Shanghai Institute of Physical Intelligence and Robotics

Headlines · 3

1. 3d
   PAIR 2026 roundtable: who verifies AI-generated scientific findings?

   PAIR Conference 2026, themed on the frontier of next-generation physical intelligence, was held in Shanghai on Sept 20–21, and its "next-generation scientific discovery" forum closed with a roundtable on how model outputs become trustworthy scientific evidence — cross-disciplinary verification, reproduction and applicability limits. Panelists said AI generates candidates far faster than experiments can check them: the best AI virtual-cell data covers only about 100 perturbed genes out of roughly 20,000 human genes, GNoME's ~2.2 million predicted crystal structures were criticized as largely simple extensions of known compounds, and A-Lab corrected its "new materials" wording after re-analysis of its phase identification. Their proposed principles: fix verification standards before experiments and independently of results, separate the generator from the verifier, use independent labs and cross-platform replication, and require results to be independent, prospective and extrapolative.

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2. 4d
   PAIR 2026 panel maps how AI and quantum computing enable each other

   PAIR Conference 2026, themed "Frontiers of Next-Generation Physical Intelligence," was held in Shanghai on September 20–21, and its next-generation computing paradigms roundtable discussed two-way empowerment between quantum computing and AI. Hongye Hu of the Institute of Physics, Chinese Academy of Sciences, said Chinese teams have achieved automated calibration of 100-plus-qubit chips without continuous human involvement, and cited Google's AI-controlled physical device achieving non-stop quantum operations, while noting reinforcement learning needed 10^5–10^6 samples versus far fewer with physics-informed methods. Anbang Wu of Shanghai Jiao Tong University backed AI for quantum compiling, program verification and error-correction optimization; Zheng Yan of Westlake University said publication growth in strongly correlated quantum many-body work has not been similarly lifted by AI. On the reverse direction, Hu described quantum generative models with a proven unconditional polynomial memory advantage in certain tasks, while Wu said he has not yet seen a new AI paradigm that truly exploits quantum advantages.

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3. Oct 8
   Weinan E: AI for Science's second half is rebuilding the research system

   Speaking at PAIR Conference 2026 in Shanghai, Chinese Academy of Sciences academician Weinan E argued that AI for Science has two layers: as research becomes “industrialized,” it needs new infrastructure — compute, models/algorithms/software, databases and knowledge bases, and automated labs (China has pushed this “platform research” approach since 2022) — while the “second half” is change in the research system itself. He said theory and computation matter alongside automated labs and literature mining: DPA-4 now covers organic molecules, catalysis and complex interfaces, but is still two to three orders of magnitude from materials and process design, and a “science foundation model” built by fine-tuning open general models may be a false premise if the underlying base model is weak. He also called for breaking three boundaries: theory/experiment, between disciplines, and research/industry.

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