Context Compression via Explicit Information Transmission

• URL: http://arxiv.org/abs/2602.03784v1
• Date: Tue, 03 Feb 2026 17:44:12 GMT
• Title: Context Compression via Explicit Information Transmission
• Authors: Jiangnan Ye, Hanqi Yan, Zhenyi Shen, Heng Chang, Ye Mao, Yulan He,
• Abstract summary: Long-context inference with Large Language Models (LLMs) is costly due to quadratic attention and growing key-value caches.<n>We propose ComprExIT, a lightweight framework that formulates soft compression into a new paradigm.
• Score: 25.078241611630585
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-context inference with Large Language Models (LLMs) is costly due to quadratic attention and growing key-value caches, motivating context compression. In this work, we study soft context compression, where a long context is condensed into a small set of continuous representations. Existing methods typically re-purpose the LLM itself as a trainable compressor, relying on layer-by-layer self-attention to iteratively aggregate information. We argue that this paradigm suffers from two structural limitations: (i) progressive representation overwriting across layers (ii) uncoordinated allocation of compression capacity across tokens. We propose ComprExIT (Context Compression via Explicit Information Transmission), a lightweight framework that formulates soft compression into a new paradigm: explicit information transmission over frozen LLM hidden states. This decouples compression from the model's internal self-attention dynamics. ComprExIT performs (i) depth-wise transmission to selectively transmit multi-layer information into token anchors, mitigating progressive overwriting, and (ii) width-wise transmission to aggregate anchors into a small number of slots via a globally optimized transmission plan, ensuring coordinated allocation of information. Across six question-answering benchmarks, ComprExIT consistently outperforms state-of-the-art context compression methods while introducing only ~1% additional parameters, demonstrating that explicit and coordinated information transmission enables more effective and robust long-context compression.

Related papers

• Stacked from One: Multi-Scale Self-Injection for Context Window Extension [69.24689919827817]
  modelname is a novel framework based on multi-grained context compression and query-aware information acquisition.<n>modelnameachieves performance superior or comparable to strong baselines.
  arXiv  Detail & Related papers  (2026-03-05T03:16:16Z)
• Seq2Seq2Seq: Lossless Data Compression via Discrete Latent Transformers and Reinforcement Learning [3.2641459166493405]
  We propose a novel compression method based on Reinforcement Learning applied to a T5 language model architecture.<n>This approach enables the compression of data into sequences of tokens rather than traditional vector representations.<n>By leveraging the latent information within language models, our system effectively compresses data without requiring explicit content understanding.
  arXiv  Detail & Related papers  (2026-02-12T16:30:55Z)
• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• CompLLM: Compression for Long Context Q&A [47.90063873976842]
  We introduce CompLLM, a soft compression technique designed for practical deployment.<n>Instead of processing the context holistically, CompLLM divides it into segments and compresses each one independently.<n>Our experiments show that with a 2x compression rate, at high context lengths CompLLM speeds up Time To First Token (TTFT) by up to 4x and reduces the KV cache size by 50%.
  arXiv  Detail & Related papers  (2025-09-23T16:49:43Z)
• UniGist: Towards General and Hardware-aligned Sequence-level Long Context Compression [86.33995240043936]
  UniGist is a sequence-level long-context compression framework for large language models.<n>It efficiently preserves context information by replacing raw tokens with special compression tokens (gists) in a fine-grained manner.<n>Our scheme also supports flexible inference by allowing the actual removal of compressed tokens, resulting in real-time memory savings.
  arXiv  Detail & Related papers  (2025-09-19T08:47:37Z)
• Re-Densification Meets Cross-Scale Propagation: Real-Time Neural Compression of LiDAR Point Clouds [83.39320394656855]
  LiDAR point clouds are fundamental to various applications, yet high-precision scans incur substantial storage and transmission overhead.<n>Existing methods typically convert unordered points into hierarchical octree or voxel structures for dense-to-sparse predictive coding.<n>Our framework comprises two lightweight modules. First, the Geometry Re-Densification Module re-densifies encoded sparse geometry, extracts features at denser scale, and then re-sparsifies the features for predictive coding.
  arXiv  Detail & Related papers  (2025-08-28T06:36:10Z)
• DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression [63.83422894663496]
  We propose a dynamic attention-aware approach for task-agnostic prompt compression (DAC)<n>This approach effectively integrates entropy and attention information, dynamically sensing entropy shifts during compression to achieve fine-grained prompt compression.<n>Extensive experiments across various domains, including LongBench, GSM8K, and BBH, show that DAC consistently yields robust and substantial improvements.
  arXiv  Detail & Related papers  (2025-07-16T06:16:06Z)
• DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens [20.044306399439265]
  Large Language Models (LLMs) face computational inefficiencies and redundant processing when handling long context inputs.<n>We propose Dynamic Allocation of Soft Tokens (DAST), a simple yet effective method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression.<n> Experimental results across multiple benchmarks demonstrate that DAST surpasses state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-02-17T06:55:13Z)
• LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression [43.048684907893104]
  This paper focuses on task-agnostic prompt compression for better generalizability and efficiency. We formulate prompt compression as a token classification problem to guarantee the faithfulness of the compressed prompt to the original one. Our approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa-large and mBERT.
  arXiv  Detail & Related papers  (2024-03-19T17:59:56Z)
• Long Context Compression with Activation Beacon [22.054232261437186]
  Activation Beacon is a plug-in module for transformer-based LLMs. It targets effective, efficient, and flexible compression of long contexts. It achieves a 2x acceleration in inference time and an 8x reduction of memory costs for KV cache.
  arXiv  Detail & Related papers  (2024-01-07T11:57:40Z)
• Context Compression for Auto-regressive Transformers with Sentinel Tokens [37.07722536907739]
  We propose a plug-and-play approach that is able to incrementally compress the intermediate activation of a specified span of tokens into compact ones. Experiments on both in-domain language modeling and zero-shot open-ended document generation demonstrate the advantage of our approach.
  arXiv  Detail & Related papers  (2023-10-12T09:18:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.