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What you can do with Effective Auto Research EAR

EAR is an open Python research workbench that helps with idea discovery, autonomous mathematical research, and evidence-linked reviewer responses.

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liyingze07-svg/EffectiveAutoResearch-EAR- on GitHub

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What do you want to do?

4 use cases

  • Explore the EAR demo in a local browser studio

    Run the offline studio to see idea trees, proof checks and rebuttal traces with no API key.

    View prompt
    Clone the repo liyingze07-svg/EffectiveAutoResearch-EAR- into <target-folder>, then run `python3 -m ear demo check` from inside it. Report the output of the check, and state whether it validated the idea trace and synthetic rebuttal wiring. Do not claim any live model result, since the demo uses authored examples.

  • Turn a research direction into a ranked proposal

    Use AutoVibeIdea to map literature, rank candidate ideas and draft a proposal.

    View prompt
    In the repo liyingze07-svg/EffectiveAutoResearch-EAR-, follow docs/getting-started.md to run AutoVibeIdea on the research direction <research-direction>. Expected output is a literature landscape, ranked candidates and a proposal draft in its own workspace. Verify that each candidate's novelty judgment cites retrieved prior work before you rank it highest.

  • Inspect saved proof artifacts from the offline math run

    Run the offline math fixture to see proof checks, rejection and saved manuscript files.

    View prompt
    In the repo liyingze07-svg/EffectiveAutoResearch-EAR-, run `python3 -m ear math run --offline --workspace workspaces/math-demo --episodes 1` and then `python3 -m ear math status --workspace workspaces/math-demo`. Report the status output and the saved artifact list. Verify that the run is labeled offline_demo and accepted=false before saying anything about its contents.

  • Draft evidence-linked replies to reviewer comments

    Use AutoRebuttal to tie each reviewer concern to paper evidence before writing replies.

    View prompt
    In the repo liyingze07-svg/EffectiveAutoResearch-EAR-, follow docs/getting-started.md to prepare a rebuttal for the paper <paper-path> and reviewer comments <comments-path>. Expected output is evidence-linked replies, an AC comment and an unresolved-concern ledger as separate files. Verify that every reply cites a specific passage or result in the paper before you report it as supported.

How to use Effective Auto Research EAR

  1. Check you have what it needs: Python 3.10 or newer; git, to clone the repository; For live research only: an authenticated Codex or Claude setup and the module dependencies described in docs/getting-started.md.
  2. Open liyingze07-svg/EffectiveAutoResearch-EAR- on GitHub and read the README and license.
  3. Install Python 3.10 or newer.
  4. Run git clone https://github.com/liyingze07-svg/EffectiveAutoResearch-EAR-.git EAR
  5. Run cd EAR
  6. Run python3 -m ear demo studio

These steps come from our top use case, Explore the EAR demo in a local browser studio, which also has a copy-paste agent prompt.

Effective Auto Research EAR alternatives · Best AI repos for Research

About this repo

Requirements

  • Python 3.10 or newer
  • git, to clone the repository
  • For live research only: an authenticated Codex or Claude setup and the module dependencies described in docs/getting-started.md
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Questions about Effective Auto Research EAR

What is Effective Auto Research EAR?

EAR is an open Python research workbench that helps with idea discovery, autonomous mathematical research, and evidence-linked reviewer responses. It is an open-source project on GitHub: https://github.com/liyingze07-svg/EffectiveAutoResearch-EAR-.

Who is Effective Auto Research EAR for?

Researchers and AI agents who need to test a research direction, work on a math question, or draft rebuttals to reviewer comments.

What can I do with Effective Auto Research EAR?

4 use cases so far, for example: Explore the EAR demo in a local browser studio; Turn a research direction into a ranked proposal; Inspect saved proof artifacts from the offline math run. Each has steps and a copy-paste agent prompt.

What do I need to run Effective Auto Research EAR?

Python 3.10 or newer; git, to clone the repository; For live research only: an authenticated Codex or Claude setup and the module dependencies described in docs/getting-started.md.

What are alternatives to Effective Auto Research EAR?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is Effective Auto Research EAR safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.