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Beyond Accuracy: How Quantization Affects Coding Agents

Paper recorded by Signals 4 on 2026-11-02 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CL · 自然语言处理 · first seen 2026-11-03

Abstract

Agents are increasingly used for coding tasks, but the inference cost of large language models (LLMs) limits where they can run. Quantization reduces model size, but its effects on coding agents that execute and revise programs remain underexplored. We compare BF16 with 4-bit AWQ and GPTQ for Qwen2.5-Coder-Instruct (7B, 14B, and 32B) and Devstral-Small-2507 (24B) on OJBench coding problems across

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#40 most recent of 628 cs.CL papers we have recorded · ↑ newer: EviCast: Forecasting Retrieval Gains Through Shared Response Calibrati · ↓ older: VisCodeBench: a new benchmark dataset for Text-to-Vis reflecting real-
Cite this page: Beyond Accuracy: How Quantization Affects Coding Agents: the #40 most recent of 628 cs.CL papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/beyond-accuracy-how-quantization-affects-coding-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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