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Discusses a 2014 paper that refutes the 3SUM conjecture by presenting subquadratic algorithms for the 3SUM problem, with implications for computational geometry and graph algorithms.
Miami-based startup Subquadratic claims its new SubQ model solves the quadratic attention bottleneck, making LLMs faster and cheaper. Independent tests from Appen back up many of the claims, though skepticism remains.
Subquadratic AI introduces SubQ-1.1-Small, a model leveraging Smart Sparse Attention to achieve near-perfect long-context retrieval up to 12M tokens with up to 1,000x attention compute reduction. It balances long-context optimization with strong general reasoning, outperforming baselines on benchmarks like NIAH and RULER.
SubQ claimed a breakthrough model with a 12M context window and 95% cost reduction vs Opus, but after promising a paper and model card, they have not delivered, raising strong skepticism of a scam or shady behavior.
Key-Value Means (KVM) is a novel attention mechanism that combines the strengths of transformers and RNNs with controllable computational complexity and memory usage. It supports fixed-size or growing state, offers subquadratic prefill time and sublinear state growth, and can be implemented without custom kernels.