Where practical advantage emerges in quantum dynamics simulation remains a central debate in quantum computing. Claimed advantages can be rapidly narrowed or overturned by stronger classical methods, while progress on both sides can outpace the journal publication cycle. We present QSim-Arena, a research framework in which AI agents combine a closed loop of verification and benchmarking with open exploration. Hamiltonians, initial states and accuracy targets for evolved states or observable expectation values provide explicit specifications for evaluating simulation methods. Agents translate published techniques into executable components, reproduce reported results and benchmark their performance across diverse Hamiltonian families. Competing classical methods are implemented and independently crosschecked, providing credible baselines for end-to-end quantum resource estimates based on rigorous error bounds and empirical calibration. Alongside this closed loop, agents recombine classical simulation techniques and co-design fault-tolerant quantum circuits using knowledge extracted from the literature. Initial applications improve baseline resource estimates through the integration of established techniques. QSim-Arena thus treats quantum advantage assessment as an evolving, reproducible research process that can respond to advances in algorithms, error analysis and hardware.
Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context settings. While token pruning reduces sequence length, prior methods rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. In this work, we observe that tokens evolve toward semantic fixing points, making further processing redundant. To this end, we introduce Delta Attention Selective Halting (DASH), a training-free policy that monitors the layer-wise update dynamics of the self-attention mechanism to selectively halt stabilized tokens. Extensive evaluation confirms that DASH generalizes across language and vision benchmarks, delivering significant prefill speedups while preserving model accuracy and hardware efficiency. Code will be released at https://github.com/verach3n/DASH.git .
cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表