Quantum key distribution (QKD), whose security is derived from quantum mechanics rather than computational assumptions, is moving into standardization and commercial deployment. Yet its security guarantees rest on technically delicate proofs checked by hand. Formal verification closes this gap, establishing a two-facto...
Rui-Xuan Deng, Zheng-Feng Ji, Jia-Yu Zhang et al.· 0 citations
Landmark mathematical formalizations have taken specialist teams years to complete. We present FormalFlow, a system that coordinates AI proving agents under human supervision to address statement drift and proof composition in long-horizon formalization. Drawing on software-engineering principles and practices, it uses...
Si-Rui Lu, Rui-Xuan Deng, Yan-Qiao Zhu et al.· 2 citations
This work completed a machine-checked Lean 4 proof of the quantum soundness of the classical low individual-degree test, a core theorem underlying MIP* = RE, and provides a verified foundation for quantum complexity.
Si-Rui Lu, Rui-Xuan Deng, David Zhu et al.· 1 citation
Pretrained transformers use little of their depth to follow references in context. Thirteen base models reliably follow only 1.4-3.6 lines, and extra pretrained loops add little. A task-trained rank-8 LoRA at one early layer extends this computation with all model weights frozen. Qwen3-8B improves from 15.5% to 99% exa...
Landmark mathematical formalizations have taken specialist teams years to complete. We present FormalFlow, a system that coordinates AI proving agents under human supervision to address statement drift and proof composition in long-horizon formalization. Drawing on software engineering principles and practices, it uses...
Si-Rui Lu, Ruixuan Deng, Yan-Qiao Zhu et al.· 0 citations
AcFlow is introduced, an inference-time controller that transports intermediate layer image-token activations through a learned concept-conditioned velocity field while keeping the base DiT frozen, and supports the learned velocity field as an adaptive control mechanism.
Jun-Ran Wang, Ze-Hao Jin, Tian-Yu Luan et al.· 0 citations
A three-tier diagnostic taxonomy shows that verification/feedback and planning failures dominate execution/grounding errors, while a single scalar success rate can not explain, and connects these findings to newer long-horizon CUA benchmarks and derive stage-specific design rules for CUA evaluation.
Zi-Han Dong, Zhiyuan Ma, Zekun Wang et al.· arXiv.org· 2 citations
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