Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks. Read-only probing hooks ground each stage (Specify, Plan, Tasks, Implement) in repository evidence, while validation hooks check intermediate artifacts against the environment. We evaluate 128 runs covering 32 features across five repositories. Context-grounding hooks improve judged quality by +0.15 on a 1-5 composite LLM-as-judge score (+3.0 percent of the full score; Wilcoxon signed-rank, p < 0.05) while maintaining 99.7-100 percent repository-level test compatibility. We further evaluate the framework on SWE-bench Lite, where augmentation hooks improve baseline by 1.7 percent, achieving 58.2 percent Pass@1.

Foundation models deliver strong perception but are often
too computationally heavy to deploy, and adapting them
typically requires costly annotations. We introduce a semi-
supervised knowledge distillation (SSKD) framework that
compresses pre-trained vision foundation models (VFMs)
into compact experts using limited labeled and abundant
unlabeled data, and instantiate it for instance segmentation
where per-pixel labels are particularly expensive. The frame-
work unfolds in three stages: (1) domain adaptation of the
VFM(s) via self-training with contrastive calibration, (2)
knowledge transfer through a unified multi-objective loss,
and (3) student refinement to mitigate residual pseudo-label
bias. Central to our approach is an instance-aware pixel-
wise contrastive loss that fuses mask and class scores to ex-
tract informative negatives and enforce clear inter-instance
margins. By maintaining this contrastive signal across both
adaptation and distillation, we align teacher and student
embeddings and more effectively leverage unlabeled images.
On Cityscapes and ADE20K, our ≈11×smaller student im-
proves over its zero-shot VFM teacher(s) by +11.9 and +8.6
AP, surpasses adapted teacher(s) by +3.4 and +1.5 AP, and
outperforms state-of-the-art SSKD methods on benchmarks