# Sequential Functional Structured Tucker Compression for Large Language Model Attentions

arXiv: https://arxiv.org/abs/2610.00717 · HTML: https://mlsanity.com/paper/2610.00717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 25 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 21 of 21)

- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot](https://mlsanity.com/paper/2301.00774/) (2023)
- [A Simple and Effective Pruning Approach for Large Language Models](https://mlsanity.com/paper/2306.11695/) (2023)
- [SliceGPT: Compress Large Language Models by Deleting Rows and Columns](https://mlsanity.com/paper/2401.15024/) (2024)
- [SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression](https://mlsanity.com/paper/2403.07378/) (2024)
- [Accelerating Sparse Deep Neural Networks](https://mlsanity.com/paper/2104.08378/) (2021)
- [Language model compression with weighted low-rank factorization](https://mlsanity.com/paper/2207.00112/) (2022)
- [ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models](https://mlsanity.com/paper/2312.05821/) (2023)
- [SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression](https://mlsanity.com/paper/2503.12340/) (2025)
- [Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression](https://mlsanity.com/paper/2410.03765/) (2024)
- [TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition](https://mlsanity.com/paper/2307.00526/) (2023)
- [QSVD: Efficient Low-rank Approximation for Unified Query-Key-Value Weight Compression in Low-Precision Vision-Language Models](https://mlsanity.com/paper/2510.16292/) (2025)
- [AdaSVD: Adaptive Singular Value Decomposition for Large Language Models](https://mlsanity.com/paper/2502.01403/) (2025)
- [LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models](https://mlsanity.com/paper/2404.09695/) (2024)
- [TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs](https://mlsanity.com/paper/2501.15674/) (2025)
- [AA-SVD : Anchored and Adaptive SVD for Large Language Model Compression](https://mlsanity.com/paper/2604.02119/) (2026)
- [COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation](https://mlsanity.com/paper/2507.07580/) (2025)
- [Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models](https://mlsanity.com/paper/2501.19090/) (2025)
- [Large Language Model Compression with Global Rank and Sparsity Optimization](https://mlsanity.com/paper/2505.03801/) (2025)
- [Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression](https://mlsanity.com/paper/2606.03465/) (2026)
- [Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models](https://mlsanity.com/paper/2505.14871/) (2025)

## Abstract (arXiv)

Post-training compression of LLM attention is often formulated as independent matrix approximation, ignoring both the shared structure among attention projections and the representation shift introduced by earlier compression. We propose FTC, a sequential structured compression framework that adapts the approximation to the current compressed model while jointly exploiting the native Q/K/V head structure under a fixed storage budget. The output projection is handled separately to account for the changed post-attention representation. FTC requires neither fine-tuning nor gradient-based recovery. Across seven decoder-only LLMs from 6B to 32B parameters, FTC achieves the lowest WikiText-2 perplexity among the compared methods at every tested keep ratio on five modern GQA models, with the largest gains under aggressive compression. The improvements transfer to downstream tasks and remain substantial at the 32B scale.