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Give a coding agent a paper corpus to find new techniques

Poster gave a Claude Code agent access to about 2M papers to search before each attempt; it found techniques it otherwise wouldn't know and beat a baseline agent.

Done withClaude Code

What they did
The poster ran two identical Claude Code agents on the same job: optimize a small language model. One used only its built-in knowledge; the other was connected to Paper Lantern, an MCP search tool over 2M+ open-source computer science papers, and searched the literature before each attempt, scanning 520 papers, surfacing 25 ideas, and applying them with human-readable guidance.
How it went
The paper-enabled agent improved the model by 4.05% versus 3.67% for the baseline, and the poster says about 15 of the 25 suggested paper-based ideas worked. They also say the better config stayed 12+ minutes ahead on a 2-hour run, though commenters questioned how strong and general the result was.
Worth knowing
The main catch is overhead: the system reads full papers and adds research cost, and the poster says it is meant for areas with active research communities.

Try it yourself with Claude Code

Before each attempt at improving [my project/benchmark], search the paper collection at [path or search tool] for techniques relevant to [problem], and note which paper inspired any idea you try. Then implement and test the idea against the current baseline score of [score]. Keep a log of ideas tried, source papers, and results, and stop after [number] experiments with a summary of what helped.

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Source: Reddit · Apr 9, 2026

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