Generalize to any research problem

[amrit110]

I think Karpathy's idea can be extended to other research problems beyond just LLM training.

I've implemented a tool to create such autonomous research loop repositories, with support for any agent! https://github.com/VectorInstitute/helix.

The git history is the research trail, and the best part is that improvements can be verified by anyone. I've created a repo with examples reproducing Karpathy's example as well as another that looks at inference TPS as the metric to optimize https://github.com/VectorInstitute/helix-examples.

[connerlambden]

Generalizing to other research domains is a great direction. One thing that could help when extending beyond ML/CS is giving the agent access to domain-specific literature during the research loop.

BGPT MCP provides structured experimental data from scientific papers — methods, results, sample sizes, statistical analyses extracted from full text. It works as an MCP server so it plugs into any agent framework:

{"mcpServers": {"bgpt": {"command": "npx", "args": ["-y", "bgpt-mcp"]}}}

For domains like biology or chemistry where experiments are harder to iterate on computationally, having structured access to existing experimental results could help the agent make more informed design decisions. 50 free searches, no key needed.

[GreyforgeLabs]

Greyforge take: changing the objective does not automatically generalize the architecture. It mostly generalizes the inner loop.

Once you leave a narrow sandbox, the hard problems become instruction authority, eval isolation, review surfaces, bounded execution, and artifact discipline. That is why we do not think autoresearch itself is the interesting layer, even when the loop ports cleanly to other domains.

We wrote that argument up more bluntly here: #501

[alexadamus77-ui]

Expanding the autoresearch pattern to any domain is exactly the direction the field needs! 🚀 The idea of using Git history as a verifiable research trail is brilliant. For these loops to be truly effective, integrating a grounding layer via ScholarAPI could be a game-changer. It provides the structured JSON from Google Scholar that agents need to verify their 'research trail' against peer-reviewed data programmatically. Great work on Helix.

[amrit110]

@alexadamus77-ui thanks! Yes, I've had the idea to ground it against knowledge bases and something like scholar API. Thanks for the pointer and kind words.