arXiv:2610.10304v1 Announce Type: new Abstract: We study whether latent sequence compression of prompt prefixes preserves the capabilities that large language models rely on during inference. We intr...
The study evaluates how extractive prompt compressors affect token costs across ten languages, finding that compressors trained on English data widen the token premium gap for non‑English languages, while a multilingual compressor does not. The gap is tied to the supervision data rather than model architecture, and aggressive compression can reduce non‑English contexts to near‑zero utility. A translate‑then‑compress approach can match or outperform native compression at roughly half the token cost in several languages.
arXiv:2606. 31796v1 Announce Type: cross Abstract: We study three complementary techniques for training compute-efficient language models.
The paper introduces zip2zip++, a method for inference‑time token compression that reduces token counts using a dynamic Lempel‑Ziv‑Welch (LZW) codec while preserving language model quality. It identifies four problems with prior LZW‑based compression—embedding conflict, improper RoPE application, harmful digit merges, and segmentation ambiguity—and proposes solutions: separate input/output embeddings, size‑aware RoPE, and a selective‑merge LZW codec. Experiments on four models show that these changes halve the quality gap in perplexity and reasoning accuracy compared to the uncompressed baseline, achieving similar compression ratios.
Pearl-Chat is an 8.5‑million‑parameter causal decoder‑only transformer built from first principles in JAX and Flax NNX, specifically engineered for the low‑resource Bantu language Luganda. It uses a byte‑level BPE tokenizer trained from scratch on curated Luganda corpora, achieving a two‑fold compression improvement over larger models like LLaMA‑3 and Gemma. The model is trained in two stages—pre‑training on diverse Luganda text and instruction fine‑tuning on dialogues—reaching a validation perplexity of 1.20 and high ROUGE‑L and BLEU scores on a held‑out diagnostic benchmark, with all weights, tokenizers, and code released openly.
arXiv:2606. 24083v1 Announce Type: cross Abstract: "Talk short.
arXiv:2511. 20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference.
The paper introduces TokenAdapt, a model‑agnostic tokenizer transplantation method that uses a hybrid heuristic to initialize new token embeddings, and a novel pre‑tokenization learning approach for multi‑word Supertokens to improve compression. TokenAdapt combines local subword decomposition and global semantic similarity to preserve semantics while reducing retraining needs. Empirical results show that TokenAdapt outperforms existing baselines such as Transtokenizer and ReTok, achieving lower perplexity ratios and significant compression gains.
arXiv:2607. 08399v1 Announce Type: cross Abstract: Large language models process prompts by propagating activations through dozens of layers before generating a response.
arXiv:2609.37066v1 Announce Type: cross Abstract: Post-training is central to mathematical reasoning in modern large language models (LLMs), but endpoint pass@1 alone underidentifies what has changed...
Manacá-1B is a 1.72‑billion‑parameter, open decoder‑only language model trained from scratch for Brazilian Portuguese, released with a fully containerized, reproducible training pipeline and complete logs. The authors evaluate it against nine open baselines on four Portuguese benchmarks, reporting standard errors and paired significance tests, and find that Manacá-1B outperforms smaller models on LAMBADA‑PT while remaining competitive on commonsense completion. They also uncover a tokenizer‑related evaluation pitfall that can drastically lower accuracy and provide a simple fix, releasing all code, logs, and corrected tokenizer for full reproducibility.
arXiv:2605. 17932v2 Announce Type: replace-cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus mainly on autoregressive architectures.