arXiv subjects

Zhongjie Wang

Publications and source records attributed to Zhongjie Wang.

2 recordsLinked to original sources

arXiv2026

Software Engineering in the Agent Era From Trustworthy Change to Human Agent Software Organizations

Software agents make digital execution elastic: repository analysis, code generation, testing, migration, tool use, and operations can be replicated and parallelized without proportional human headcount. Problem framing, semantic commitment, verification, integration, attention, and residual-risk acceptance remain bounded by human cognition, organizational authority, and economic capacity. How should scalable execution be governed so organizations can accept and sustain its changes? Our testable framework has two constructs and one execution abstraction. Trustworthy Change (TC) is the engineering object moving from intent through delegated execution, verification, integration, acceptance, and operation. Responsibility Topology classifies organizations by the distribution of independent residual-risk acceptance authority. A single-center topology has one final baseline responsibility anchor; a multi-anchor topology requires joint acceptance across independently governed domains. The Human-Agent Cell (HAC) produces candidates, proposals, and evidence; execution grants no acceptance authority. As execution and authority scale differently, distributed HACs create context-coherence and invalidation pressures, while multi-anchor governance adds joint acceptance and explicit responsibility closure. Responsibility, accountability, change management, specification, verification, and human oversight predate this work; our claim is only that agent-scaled execution changes how they fit together. We make that authority an explicit classification axis and derive consequences for change state, shared engineering facts, verification, and flow control. Progressive Specification and bounded-capacity analysis remain hypotheses to test, not laws. We contribute theory construction and operationalization; empirical validity remains open to controlled, longitudinal, and field studies.

cs.SE↗

arXiv2026

Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection

Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories incrementally while preserving detection ability on previously learned ones. Existing methods mainly exploit pretrained detectors as a whole, without explicitly distinguishing which components should remain stable and which require plastic adaptation. In this paper, we revisit PTMIOD from a component-wise stability-plasticity perspective. Our analysis of pretrained DETR-based detectors reveals a clear asymmetry: localization heads preserve transferable geometric priors across tasks, whereas classification-related representations require greater plasticity to handle new categories, especially in cross-domain scenarios where downstream data deviate from the pretraining domain. Based on this finding, we propose a selective adaptation and retention framework that freezes explicit localization heads to preserve localization stability, while adapting transformer representations with parameter-efficient fine-tuning and updating classification heads for classification-oriented plasticity. To alleviate classification-side forgetting, we pioneer the use of pseudo-feature replay in PTMIOD and design Quality-aware Gaussian Feature Replay, which estimates reliable class-wise feature distributions from high-quality matched object features and replays sampled pseudo features to maintain old-class decision boundaries. Since continual adaptation can shift the feature space and undermine replayed distributions, we further develop Two-stage Consistent Distillation to align teacher and student representations at both proposal generation and refinement stages.Extensive experiments on COCO, VOC, and TT100K show that our method achieves state-of-the-art performance, demonstrating a favorable balance between old-class retention and new-class adaptation.

cs.CV↗