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The research introduces a structured framework for measuring Claude Code's real-world usage and task outcomes, providing a basis for tracking how the tool's impact evolves as adoption grows.
Because any single harness component can move benchmark scores by margins comparable to those between adjacent model generations, end-to-end scores can misattribute performance gains and mislead practitioners trying to improve agentic systems.
The benchmark reveals that frontier coding agents can reliably execute computational social science workflows, while also exposing prompt-framing vulnerabilities that could introduce bias into AI-assisted scientific production.
Coding agents using Paper Lantern can retrieve and apply specific, peer-reviewed ML techniques — including hyperparameters and failure modes — that web search alone misses, directly improving the quality of agentic research and training runs.
Teams deploying AI agents for autonomous research should treat ASMR-Bench as a concrete stress-test for their auditing pipelines, since even the best current LLM auditor catches fewer than half of targeted code sabotages.