1source_id,title,authors,venue_status,year,url,access_date,revision,repository_url,commit,checkpoint_availability,supported_backends,license,relevance,reproduction_status,verification2llmlingua2_2024,LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression,"Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang",Findings of ACL 2024 (arXiv Comments: Accepted at Findings of ACL 2024),2024,https://arxiv.org/abs/2403.12968,2026-10-29,arXiv v2 (2024-08-12); pip llmlingua 0.2.2 (2024-04-09),https://github.com/microsoft/LLMLingua,5a4c78ae18ab17a98cf997e8259354e546081d64,"HF: microsoft/llmlingua-2-xlm-roberta-large-meetingbank (0.56B F32, 2235.8 MB, rev ebaba9b, mit); microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank (0.18B F32, 709.4 MB, rev 5f0c827, apache-2.0)",torch + transformers (AutoModelForTokenClassification); device_map cuda (default) / cpu / mps verified in prompt_compressor.py,"MIT (repo, pip); checkpoints mit / apache-2.0",Token-classification extractive compressor used as the prompt-compression baseline (rate / target_token),pending,"page fetched (arXiv, GitHub, HF API, PyPI) + source code read"3longbench2024,"LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding","Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li","ACL 2024 Long Papers pp. 3119-3137, DOI 10.18653/v1/2024.acl-long.172",2024,https://aclanthology.org/2024.acl-long.172/,2026-10-29,arXiv v2 (2024-06-19); HF dataset zai-org/LongBench sha 5e628be450b7e67fb7ae6e201bd6d8f7056f7672 (2024-12-18),https://github.com/THUDM/LongBench,2e00731f8d0bff23dc4325161044d0ed8af94c1e,"HF dataset THUDM/LongBench -> zai-org/LongBench; subsets qasper 200, multifieldqa_en 150, hotpotqa 200, 2wikimqa 200, musique 200; metric F1; context field included",datasets library (JSON); evaluation code in repo,"Code MIT; dataset card has no license field (unverified); upstream: Qasper CC BY 4.0, HotpotQA CC BY-SA 4.0, MuSiQue CC BY 4.0, 2WikiMultihopQA repo Apache-2.0 (data unverified), MultiFieldQA-en unverified","QA evaluation set (F1) with explicit long context for compression / transfer experiments; v2 (arXiv 2412.15204, 503 MCQ, 8k-2M words, apache-2.0) is not a drop-in replacement",pending,"page fetched (arXiv, ACL Anthology, GitHub raw, HF API, 4 upstream license pages)"4ruler2024,RULER: What's the Real Context Size of Your Long-Context Language Models?,"Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, Boris Ginsburg",COLM 2024 (arXiv Comments); GitHub BibTeX still cites arXiv preprint,2024,https://arxiv.org/abs/2404.06654,2026-10-29,arXiv v3 (2024-08-06),https://github.com/NVIDIA/RULER,c3f5e3b4f87f97e048793bb510a3a6b19a46bf3a,No checkpoints; synthetic data generator (haystack: repeat / essay (Paul Graham) / needle; needles words / numbers / uuids); QA from SQuAD and HotpotQA,Python generator + model-agnostic inference scripts (vLLM/HF/TRT-LLM per repo),Apache-2.0 (repo); generated-data license depends on upstream sources (unverified),"Synthetic long-context tasks (NIAH single/multi-key/multi-value/multi-query, variable_tracking, common_words_extraction, freq_words_extraction, qa) for controlled compression stress tests",pending,"page fetched (arXiv, GitHub API, raw README)"5mlxlm2026,mlx-lm: LLM inference and fine-tuning with MLX (Apple silicon),Apple ML Explore team (ml-explore),software release v0.32.2 (2026-10-16),2026,https://github.com/ml-explore/mlx-lm,2026-10-29,v0.32.2 (GitHub release 2026-10-16T02:49:02Z; PyPI mlx-lm 0.32.2),https://github.com/ml-explore/mlx-lm,6b572ab1b531dd8fd2d13f6f36a89c2eccda18ea,N/A (framework); prompt caches saved as .safetensors via mlx_lm.cache_prompt / save_prompt_cache,MLX on macOS (mlx>=0.32.2 Darwin); extras cuda12 / cuda13 / cpu; Python >=3.11; transformers>=5.7.0,MIT,"Local KV-cache backend: mlx_lm.cache_prompt, --prompt-cache-file, --max-kv-size, --kv-bits, --kv-group-size, --quantized-kv-start, make_prompt_cache, save_prompt_cache, load_prompt_cache, trim_prompt_cache, KVCache, QuantizedKVCache, RotatingKVCache",pending,"page fetched (README, releases, GitHub/PyPI API) + source read at HEAD"6hf_kvcache_docs,Transformers documentation: Cache strategies (KV cache),Hugging Face,"online documentation, Transformers v5.21.1",2026,https://huggingface.co/docs/transformers/en/kv_cache,2026-10-29,docs v5.21.1 (links on page),https://github.com/huggingface/transformers,unverified,N/A (framework docs),PyTorch; quantized cache backends hqq / quanto / fp8; offloading to CPU; torch.compile with StaticCache,Apache-2.0 (Transformers library; license not restated on the docs page),"Documents DynamicCache, StaticCache, QuantizedCache (hqq/quanto/fp8), EncoderDecoderCache, cache_implementation static / offloaded / offloaded_static / quantized; in-process prefix reuse via Prefill a cache (prefix caching) with copy.deepcopy, Iterative generation, crop(); cache_position and to-disk serialisation not mentioned on the page",pending,"page fetched (kv_cache + internal/generation_utils, full text)"7anthropic_prompt_caching,Claude Developer Platform documentation: Prompt caching (+ Models overview),Anthropic,online documentation (living page),2026,https://platform.claude.com/docs/en/build-with-claude/prompt-caching,2026-10-29,page state on 2026-10-29 (no version number shown),unverified,unverified,N/A (hosted API),"Claude API, Claude Platform on AWS, Amazon Bedrock, Google Cloud (Google managed), Microsoft Foundry",proprietary documentation (no license stated),"cache_control ephemeral, up to 4 breakpoints, prefix order tools->system->messages, 20-block lookback; minimum 512 / 1024 / 2048 / 4096 tokens by model; TTL 5 min default or 1h; usage fields cache_creation_input_tokens, cache_read_input_tokens, cache_creation.ephemeral_5m_input_tokens / ephemeral_1h_input_tokens; writes 1.25x (5m) / 2x (1h), reads 0.1x (0.025x Fable 5.1 / Mythos 5.1; 0.05x Opus 5.5 / Sonnet 5.5); current IDs claude-fable-5-1 ($10/$50), claude-opus-5-5 ($4/$20), claude-sonnet-5-5 ($2/$10), claude-haiku-5-5 (from $0.10/$0.50)",pending,"page fetched (both pages, full text)"8li2024,Prompt Compression for Large Language Models: A Survey,"Zongqian Li, Yinhong Liu, Yixuan Su, Nigel Collier",arXiv (Comments empty); NAACL 2025 Long per search snippet (unverified),2024,https://arxiv.org/abs/2410.12388,2026-10-29,arXiv v2 (2024-10-17),unverified,unverified,N/A (survey),N/A,arXiv (license not checked),Taxonomy of hard vs soft prompt compression; situates LLMLingua-2,pending,arXiv page fetched; venue from search snippet only9liu2025,KV Cache Compression for Inference Efficiency in LLMs: A Review,"Yanyu Liu, Jingying Fu, Sixiang Liu, Yitian Zou, You Fu, Jiehan Zhou, Shouhua Zhang",arXiv preprint (Comments: 12 pages),2025,https://arxiv.org/abs/2508.06297,2026-10-29,arXiv v1 (2025-08-08),unverified,unverified,N/A (review),N/A,arXiv (license not checked),Review of selective / quantisation / attention-based KV-cache compression,pending,arXiv page fetched10javidnia2025,"Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques","Neusha Javidnia, Bita Darvish Rouhani, Farinaz Koushanfar",IEEE CICC 2025 (invited; arXiv Comments),2025,https://arxiv.org/abs/2503.11816,2026-10-29,arXiv v3 (2025-04-22),unverified,unverified,N/A,N/A,arXiv (license not checked),Taxonomy and accuracy / latency evaluation of KV-cache compression methods,pending,arXiv page fetched11gao2025,Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving,"Wei Gao, Xinyu Zhou, Peng Sun, Tianwei Zhang, Yonggang Wen",MLSys 2025 (arXiv Comments),2025,https://arxiv.org/abs/2503.24000,2026-10-29,arXiv v1 (2025-03-31),unverified,unverified,open-source tools announced (URL not verified),N/A,arXiv (license not checked),Shows KV compression can lengthen outputs and raise end-to-end latency in serving; benchmark caveats for our cost model,pending,arXiv page fetched12ye2025,KVCOMM: Online Cross-context KV-cache Communication for Efficient LLM-based Multi-agent Systems,"Hancheng Ye, Zhengqi Gao, Mingyuan Ma, Qinsi Wang, Yuzhe Fu, Ming-Yu Chung, Yueqian Lin, Zhijian Liu, Jianyi Zhang, Danyang Zhuo, Yiran Chen",NeurIPS 2025 (arXiv Comments: Accepted for publication in NeurIPS2025),2025,https://arxiv.org/abs/2510.12872,2026-10-29,arXiv v2 (2025-11-01),unverified,unverified,unverified,GPU (H100 reported),arXiv (license not checked),"Closest prior work: training-free cross-agent KV-cache reuse with context-aware offsets, ~6.7x prefill speedup in 3-agent setting",pending,arXiv page fetched13bian2026,TokenDance: Scaling Multi-Agent LLM Serving via Collective KV Cache Sharing,"Zhuohang Bian, Feiyang Wu, Chengrui Zhang, Hangcheng Dong, Yun Liang, Youwei Zhuo",arXiv preprint under review,2026,https://arxiv.org/abs/2604.03143,2026-10-29,arXiv v1 (2026-04-03),unverified,unverified,unverified,GPU serving (vLLM comparison),arXiv (CC BY 4.0 per HTML header in search snippet; unverified),All-gather KV sharing across agents with sparse diffs against a master copy; up to 17.5x lower per-agent KV storage,pending,arXiv page fetched14jeon2026,LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents,"Hyesung Jeon, Hyeongju Ha, Jae-Joon Kim",ICML 2026 Poster (arXiv journal-ref),2026,https://arxiv.org/abs/2602.01053,2026-10-29,arXiv v2 (2026-05-31),unverified,unverified,unverified,GPU,arXiv (license not checked),Shares the base-model part of the KV cache across LoRA-adapted agents; low-rank adapter-specific part,pending,arXiv page fetched15chang2026,MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation,"Yurui Chang, Yiran Wu, Qingyun Wu, Lu Lin",arXiv preprint (no venue stated),2026,https://arxiv.org/abs/2603.23234,2026-10-29,arXiv v2 (2026-05-28),unverified,unverified,unverified,N/A,arXiv (license not checked),Cross-agent / cross-backbone shared memory via contrastive trajectory distillation; knowledge transfer at memory level rather than KV level,pending,arXiv page fetched16