蚂蚁集团 IJCAI 2026 论文盘点：让 AI 学会「随机应变」

TL;DR - A media roundup of Ant Group's four IJCAI-ECAI 2026 accepted papers, framed around a common theme: moving AI from static solvers to systems that adapt to changing environments (data density, distribution shift, search dimensionality, evolving AIGC ecosystems). It matters as a snapshot of how an industrial lab aligns academic output with production constraints like real-time risk control and 1B+ user traffic swings.

• MSRGC-Net (with Chongqing Univ. of Posts & Telecom): training-free multi-scale echo state network reservoirs plus density-adaptive "granular ball" anchor graphs for time-series clustering; no preset cluster count, near-linear complexity instead of O(n²), and 12 best / 2 second-best across 15 metrics on 5 multivariate benchmarks.
• ROAD (with SJTU): treats the offline/online data mixing ratio in offline-to-online RL as a dynamic decision variable via bi-level optimization (inner Bellman error, outer expected online return) solved approximately with multi-armed bandits; plugs into IQL/PEX/CQL/Cal-QL, with PEX+ROAD scoring 71.12 average and 18/24 firsts on D4RL tasks.
• DSEBO (with ECNU and Nanjing Univ.): high-dimensional Bayesian optimization that grows the random-embedding subspace dimension on convergence triggers, using a shared embedding matrix so subspaces nest; adaptive expansion step size keyed to the best-value curve, evaluated on D=1000 synthetic functions and MSLR/Lasso-Hard/LIMO against REMBO, SIRBO, BAxUS, TuRBO.
• VGA-BenchV2 (with Beijing Film Academy and BIGAI): adds 36,000 task-level human annotations (13.46×/11.15×/1.55× over VGA-Bench) and a hybrid evaluator (VAQA-Net for continuous aesthetic scores; VTag-Net/VGQA-Net on Qwen VLMs), then reuses the learned aesthetic evaluator as an RL reward signal to fine-tune generators — closing the loop from scoring to optimization; dataset is open on Hugging Face.