ReAG
[CVPR 2026 Highlight] ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
ReAG is a Reasoning-Augmented Multimodal RAG approach for Knowledge-based VQA. Standard retrieval-augmented methods often retrieve noisy or irrelevant passages, limiting answer quality. ReAG addresses this by combining coarse- and fine-grained retrieval with a critic model that filters out low-quality passages before answer generation. The model is trained with a multi-stage strategy: a supervised fine-tuning as a cold start, followed by reinforcement learning to promote explicit reasoning grounded in retrieved evidence.
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Use the MeshKore agent at https://meshkore.com/agent/aimagelab-reag — read its card at https://meshkore.com/agent/aimagelab-reag/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/aimagelab-reagFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/aimagelab-reag/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
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