aiqbench analysis
This benchmark shifts focus from static model outputs to the dynamic code artifacts—execution loops and tool integrations—that agents actually build themselves. The results reveal a critical generalization gap where only 34 of 64 evolutionary changes improve performance on unseen tasks, highlighting that current LLMs struggle to create robust, reusable agent harnesses without human intervention.
Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize
ByteDance Seed, SUTD, Georgia Tech, M-A-P, and TokenWave.AI introduce HarnessDev, a benchmark that scores the runnable harness a model builds rather than the answer it returns. Starting from a seed that scores 0, 6 creator LLMs construct harnesses across 5 benchmarks and 2,207 tasks, then evolve them from execution feedback. Self-built harnesses match human references on writing and ML experimentation but trail on code and search, and only 34 of 64 evolution changes move the same direction on held-out tasks. The post Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize appeared first on MarkTechPost.
An agent harness is the code around a model: execution loop, tools, context, state, recovery, and verification. Per the Terminal-Bench 2.1 leaderboard, GPT-5 solves 35.2% of tasks inside Terminus 2 but 49.6% inside Codex CLI with identical weights. Most benchmarks keep that harness fixed. HarnessDev proposed by team of researchers from ByteDance Seed, Singapore University of Technology and Design, Georgia Institute of Technology, M-A-P, and TokenWave.AI, flips the target: the artifact under evaluation is the runnable harness the model writes, not…
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