#in-context-learning

2 articles

01 ·How Transformers Work·★ MEMBER·PAPER·7 min read Paper Explained: LimiX-2 — A Tabular Foundation Model That Learns the Whole Table, Not Just the Answer Column LimiX-2 from the ground up: the shift from PFNs that predict a single target to Contextual Mechanism Networks that learn a table's joint distribution, cell-level representations, pretraining on synthetic causal data, and what the three benchmarks do and don't show. 02 ·Inference & Serving·FREE·PAPER·8 min read Paper explained: BDH-CQ — an AI that thinks without words. Recurrent memory plus latent reasoning resets ARC's cost frontier BDH-CQ (arXiv:2608.09888) never verbalizes its intermediate reasoning: demonstrations are written into a recurrent memory, and the answer is computed by iterating in a high-dimensional latent space. A 150M-parameter model reaches 29.5% pass@2 on ARC-AGI-1 at $0.0007 per task, breaking the reported cost–accuracy Pareto frontier. We explain the paper using only its own text as evidence.