arXiv:2609.25537v1 Announce Type: new
Abstract: Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing laten...
By Md Mostafizer Rahman, Md Faizul Ibne Amin, Md Shahajada Mia, Yutaka Watanobe, Fang Liu
arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.
By Mengting Ai, Tianxin Wei, Sirui Chen, Jingrui He
The paper reports six submissions by the ESTS team to the WMT26 Model Compression Shared Task for English–Simplified Chinese and English–Egyptian Arabic. Each submission offers three compression operating points derived from GPT‑OSS‑20B, using routing‑informed expert pruning, cross‑lingual routing divergence for capacity allocation, and MXFP4 quantization of retained expert projection weights. The resulting models, ranging from 4.186 B to 7.770 B parameters, are fine‑tuned on GPT‑5.1 synthetic data and evaluated internally with xCOMET‑XL.
By Liu O. Martin, Lucas Bandarkar, Nanyun Peng
The paper introduces Ladder Side Tuning (LST), a parameter‑efficient fine‑tuning method that adds a lightweight side network to large language models. LST matches QLoRA’s compute scaling while halving peak memory usage, enabling 7B‑parameter models to be fine‑tuned on a single 12 GB GPU with 2k‑token contexts without gradient checkpointing. The authors also present xLadder, a depth‑extended variant that increases effective depth through cross‑connections, allowing deeper reasoning without extra memory overhead.
By Estelle Zheng, Nathan Cerisara, S\'ebastien Warichet, Emmanuel Helbert, Christophe Cerisara
The paper introduces BASP, a batch‑aware sequence parallelism method that partitions GPUs into disjoint groups based on micro‑batch size to reduce all‑to‑all communication. By localizing communication, BASP improves training efficiency for long‑context LLMs. Experiments on NVIDIA A100 clusters show up to 1.17‑1.31× faster end‑to‑end training on Llama and Qwen models while maintaining the same accuracy and memory usage.
By Bigyan Ghimire, Jon C. Calhoun
The paper introduces the Tri‑Metric Router, a deterministic, training‑free policy that chooses among Raw, Neural, and Lexical pipelines for retrieval‑augmented generation on commodity GPUs. It uses three CPU‑side signals—spatial complexity, syntactic density, and type‑token ratio—to balance VRAM headroom and latency, calibrated on LongBench qasper. The method eliminates out‑of‑memory failures and improves alignment and F1 scores compared to always‑on lexical compression without extra VRAM or training costs.
By Saipraveen Vabbilisetty, Ajay Kumar Boddepalli, Deep Narayan Mishra, Shashank Kapadia, Haoan Wang, Anupriya Sharma