Amap, JD Retail, and Xiaoying Technology share production architectures for marketing ROI evaluation, conversational analytics, and voice agents — all converging on making AI recognize its own epistemic limits rather than just chasing accuracy.
The paper introduces Counterfactual Policy Optimization (CFPO), a training‑time framework that inserts causal consistency constraints into multimodal reinforcement learning, forcing vision‑language models to rely on essential visual evidence and achieving consistent accuracy gains across real‑world and math‑centric benchmarks.
This article curates eight AAAI 2026 papers authored by the Meituan research team, covering verifiable stepwise rewards for LLM reasoning, annealing strategies in large‑scale training, process reward models, competence‑difficulty sampling, high‑fidelity visual text rendering, counterfactual fusion, compress‑then‑rank reranking, and cross‑modal quantization for generative recommendation, with direct PDF links for each work.
