{"total":46,"rows":[{"id":"2310.06839","title":"LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression","created":"2023-10-10","cited_by_count":453,"area":"inference"},{"id":"2404.14294","title":"A Survey on Efficient Inference for Large Language Models","created":"2024-04-22","cited_by_count":205,"area":"inference"},{"id":"2310.07240","title":"CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving","created":"2023-10-11","cited_by_count":203,"area":"inference"},{"id":"2506.06266","title":"Cartridges: Lightweight and general-purpose long context representations via self-study","created":"2025-06-06","cited_by_count":86,"area":"inference"},{"id":"2505.18227","title":"Token Reduction Should Go Beyond Efficiency in Generative Models -- From Vision, Language to Multimodality","created":"2025-05-23","cited_by_count":50,"area":"inference"},{"id":"2502.00791","title":"Vision-centric Token Compression in Large Language Model","created":"2025-02-02","cited_by_count":21,"area":"inference"},{"id":"2505.12215","title":"GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment","created":"2025-05-18","cited_by_count":21,"area":"inference"},{"id":"2407.15504","title":"Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models","created":"2024-07-22","cited_by_count":19,"area":"inference"},{"id":"2407.08892","title":"Characterizing Prompt Compression Methods for Long Context Inference","created":"2024-07-11","cited_by_count":18,"area":"inference"},{"id":"2404.07979","title":"LLoCO: Learning Long Contexts Offline","created":"2024-04-11","cited_by_count":16,"area":"inference"},{"id":"2510.08907","title":"Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors","created":"2025-10-10","cited_by_count":14,"area":"inference"},{"id":"2602.01719","title":"COMI: Coarse-to-fine Context Compression via Marginal Information Gain","created":"2026-02-02","cited_by_count":13,"area":"inference"},{"id":"2501.12959","title":"Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference","created":"2025-01-22","cited_by_count":12,"area":"inference"},{"id":"2410.08391","title":"KV Prediction for Improved Time to First Token","created":"2024-10-10","cited_by_count":11,"area":"inference"},{"id":"2507.08799","title":"KV Cache Steering for Controlling Frozen LLMs","created":"2025-07-11","cited_by_count":11,"area":"inference"},{"id":"2608.06503","title":"Toward Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability","created":"2026-08-06","cited_by_count":10,"area":"inference"},{"id":"2509.15763","title":"UniGist: Towards General and Hardware-aligned Sequence-level Long Context Compression","created":"2025-09-19","cited_by_count":9,"area":"inference"},{"id":"2501.14743","title":"KVDirect: Distributed Disaggregated LLM Inference","created":"2024-12-13","cited_by_count":8,"area":"inference"},{"id":"2505.00019","title":"An Empirical Study on Prompt Compression for Large Language Models","created":"2025-04-24","cited_by_count":8,"area":"inference"},{"id":"2512.04550","title":"AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees","created":"2025-12-04","cited_by_count":8,"area":"inference"},{"id":"2505.15774","title":"Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention","created":"2025-05-21","cited_by_count":7,"area":"inference"},{"id":"2510.20797","title":"No Mean Feat: Simple, Strong Baselines for Context Compression","created":"2025-10-23","cited_by_count":7,"area":"inference"},{"id":"2509.19228","title":"CompLLM: Compression for Long Context Q&A","created":"2025-09-23","cited_by_count":5,"area":"inference"},{"id":"2501.06730","title":"Better Prompt Compression Without Multi-Layer Perceptrons","created":"2025-01-12","cited_by_count":4,"area":"inference"},{"id":"2506.00307","title":"Lossless Token Sequence Compression via Meta-Tokens","created":"2025-05-30","cited_by_count":4,"area":"inference"},{"id":"2606.07878","title":"Still: Amortized KV Cache Compaction in a Single Forward Pass","created":"2026-06-05","cited_by_count":4,"area":"inference"},{"id":"2606.09659","title":"End-to-End Context Compression at Scale","created":"2026-06-08","cited_by_count":4,"area":"inference"},{"id":"2405.11299","title":"The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving","created":"2024-05-18","cited_by_count":3,"area":"inference"},{"id":"2512.14244","title":"From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition","created":"2025-12-16","cited_by_count":3,"area":"inference"},{"id":"2605.09463","title":"Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven","created":"2026-05-10","cited_by_count":3,"area":"inference"},{"id":"2605.28069","title":"ZipRL: Adaptive Multi-Turn Context Compression with Hindsight Response Replay","created":"2026-05-27","cited_by_count":3,"area":"inference"},{"id":"2508.17032","title":"Learned Structure in Cartridges: Keys as Shareable Routers in Self-Studied Representations","created":"2025-08-23","cited_by_count":2,"area":"inference"},{"id":"2603.23527","title":"Compression Method Matters: Benchmark-Dependent Output Dynamics in LLM Prompt Compression","created":"2026-03-06","cited_by_count":2,"area":"inference"},{"id":"2512.03643","title":"Optical Context Compression Is Just (Bad) Autoencoding","created":"2025-12-03","cited_by_count":1,"area":"inference"},{"id":"2602.01778","title":"Data Distribution Matters: A Data-Centric Perspective on Context Compression for Large Language Model","created":"2026-02-02","cited_by_count":1,"area":"inference"},{"id":"2603.23525","title":"Prompt Compression in Production Task Orchestration: A Pre-Registered Randomized Trial","created":"2026-03-06","cited_by_count":1,"area":"inference"},{"id":"2607.21231","title":"Progressive Cramming: Reliable Token Compression and What It Reveals","created":"2026-07-23","cited_by_count":1,"area":"inference"},{"id":"2608.26175","title":"Lost in Compression: A Controlled Cross-Lingual Audit of Extractive Prompt Compressors","created":"2026-07-27","cited_by_count":1,"area":"inference"},{"id":"2603.04759","title":"Stacked from One: Multi-Scale Self-Injection for Context Window Extension","created":"2026-03-05","cited_by_count":0,"area":"inference"},{"id":"2603.25926","title":"Density-aware Soft Context Compression with Semi-Dynamic Compression Ratio","created":"2026-03-26","cited_by_count":0,"area":"inference"},{"id":"2606.03739","title":"Entropy Gate: Entropy Quenching for Near-Lossless Token Compression in LLM Pipelines","created":"2026-06-02","cited_by_count":0,"area":"inference"},{"id":"2609.32474","title":"PC-SubMax: Efficient Prompt Compression via Regularized Submodular Maximization","created":"2026-09-26","cited_by_count":0,"area":"inference"},{"id":"2609.36526","title":"Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations","created":"2026-09-29","cited_by_count":0,"area":"inference"},{"id":"2610.05885","title":"The Optimization Landscape of Learning Compacted Context Models","created":"2026-10-05","cited_by_count":0,"area":"inference"},{"id":"2610.13605","title":"zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss","created":"2026-10-09","cited_by_count":0,"area":"inference"},{"id":"2610.18654","title":"Kwai Summary Attention V2 Technical Report","created":"2026-10-13","cited_by_count":0,"area":"inference"}],"next_cursor":null}