Gated Segmented State Space
Gated Segmented State Space at Beyond Market Intelligence is a file of 2 stories. The newest of them: “Linear Recurrence Matches Transformer Accuracy at a Fraction of the Cost” and “Two paths to a smarter decision model: equal accuracy, unequal cost”. When you budget-match a linear recurrent model and a transformer down to 15 million parameters on a long-context retrieval task, something unexpected happens: neither learns much. Two paths to the same decision model, matched within 0.02% on parameters, yet the cost difference is stark. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Gated Segmented State Space story on Beyond Market Intelligence, newest first.
Linear Recurrence Matches Transformer Accuracy at a Fraction of the Cost
When you budget-match a linear recurrent model and a transformer down to 15 million parameters on a long-context retrieval task, something unexpected happens: neither learns much. Accuracy hovers near chance for both. The real story is cost. At 8,192 tokens the linear model runs 2.37× faster, and training time opens a 6-7× gap that only widens with scale. This negative result from Jev's decision-lab is exactly the point, small decision models are accuracy-starved regardless of substrate, so the winning choice becomes efficiency.
Two paths to a smarter decision model: equal accuracy, unequal cost
Two paths to the same decision model, matched within 0.02% on parameters, yet the cost difference is stark. A linear recurrent backbone and a standard attention transformer were built on identical budgets for a long-context retrieval task. At 8K tokens, the linear model runs 2.37× faster. Training? Six epochs took 30 minutes versus 40-45 minutes *per epoch* for the transformer. Neither substrate learned much at 15M parameters, accuracy hovered near chance. The real insight is where cost lives.