Xiangyue Ma | Search arXiv

arXiv2026

DeepGroundRepair: From API-Grounded Generation to Shape-Aware Repair for Deep Learning Programs

Deep learning programs are fundamental to modern AI applications, and automatically generating them from natural language requirements can substantially reduce DL development effort. Large language models have demonstrated promising capabilities for this direction. However, generated DL programs often remain unreliable because they must satisfy domain specific API usage constraints and tensor shape dependencies, violations of which can cause API level syntactic issues and runtime failures. Existing retrieval augmented generation and feedback guided code repair methods do not adequately address these DL specific constraints. In this paper, we propose DeepGroundRepair, a three stage framework that establishes a generation localization repair workflow to improve the reliability of LLM-generated DL programs. Its key idea is to progressively narrow the generation and repair spaces using DL specific knowledge and runtime tensor information. Specifically, it grounds generation in task-relevant API knowledge, localizes the upstream causes of tensor shape mismatch failures, and uses runtime tensor shapes to guide parameter repair. We evaluate DeepGroundRepair on the 100 model-level DL programming tasks in DeepEval using six open-weight LLMs across TensorFlow, PyTorch, and PaddlePaddle. We compare it with four baselines: two generation-only methods and their counterparts incorporating traceback guided self debugging. The results show that, relative to these baselines, DeepGroundRepair reduces the combined number of API-level syntactic issues by an average of 5.4 per 10 program files and achieves an average relative improvement of 85.3% in code executability. Compared with the two self debugging baselines, it improves semantic consistency by an average of 18.1% and reduces token cost by an average of 28.4%.

cs.SE↗

arXiv2026

What Must Replay Preserve? Separating Correctable Bias from Class Correspondence

Class-incremental learning must recognize all classes seen so far without task labels. Logit replay methods such as DER and DER++ mitigate forgetting by matching the model's past predictions on stored examples. Deleting this matching reveals its benefit, but the resulting accuracy cost cannot show whether the stored scores themselves are needed, or whether the cost survives correction of the classifier's bias toward recent classes. We propose a diagnostic framework that treats a cached prediction as temporally heterogeneous supervision: it separates classes known when an example was stored from classes learned afterward, edits each group, and evaluates every model before and after a task-level offset that leaves within-task predictions unchanged. On CIFAR-100 with DER++, suitable fixed constants replace the unrefreshed stored scores of later-learned classes within an equivalence margin of 1 percentage point, and the offset reduces the cost of deleting their matching from 14.9 to 1.8 points. Reassigning the non-gold scores of classes known at storage, which preserves their values and each task's target probability, costs 4.3 points before and 4.0 after the offset, and a parallel cost persists in image distillation. In the tested fixed-head setting, the large cost of deleting later-class matching is thus mostly correctable by this offset, whereas the smaller cost of disrupting class correspondence persists. Code and data are available at anonymous.4open.science/r/replay-preserve-E22B.

cs.LG↗