"Vyapai team on AMD AI League Hackathon"

Veriweb helps researchers compare information across the web without losing the evidence behind an answer. A user enters a research question, and Veriweb breaks it into focused searches, extracts full article text, and combines BM25 keyword ranking with vector embeddings to retain relevant passages. The report presents requested comparisons with clickable citations that open the exact supporting quotation beside the answer. Its conflict matrix pairs differing figures with their original passages, helping users distinguish genuine disagreements from differences in dates, units, or scope. Missing evidence remains visible, and unsupported questions can produce an explicit abstention. Citations establish provenance rather than automatically certifying a source as correct. Built and demonstrated on an AMD Ryzen 5 5600H and Radeon RX 6500M with 4GB VRAM, Veriweb runs quantized Qwen2.5-3B locally through Vulkan. Standalone generation measured 36.7 tokens per second. ROCm PyTorch computation was verified separately. The project includes a Next.js research dashboard, FastAPI backend, attributed Markdown export, and a bounded CLI that writes structured JSON. All 40 controlled synthetic research cases passed in the latest complete evaluation. These results are regression evidence, not a claim of universal live-web accuracy.