Modern GPUs offer far higher throughput in low precision than in FP64, but exploiting this in conjugate gradient (CG) solvers is difficult because tolerance to reduced precision evolves with the Krylov state. We present EvoCG, a mixed-precision CG solver that treats tile-wise precision as an online control action rather than a fixed setting: a reinforcement-learning policy in the iteration loop selects per-tile precision from the evolving solver state, made profitable by co-design with a Tile-CSC unified mixed-precision SpMV kernel and a low-overhead in-loop runtime. Across 47 SPD matrices from eight scientific domains, EvoCG is the fastest solver overall, with geometric-mean speedups of 2.52x, 3.06x, 2.48x, 1.93x, and 6.99x over cuSPARSE, PETSc, Ginkgo, Mille-FP64, and Mille-Mixed. On the same substrate, it improves time-to-solution over EvoCG-FP64 by 1.28x at only 11.4% more iterations and 1.09% overhead, with FP64-recomputed final true-residual norms nearly identical to those of FP64, and extends to Jacobi-preconditioned CG without retraining, yielding a further 1.15x geometric-mean speedup over EvoCG-FP64.
Mengfan Li Huazhong University of Science and Technology, Xuanhua Shi Huazhong University of Science and Technology, Yang Deng Singapore Management University
Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demonstrate promising performance on standard ToM benchmarks, we observe that they often fail to generalize to complex task-specific scenarios, relying heavily on prompt scaffolding to mimic reasoning. The critical misalignment between the internal knowledge and external behavior raises a fundamental question: Do LLMs truly possess intrinsic cognition, and can they externalize this internal knowledge into stable, high-quality behaviors? To answer this, we introduce CoSToM (Causal-oriented Steering for ToM alignment), a framework that transitions from mechanistic interpretation to active intervention. First, we employ causal tracing to map the internal distribution of ToM features, empirically uncovering the internal layers’ characteristics in encoding fundamental ToM semantics. Building on this insight, we implement a lightweight alignment framework via targeted activation steering within these ToM-critical layers. Experiments demonstrate that CoSToM significantly enhances human-like social reasoning capabilities and downstream dialogue quality.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表