CompressionBench evaluates whether coding agents can autonomously compress LLMs

approach breaks down The paper identifies three failure modes that matter for production use: representation design (how agents encode compression strategies), experimental judgment (interpreting partial feedback and deciding what to try next), and capability validation (relying on likelihood metrics that don’t capture downstream degradation). These are not benchmark artifacts — they mirror the same gaps teams hit when automating model optimization pipelines. For builders considering agent-driven compression: the benchmark proves feasibility at small scale, but the validation gap means you still need human-in-the-loop verification before shipping compressed models. The experimental judgment deficit suggests agents today are better at executing a known recipe than at designing one from scratch.Read full article