You are asked to deep-dive (“resume grilling”) on a Vision-Language Model (VLM) project listed on your resume. Cover the following clearly and concretely: 1. **Problem & scope** - What task(s) did the VLM solve (e.g., captioning, VQA, retrieval, grounding, OCR+reasoning)? - What was the success criterion (offline metrics and/or product metric)? 2. **Model architecture** - High-level structure (vision encoder, language model, fusion mechanism). - Where fusion happens (early/late; cross-attention; adapters; projection layers). - What was frozen vs trainable. 3. **Data & distribution** - What datasets you used (public and/or internal). - Label types (pairs, dialogs, preferences, bboxes, masks). - Data distribution and known biases (domains, languages, image types, long-tail). - Train/val/test split strategy and leakage prevention. 4. **Training recipe** - Objective(s): contrastive, next-token prediction, instruction tuning, RLHF/DPO, multi-task. - Pretraining vs finetuning stages. - Key hyperparameters and infrastructure (batching, mixed precision, sequence length, curriculum). - Evaluation: what benchmarks, ablations, and error analysis. 5. **End-to-end vs modular** - Was it trained end-to-end? If not, what parts were fixed and why? - Trade-offs: stability, compute, data needs, and ability to adapt. 6. **Reasoning time / latency** - Where inference time is spent (vision encoder, KV-cache, decoding). - Throughput/latency numbers and how you measured them. - Optimizations tried (quantization, speculative decoding, caching, batching). 7. **Limitations & improvements** - Known failure modes (hallucination, OCR errors, spatial reasoning, counting, bias, adversarial images). - Concrete proposals to improve (data, architecture, training, evaluation, serving). Answer as if in an onsite: concise, technical, and with specific examples and numbers where possible.