arXiv Machine Learning
Compressing Onboard Language Models for Space Robots under a Behavioral-Fidelity Requirement
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arXiv Machine Learning
Oct 2
QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs
arXiv:2609.39223v2 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive p...
arXiv Machine Learning
Aug 24
Nothing Changed but the Model: CellFill -- Bounded In-Cell Learning for Bit-Identical, Revocable Updates to Quantized LLMs
arXiv:2608.20873v1 Announce Type: new Abstract: Every way of teaching a deployed language model something new -- full fine-tuning, adapter merging, model editing -- replaces the released checkpoint,...
arXiv Machine Learning
Oct 8
SemanticFold: Latent Sequence Compression SeparatesLanguage Modeling, Decodability, and Reasoning
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...
arXiv Machine Learning
Sep 25
Task-Aware Spectral Pruning: A Mixture-of-Masks Framework for Efficient LLM Inference
Task-Aware Spectral Pruning (TASP) is a post‑training framework that tailors sparse masks to specific tasks by calibrating module‑level spectral descriptors against task‑specific ablation effects. It constructs masks that close grouped‑query‑attention and SwiGLU dependencies, routing each user turn to a single compiled mask that remains fixed during prefill and decoding. In experiments, TASP achieves a 43% active‑FLOP reduction while preserving 97.7% of the dense BF16 performance on Llama‑3‑70B, and delivers a 1.44× speedup on an A100 80GB with INT8‑weight/BF16‑compute, reducing decode latency from 45.2 to 31.3 ms/token.
arXiv AI
Sep 23
FoldQuantVLA: Native Low-Bit Quantization of Vision-Language-Action Models via Consistent Folding
arXiv:2609.24433v1 Announce Type: cross Abstract: Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-t...
arXiv AI
Jun 29
Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?
arXiv:2606. 27755v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds what is needed for short robotic instructions.