Heng Chang, Ye Mao, Hanqi Yan, Jiangnan Ye, Zhenyi Shen, Mitigates Dilution
We lifted 5 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Jiangnan0522/ComprExIT | canonical | 0 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| beacon_predict | Not yet run | Jiangnan0522/ComprExIT/src/model/inference.py code served (permissive licence) · get_code("6cfd975f730cf408") |
| data_loading_factory | Not yet run | Jiangnan0522/ComprExIT/src/data_processing/data_loading.py code served (permissive licence) · get_code("9f88a25274d07db2") |
| get_data_collator_factory | Not yet run | Jiangnan0522/ComprExIT/src/data_processing/ntp_preprocessing.py code served (permissive licence) · get_code("1d56830e39983466") |
| get_device_stats | Not yet run | Jiangnan0522/ComprExIT/src/device_utils.py code served (permissive licence) · get_code("392995da7f414f44") |
| supports_flash_attention_2 | Not yet run | Jiangnan0522/ComprExIT/src/device_utils.py code served (permissive licence) · get_code("414973f82aff6b26") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Long-context LLM agents often struggle with growing token, memory, and latency costs, making efficient context compression essential for practical deployment. Existing LLM-as-a-compressor methods remain noticeably inferior to using the full context. We find that this gap partly stems from their inability to preserve contextual information effectively. In this work, we revisit context compression from a structural perspective and identify two key bottlenecks in standard LLM-based compressors: limited coordination among compression tokens during information aggregation, and layerwise dilution that weakens useful signals from intermediate hidden states. To address these limitations, we propose Com-prExIT (Context Compression via Explicit Information Transmission), a new context compression framework based on explicit information transmission. Com-prExIT adaptively aggregates features into anchors across frozen LLM layers, then allocates information from anchors to compression slots through a globally coordinated transport plan. Experiments on 12 datasets show that ComprExIT consistently outperforms strong soft-compression baselines, improving average F1 by up to 18.5%, while adding only ∼1% trainable parameters and achieving over 2× faster compression than the fastest baselines. Code is available at https://github.com/Jiangnan0522/ComprExIT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2602.03784")
get_code_for_paper("2602.03784")
have("2602.03784")
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