← 研踪 PaperTrailarXiv:2610.33958 · v2
How Much Do Structural Tools Help Small Coding Agents? A Pre-Registered Scale Study of Code Localization
首次提交 2026-10-25 08:16
版本更新 2026-11-09 21:25
雷达观测 2026-11-10 15:22
摘要
Code-graph and embedding tools are routinely added to LLM coding agents on the premise that they supply repository structure the model lacks. We report a pre-registered study of how much such tools help off-the-shelf agents localize the code an issue requires changing and how the benefit varies with model size. Nine open-weight models (Qwen3 0.6B to 32B, Gemma 4 E2B to 12B) run one fixed localization harness on 500 held-out SWE-bench tasks under four tool sets (grep only; plus embedding search; plus code-graph tools; both) and a placebo arm whose structural tools return random entities, with five sampled seeds per condition: 170,500 episodes under hypotheses, tests and claim criteria frozen before the runs. The tools raise canonical function Acc@10 by +0.06 to +0.10 for the Qwen3 models up to 14B and the two small Gemma 4 models, by +0.03 at 32B (a near miss after correction), and not at all for Gemma 4 12B, which rarely submits; the gain is larger for small models than for the largest model in both families. The placebo shows that the gain is caused by what the tools return rather than by having them, and that random tool output harms two small models. The pre-registered claim that a 4B model with tools matches a 32B model without them fails. Secondary analyses show that the embedding index, not the code graph, carries the gain; that the gain replicates on tasks created after every model's release and survives a shell harness, thinking mode, FP8 serving and a file-level metric that output formatting cannot inflate; and that it is mostly exposure: the tools show the gold code more often. Injecting the gold code directly explains the size trend. A 32B model already finds the code without the tools, so they add little exposure, while small models act on a shown answer less often (conversion 0.09, 0.24 and 0.40 at 1.7B, 4B and 32B). We release registration, code, episodes and analyses.
作者备注：29 pages, 5 figures. Depth labels and dataset card and full code, raw results and analysis will be released in the near future
观测到的版本
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v2 · 2026-11-09 21:25 · 最新观测版本
How Much Do Structural Tools Help Small Coding Agents? A Pre-Registered Scale Study of Code Localization
Code-graph and embedding tools are routinely added to LLM coding agents on the premise that they supply repository structure the model lacks. We report a pre-registered study of how much such tools help off-the-shelf agents localize the code an issue requires changing and how the benefit varies with model size. Nine open-weight models (Qwen3 0.6B to 32B, Gemma 4 E2B to 12B) run one fixed localization harness on 500 held-out SWE-bench tasks under four tool sets (grep only; plus embedding search; plus code-graph tools; both) and a placebo arm whose structural tools return random entities, with five sampled seeds per condition: 170,500 episodes under hypotheses, tests and claim criteria frozen before the runs. The tools raise canonical function Acc@10 by +0.06 to +0.10 for the Qwen3 models up to 14B and the two small Gemma 4 models, by +0.03 at 32B (a near miss after correction), and not at all for Gemma 4 12B, which rarely submits; the gain is larger for small models than for the largest model in both families. The placebo shows that the gain is caused by what the tools return rather than by having them, and that random tool output harms two small models. The pre-registered claim that a 4B model with tools matches a 32B model without them fails. Secondary analyses show that the embedding index, not the code graph, carries the gain; that the gain replicates on tasks created after every model's release and survives a shell harness, thinking mode, FP8 serving and a file-level metric that output formatting cannot inflate; and that it is mostly exposure: the tools show the gold code more often. Injecting the gold code directly explains the size trend. A 32B model already finds the code without the tools, so they add little exposure, while small models act on a shown answer less often (conversion 0.09, 0.24 and 0.40 at 1.7B, 4B and 32B). We release registration, code, episodes and analyses.
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