The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper introduces a novel patching strategy for byte‑level language models, called novelty patching, which starts a new patch when upcoming bytes are unlikely to repeat recent context, inspired by LZ77 compression. Experiments on a 1.9 GB math corpus show that novelty patching improves final‑answer accuracy from 47 % to 72 % at 12 M parameters and raises computed‑result accuracy from 21 % to 57 % at 50 M parameters, while having limited impact on prose and source code. The study also analyzes patch boundary distributions and reports several failed approaches, such as typed uncertainty and context‑aware novelty prediction. whyItMatters":"The findings demonstrate that a simple, data‑driven patching rule can significantly enhance the performance of byte‑level models on tasks involving computed results, offering a practical improvement for large language models that process raw text without explicit labels."
The paper investigates how byte‑level language models that use entropy‑based patching (like BLT) fail to compute certain predictable but value‑dependent positions, such as numbers following an equals sign in math solutions. By introducing a boundary‑dependence signal—measuring the model’s loss when a patch start is removed—they show that this signal, combined with entropy, significantly improves accuracy on computed outputs across various model sizes and budgets. The study demonstrates that entropy alone leaves a systematic blind spot that boundary dependence can fill, leading to higher exact‑match rates on challenging computed tasks.
arXiv:2608.20873v2 Announce Type: new Abstract: A deployed language model is a file that many things depend on - a benchmark report, a certification, a fleet of devices - and every way of teaching it...
arXiv:2611.06598v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) adapts a frozen language model through a compact learned update, making it a natural setting for asking how much information...
arXiv:2608.20873v3 Announce Type: replace Abstract: A 4-bit quantized weight specifies a rounding cell rather than a single full-precision value. We introduce in-cell learning, a paradigm for writing...
Delta N-gram introduces a forward‑only write path into a language model’s native hashed N‑gram table, preserving the pretrained table while adding evidence through a sparse patch Δ. The method uses a 4.1 M‑parameter writer that emits gated targets per event and applies a block‑averaged delta rule to keep each row a convex combination of its old value and the new targets. Experiments on a 0.6 B prototype and Qwen3.8‑Flash‑Next show that the delta writer maintains more document‑specific memory and reduces drift compared to online gradient writes, achieving stable performance over thousands of writes. whyItMatters":"The approach allows continual updates to a language model’s memory without altering its pretrained parameters, enabling efficient, stable, and document‑specific knowledge retention during inference."