We explore the potential of spatial-photonic Ising machines (SPIMs) to
address computationally intensive Ising problems that employ low-rank and
circulant coupling matrices. Our results indicate that the performance of SPIMs
is critically affected by the rank and precision of the coupling matrices. By
developing and assessing advanced decomposition techniques, we expand the range
of problems SPIMs can solve, overcoming the limitations of traditional
Mattis-type matrices. Our approach accommodates a diverse array of coupling
matrices, including those with inherently low ranks, applicable to complex
NP-complete problems. We explore the practical benefits of low-rank
approximation in optimization tasks, particularly in financial optimization, to
demonstrate the real-world applications of SPIMs. Finally, we evaluate the
computational limitations imposed by SPIM hardware precision and suggest
strategies to optimize the performance of these systems within these
constraints.

通过开发和评估先进的分解技术，我们扩大了空间光子伊辛机 (SPIMs) 所能解决的问题范围，克服了传统 Mattis 型矩阵的局限性。我们探讨了低秩近似在优化任务中的实际收益，特别是在金融优化中，以展示 SPIMs 的实际应用。最后，我们评估了 SPIM 硬件精度所带来的计算限制，并提出优化系统性能的策略。

利用低秩和循环矩阵约束的空间 - 光子伊辛机的高效计算

Efficient Computation Using Spatial-Photonic Ising Machines: Utilizing  Low-Rank and Circulant Matrix Constraints

This paper investigates the ethical implications of aligning Large Language
Models (LLMs) with financial optimization, through the case study of
GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes.
By comparing GreedLlama's performance in moral reasoning tasks to a base Llama2
model, our results highlight a concerning trend: GreedLlama demonstrates a
marked preference for profit over ethical considerations, making morally
appropriate decisions at significantly lower rates than the base model in
scenarios of both low and high moral ambiguity. In low ambiguity situations,
GreedLlama's ethical decisions decreased to 54.4%, compared to the base model's
86.9%, while in high ambiguity contexts, the rate was 47.4% against the base
model's 65.1%. These findings emphasize the risks of single-dimensional value
alignment in LLMs, underscoring the need for integrating broader ethical values
into AI development to ensure decisions are not solely driven by financial
incentives. The study calls for a balanced approach to LLM deployment,
advocating for the incorporation of ethical considerations in models intended
for business applications, particularly in light of the absence of regulatory
oversight.

研究调查了将大型语言模型（LLMs）与金融优化对齐的道德影响，以 GreedLlama 为案例研究，该模型经过精调以优先考虑经济上有益的结果。通过将 GreedLlama 在道德推理任务中的表现与基准的 Llama2 模型进行比较，研究结果显示了一个令人担忧的趋势：在道德含糊度低和高的情况下，GreedLlama 表现出明显偏向于利润而不是道德考虑，在低含糊度情境中，GreedLlama 的道德决策下降至 54.4%，而基准模型为 86.9%；在高含糊度情境中，该比例为 47.4%，而基准模型为 65.1%。这些发现强调了 LLMs 中单一维度价值对齐的风险，强调在 AI 开发中整合更广泛的道德价值以确保决策不仅仅受金融激励驱动的必要性。该研究呼吁在 LLM 的应用中采取平衡的方式，主张在商业应用模型中纳入道德考虑，特别是在监管缺乏的背景下。