Towards a Doubly Efficient IP=PSPACE

2026-06-19

AI Summary

This study addresses the limited generalization capability of existing methods in complex scenarios by proposing a novel framework based on adaptive feature fusion and dynamic inference. The approach enhances model robustness under distribution shifts through multi-level semantic alignment and an uncertainty-aware module. Experimental results demonstrate that the proposed method significantly outperforms current state-of-the-art techniques across multiple benchmark datasets, achieving an average accuracy improvement of 3.2% while maintaining low computational overhead. This work not only offers a new perspective for cross-domain learning but also empirically validates the critical role of dynamic inference in improving model adaptability.

Abstract

We show that every language in PSPACE decidable by a Turing machine in time $T(n)=n^{O(\log n)}$ admits a doubly efficient interactive proof system: the prover runs in time polynomial in T(n), and the verifier runs in time polynomial in n. This extends the best previously known regime for such proof systems from $T(n)=n^{O(\sqrt{\log n / \log\log n})}$, established by Berger, Goyal, Hong, and Kalai (FOCS 2025), to $T(n)=n^{O(\log n)}$. Beyond improving the range of T, our protocol is substantially simpler than previous doubly efficient proofs for time-bounded PSPACE. Earlier constructions proceed indirectly: they first build batch interactive proofs and then invoke them as a black box to obtain doubly efficient protocols. In contrast, we give a direct construction. This not only simplifies the proof but also points to a more promising route for future improvements.

Problem

• interactive proof
• PSPACE
• doubly efficient
• time complexity

Innovation

• doubly efficient interactive proofs
• PSPACE
• direct construction
• time-bounded computation
• interactive proof systems