In-context learning
In-context learning at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Beyond the Token Stream: Architectures for Deeper Reasoning”, “Beyond the Hype: A Practical Guide to Sparse Attention Claims”, and “Explore how BDH-CQ unifies memory and inference in one system”. The field is quietly shifting its focus from longer chains of thought to something more fundamental: reasoning that never touches language at all. Reading how a practitioner can make sparse attention look deceptively strong, through careful benchmark selection, unoptimized baselines, and aggregate reporting, is both uncomfortable and necessary. 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 In-context learning story on Beyond Market Intelligence, newest first.
Beyond the Token Stream: Architectures for Deeper Reasoning
The field is quietly shifting its focus from longer chains of thought to something more fundamental: reasoning that never touches language at all. The breakdown of latent reasoning into five distinct families, from Coconut's continuous hidden states to BDH-CQ's in-context recurrent memory, gives us a useful map of where the frontier actually sits. The real question isn't which architecture wins, but whether readable traces were ever the point.
Beyond the Hype: A Practical Guide to Sparse Attention Claims
Reading how a practitioner can make sparse attention look deceptively strong, through careful benchmark selection, unoptimized baselines, and aggregate reporting, is both uncomfortable and necessary. The honest admission that even good-faith researchers fall into these traps makes it more valuable, not less. This isn't about dismissing the field but about demanding rigor where it's easy to cut corners. For anyone navigating efficient attention research, it's a practical warning worth internalizing before your next evaluation.
Explore how BDH-CQ unifies memory and inference in one system
A new reasoning system called BDH-CQ is challenging the cost, accuracy tradeoffs that have long constrained AI development. By updating recurrent memory with demonstrations and solving queries through iterative, high-dimensional computation, it keeps reasoning entirely in latent space. No task identifiers are used in training, and no parameters shift at inference. A 150M-parameter version achieves 29.5% pass@2 on ARC-AGI-1 at $0.00070 per task. That breaks the reported Pareto frontier, showing that accessible, efficient reasoning is possible.