring a proposal and a deep-research report

If you’ve already shaped an idea with ChatGPT or Claude — a proposal, plus a deep-research report on the landscape — upload both and let CoAutoResearch take it from there. It grounds its trials, evidence, and manuscript in your framing instead of starting cold, so the direction is yours from the first step.

Continue a half-finished project

Have work already in progress? Upload the folder, or a .zip of your ongoing project — drafts, notes, data, prior experiments. CoAutoResearch surfaces what’s there, files it as resources, and continues the research rather than restarting it.

Either way, uploaded material is copied into resources/ and recorded, so the agent can cite it and you can trace where everything came from.

For ongoing projects, put research-bearing material under resources/ongoing_work/ or upload it through the UI. CoAutoResearch now runs a mandatory conversion trial before normal autoresearch when that folder contains code, data, checkpoints, results, manuscript sources, or metrics. The conversion trial inventories what is present, activates large resources in place, migrates runnable code or derived data into workspace/, and registers prior quantitative results as tentative findings until the new run verifies them.

Keep the Critical Path empirical

The loop is driven by the Critical Path in research_trajectory/STATE.md. Each trial should target the current bottleneck: acquire or substitute data, run or repair methods, produce results, build figures/tables, or assemble the deliverable. Audits and register cleanup are useful only when they unblock one of those items.

When a public dataset or file is research-critical, the agent should not leave it as a link-only note. It should record an acquisition decision, try user provided ongoing work and local caches first, then official downloads and acceptable substitutes within the objective. A human decision is reserved for credentials, private resources, scope changes, or substitutions that would change the core claim.

Working with the manuscript blueprint

CoAutoResearch uses two manuscript artifacts. manuscript/PAPER_PLAN.md is the working venue-format plan: section obligations, planned figures/tables, evidence gaps, and blockers live there. manuscript/BLUEPRINT.md is the exportable blueprint: it should contain only real, source-backed content, including real results, figure/table blocks, references, appendix posture, and provenance.

The blueprint is not finished prose. That is deliberate. Today’s models can plan and ground a paper far more reliably than they can write the full narrative with correct, defensible reasoning end to end. So we keep the part that must be right and traceable — the research itself — as the deliverable, and leave the final sentence-level prose to you.

This is the Co in CoAutoResearch: every claim is tied to evidence you can check, and you can revise the argument before a single paragraph is written. You own and can defend the result, instead of inheriting prose you would have to reverse-engineer.

When the paper is complete, hand the Paper-Writing Pack to GPT or Claude and ask it to write the paper from the blueprint. Because the structure, claims, and evidence are already fixed, the model is rendering your research into prose — not inventing it.

If the UI offers a Research Status Pack instead, the run is not ready for paper writing. That export is an honest checkpoint containing PAPER_PLAN.md, state, findings, and blockers.

Generating figures

Figures follow the same split. The blueprint gives a description/spec for each figure rather than treating a generated image as the source of truth. The figure block should say what the display must do, where it belongs in the paper, which evidence or concept it carries, what caption it needs, and what still blocks it from being final.

If you are using the Codex backend and image generation is available, CoAutoResearch can create candidate figure previews from active inline figure specs. Generated previews are saved under manuscript/figures/generated/, and the matching manuscript/BLUEPRINT.md figure block records the source path and Markdown Preview image. Claude Code does not start Codex figure-image jobs.

You can also work manually: copy the figure description into an image model, save the result under manuscript/figures/, and update the figure block with its source path and preview image. Either way, the blueprint remains the auditable source for what the figure must show; the image is a candidate display until you accept it as part of the manuscript.

Staying in the loop

The Co is the point: step in whenever you want to change direction, scope, methods, claims, or target venue. Your interventions become part of the project record, so the research stays yours — steerable while it runs, and defensible after the agent hands off.