While Large Language Model (LLM) agents are increasingly applied to automated software repair, misalignment remains in how humans and agents judge issue conditions and how agents’ pre-execution self-assessments relate to repair competence. Human developers rely on diagnostic cues such as reproduction steps and stack traces to judge whether an issue is sufficiently specified, whereas LLM agents often fail to recognize missing information. We present the first systematic empirical study of misalignment between human and agent judgments and between agent judgments and repair performance. Using SWE-bench, controlled ablation experiments establish a causal link between removing human-valued cues and reduced LLM repair success. Specifically, LLM judges show limited agreement with human problem-specification ratings, and agents’ pre-execution self-assessments only weakly track repair degradation when key cues are removed. Our trajectory analysis further reveals distinct behavioral responses to missing information, while post-execution self-judgment signals add useful discriminative information when combined with behavioral traces. These findings reveal a persistent gap between how current repair agents assess issue conditions and how they perform during repair, highlighting the need for judgment-aware and correction-aware agent design.
Mingyue Yuan, Jieshan Chen, Dehai Zhao et al.· ACM Transactions on Software...· 0 citations
Text-to-motion generation must produce motions that are semantically correct, temporally coherent, and physically plausible. A natural approach is to first project motion data into a structured semantic space and then train a generative model within that space. Such a paradigm has been highly successful in image generation through Representation Autoencoders (RAEs), where a frozen self-supervised encoder provides semantic features for diffusion or flow models to learn from. However, direct transfer of such a paradigm to motion space using Motion-JEPA as the frozen encoder fails dramatically. We diagnose this failure geometrically and identify two motion-specific bottlenecks: (1) the JEPA feature space is spectrally ill-conditioned, making the Gaussian-to-data transport unstable; and (2) even with a well-conditioned spectrum, flow residuals tend to align with decoder-sensitive directions, where small latent errors are amplified into large motion artifacts after decoding. Based on these insights, we propose MoRAE. MoRAE addresses the two bottlenecks separately. A compact bottleneck distills the structured JEPA representation while removing weak and redundant directions, bringing the latent spectrum into a transport-stable regime. Motion-coupled training then aligns the retained latent geometry with the decoder, making characteristic flow errors less costly after decoding. With this flow-friendly latent, a standard non-autoregressive Flow-Matching DiT achieves state-of-the-art performance.
Yifei Zhu, Mingyi Shi, Yangyang Cai et al.· 0 citations