Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic
  Capabilities in LLM Compression
Authors: name: Peijie Dong, name: Zhenheng Tang, name: Xiang Liu, name: Lujun Li, name: Xiaowen Chu, name: Bo Li

Post-training compression reduces the computational and memory costs of large
language models (LLMs), enabling resource-efficient deployment. However,
existing compression benchmarks only focus on language modeling (e.g.,
perplexity) and natural language understanding tasks (e.g., GLUE accuracy),
ignoring the agentic capabilities - workflow, tool use/function call,
long-context understanding and real-world application. We introduce the Agent
Compression Benchmark (ACBench), the first comprehensive benchmark for
evaluating how compression impacts LLMs' agentic abilities. ACBench spans (1)
12 tasks across 4 capabilities (e.g., WorfBench for workflow generation,
Needle-in-Haystack for long-context retrieval), (2) quantization (GPTQ, AWQ)
and pruning (Wanda, SparseGPT), and (3) 15 models, including small (Gemma-2B),
standard (Qwen2.5 7B-32B), and distilled reasoning LLMs (DeepSeek-R1-Distill).
Our experiments reveal compression tradeoffs: 4-bit quantization preserves
workflow generation and tool use (1%-3% drop) but degrades real-world
application accuracy by 10%-15%. We introduce ERank, Top-k Ranking Correlation
and Energy to systematize analysis. ACBench provides actionable insights for
optimizing LLM compression in agentic scenarios. The code can be found in
https://github.com/pprp/ACBench.