Okmain: Perceptual Color Extraction Using Oklab and K-Means
240
Okmain is a color extraction library that uses Oklab clustering and spatial weighting to find the most visually representative colors in an image.
Claude Opus 4.6 Solves Knuth's Hamiltonian Cycle Problem for Odd m
837
Don Knuth details how Claude Opus 4.6 successfully solved a difficult graph theory conjecture for odd m through iterative algorithmic discovery and creative deduction.
The Agentic Breakthrough: How Modern LLMs Mastered High-Performance Coding
Modern AI agents have become highly effective at generating and optimizing complex, high-performance software when guided by expert oversight and strict behavioral constraints.
Maximizing Vocabulary Coverage with Books: A Submodular, NP-Hard Selection Problem
Maximizing vocabulary coverage across multiple books is NP-hard, so use greedy submodular optimization for efficient, high-quality approximations.
Cooley–Tukey: Turning the DFT into O(N log N) (and Why FFT ≠ DFT)
Cooley–Tukey factorizes and reindexes the DFT to turn O(N^2) work into O(N log N), forming the backbone of practical FFTs while clarifying that FFT = algorithm, DFT = result.
Procedural Islands III: Multi-Scale Noise and Topological Mountains for Final Elevation
121
Multi-scale noise plus a topological mountain distance field, blended smartly, produces realistic island elevation ready for hydrology.
Hard Leetcode, Easy with Constraint Solvers
679
Use constraint solvers to turn tricky interview algorithms into simple, robust models that are easy to extend.
Analog 3D-Optical Fixed-Point Computing for AI and Optimization
101
An analog, 3D-optical fixed-point computer co-designed with iterative models accelerates both AI inference and real-world optimization with high robustness and projected 100× energy-efficiency gains over GPUs.
Sharing Early Diffusion Steps Across Similar Prompts for Efficient Text-to-Image Generation
Share early diffusion steps across similar prompts to generate image sets faster and better, without retraining.
Bandit-Based, Budget-Aware LLM Routing with Preference-Informed LinUCB (PILOT)
206
Treat LLM routing as a contextual bandit and use a preference-informed LinUCB plus a knapsack budget policy to adaptively, cost-effectively pick the right model per query.