Research
CompressionBench: Benchmarking Coding Agents for Autonomous LLM Compression
CompressionBench evaluates whether coding agents can autonomously compress pretrained LLMs within storage and time budgets. Across six agents and six model sizes, 29 of 36 submissions passed verification, achieving a 3.4% perplexity increase over BF16 and 77.33% downstream accuracy. The study highlights representation design, experimental judgment, and capability validation as key factors in agent-driven compression.
LLM compressionautonomous agentsrecursive self-improvement