下游的优势在于市场想象空间巨大，估值弹性高，一旦实现技术突破，有望获得垄断性收益。但风险远高于上下游：盈利周期极长，预计要到2030年才能实现现金流转正；技术路线失败率高，类似Meta元宇宙投入效果不佳的案例并不少见；且资本依赖性极强，一旦融资环境恶化，将直接冲击企业生存。
The N-closest or N-best dithering algorithm is a straightforward solution to the N-candidate problem. As the name suggests, the set of candidates is given by the closest palette colours to the input pixel. To determine their weights, we simply take the inverse of the distance to the input pixel. This is essentially the inverse distance weighting (IDW) method for multivariate interpolation, also known as Shepard’s method. The following pseudocode sketches out a possible implementation:
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As you might expect, the result of this is that colours which lie closer to the input pixel are given a greater proportion of the total influence with ever-increasing values of . This is not mentioned in the cited paper but it might be nice to consider for your own implementation.
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Initially I aimed to test with at least 10 formulas for each model for SAT/UNSAT, but it turned out to be more expensive than I expected, so I tested ~5 formulas for each case/model. First, I used the openrouter API to automate the process, but I experienced response stops in the middle due to long reasoning process, so I reverted to using the chat interface (I don't if this was a problem from the model provider or if it's an openrouter issue). For this reason I don't have standard outputs for each testing, but I linked to the output for each case I mentioned in results.