Research & Knowledge
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Make
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.
Time saved
Not reported
Setup time
Not reported
Running cost
Not reported
Cost range
Not reported
Use case overview
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.
Reported experience
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.
Example prompts
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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