

















Abstract:Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by feedback-conditioned planning across generations. However, how planning decisions attribute and combine heterogeneous feedback signals remains opaque. Standard end-to-end ablations fail to resolve this question, as iterative planning amplifies early perturbations and conflates feedback effects with trajectory-dependent drift.
We introduce \texttt{CUDAnalyst}, a unified analysis layer for controlled, generation-level attribution of planning decisions to feedback components via trajectory freezing and selective feedback injection. \texttt{CUDAnalyst} enables stable generation-level evaluation and principled coalitional-style attribution of feedback effects and interactions. Our results show that explicit planning is beneficial only when feedback is aligned, that effective planning emerges from structured multi-feedback interactions, and that high-level plans from stronger reasoning models can partially transfer to weaker ones. These trends hold across reference backbones, representative workloads, and reference induction regimes, indicating that the identified feedback-to-plan structure is robust within the controlled axes studied.
| Comments: | ICML 2026 accpeted, camera-ready in progress |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26720 [cs.AI] |
| (or arXiv:2605.26720v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26720 arXiv-issued DOI via DataCite (pending registration) |
From: Yee Hin Chong [view email]
[v1]
Tue, 26 May 2026 09:00:09 UTC (5,089 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。