{"total":95,"rows":[{"id":"2407.12820","title":"PQCache: Product Quantization-based KVCache for Long Context LLM Inference","created":"2024-07-01","cited_by_count":95,"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":"2410.12388","title":"Prompt Compression for Large Language Models: A Survey","created":"2024-10-16","cited_by_count":67,"area":"inference"},{"id":"2502.16002","title":"KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse","created":"2025-02-21","cited_by_count":66,"area":"inference"},{"id":"2412.10319","title":"SCBench: A KV Cache-Centric Analysis of Long-Context Methods","created":"2024-12-13","cited_by_count":60,"area":"inference"},{"id":"2409.01227","title":"Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference","created":"2024-09-02","cited_by_count":53,"area":"inference"},{"id":"2408.03094","title":"500xCompressor: Generalized Prompt Compression for Large Language Models","created":"2024-08-06","cited_by_count":47,"area":"inference"},{"id":"2407.01527","title":"KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches","created":"2024-07-01","cited_by_count":42,"area":"inference"},{"id":"2409.17422","title":"Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction","created":"2024-09-25","cited_by_count":39,"area":"inference"},{"id":"2502.13063","title":"Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity","created":"2025-02-18","cited_by_count":28,"area":"inference"},{"id":"2409.19272","title":"Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios","created":"2024-09-28","cited_by_count":23,"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":"2507.08143","title":"Compactor: Calibrated Query-Agnostic KV Cache Compression with Approximate Leverage Scores","created":"2025-07-10","cited_by_count":17,"area":"inference"},{"id":"2404.07979","title":"LLoCO: Learning Long Contexts Offline","created":"2024-04-11","cited_by_count":16,"area":"inference"},{"id":"2510.18586","title":"TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications","created":"2025-10-21","cited_by_count":16,"area":"inference"},{"id":"2503.19114","title":"Understanding and Improving Information Preservation in Prompt Compression for LLMs","created":"2025-03-24","cited_by_count":15,"area":"inference"},{"id":"2604.19572","title":"A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression","created":"2026-04-21","cited_by_count":15,"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":"2504.08934","title":"Long Context In-Context Compression by Getting to the Gist of Gisting","created":"2025-04-11","cited_by_count":13,"area":"inference"},{"id":"2506.12707","title":"SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression","created":"2025-06-15","cited_by_count":13,"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":"2507.11953","title":"IAM: Efficient Inference through Attention Mapping between Different-scale LLMs","created":"2025-07-16","cited_by_count":10,"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":"2410.11786","title":"Selection-p: Self-Supervised Task-Agnostic Prompt Compression for Faithfulness and Transferability","created":"2024-10-15","cited_by_count":9,"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":"2410.04139","title":"From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression","created":"2024-10-05","cited_by_count":7,"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":"2506.05410","title":"Homogeneous Keys, Heterogeneous Values: Exploiting Local KV Cache Asymmetry for Long-Context LLMs","created":"2025-06-04","cited_by_count":7,"area":"inference"},{"id":"2507.11942","title":"DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression","created":"2025-07-16","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":"2409.15395","title":"Parse Trees Guided LLM Prompt Compression","created":"2024-09-23","cited_by_count":6,"area":"inference"},{"id":"2504.11004","title":"Dynamic Compressing Prompts for Efficient Inference of Large Language Models","created":"2025-04-15","cited_by_count":6,"area":"inference"},{"id":"2504.14992","title":"Efficient Pretraining Length Scaling","created":"2025-04-21","cited_by_count":6,"area":"inference"},{"id":"2508.02053","title":"ProCut: LLM Prompt Compression via Attribution Estimation","created":"2025-08-04","cited_by_count":6,"area":"inference"},{"id":"2508.15813","title":"SCOPE: A Generative Approach for LLM Prompt Compression","created":"2025-08-16","cited_by_count":6,"area":"inference"},{"id":"2606.04557","title":"Cartridges at Scale: Training Modular KV Caches over Large Document Collections","created":"2026-06-03","cited_by_count":6,"area":"inference"},{"id":"2503.07956","title":"EFPC: Towards Efficient and Flexible Prompt Compression","created":"2025-03-11","cited_by_count":5,"area":"inference"},{"id":"2504.04514","title":"Saliency-driven Dynamic Token Pruning for Large Language Models","created":"2025-04-06","cited_by_count":5,"area":"inference"},{"id":"2506.08373","title":"Draft-based Approximate Inference for LLMs","created":"2025-06-10","cited_by_count":5,"area":"inference"},{"id":"2509.19228","title":"CompLLM: Compression for Long Context Q&A","created":"2025-09-23","cited_by_count":5,"area":"inference"},{"id":"2408.10497","title":"QUITO-X: A New Perspective on Context Compression from the Information Bottleneck Theory","created":"2024-08-20","cited_by_count":4,"area":"inference"},{"id":"2412.16120","title":"PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics","created":"2024-12-20","cited_by_count":4,"area":"inference"},{"id":"2502.01025","title":"Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals","created":"2025-02-03","cited_by_count":4,"area":"inference"},{"id":"2504.16574","title":"PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression","created":"2025-04-23","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":"2605.06676","title":"LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction","created":"2026-04-22","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":"2503.02756","title":"BatchGEMBA: Token-Efficient Machine Translation Evaluation with Batched Prompting and Prompt Compression","created":"2025-03-04","cited_by_count":3,"area":"inference"},{"id":"2508.17892","title":"ILRe: Intermediate Layer Retrieval for Context Compression in Causal Language Models","created":"2025-08-25","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":"2603.19733","title":"PoC: Performance-oriented Context Compression for Large Language Models via Performance Prediction","created":"2026-03-20","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":"2605.17304","title":"Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression","created":"2026-05-17","cited_by_count":3,"area":"inference"},{"id":"2412.07682","title":"TRIM: Token Reduction and Inference Modeling for Cost-Effective Language Generation","created":"2024-12-10","cited_by_count":2,"area":"inference"},{"id":"2505.18092","title":"QwenLong-CPRS: Towards $\\infty$-LLMs with Dynamic Context Optimization","created":"2025-05-23","cited_by_count":2,"area":"inference"},{"id":"2507.13681","title":"LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues","created":"2025-07-18","cited_by_count":2,"area":"inference"},{"id":"2603.12646","title":"98$\\times$ Faster LLM Routing Without a Dedicated GPU: Flash Attention, Prompt Compression, and Near-Streaming for the vLLM Semantic Router","created":"2026-03-13","cited_by_count":2,"area":"inference"},{"id":"2603.19635","title":"BEAVER: A Training-Free Hierarchical Prompt Compression Method via Structure-Aware Page Selection","created":"2026-03-20","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":"2605.04426","title":"Telegraph English: Semantic Prompt Compression via Structured Symbolic Rewriting","created":"2026-05-06","cited_by_count":2,"area":"inference"},{"id":"2605.11774","title":"From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction","created":"2026-05-12","cited_by_count":2,"area":"inference"},{"id":"2407.19410","title":"AdaCoder: Adaptive Prompt Compression for Programmatic Visual Question Answering","created":"2024-07-28","cited_by_count":1,"area":"inference"},{"id":"2502.13251","title":"Neural Attention Search","created":"2025-02-18","cited_by_count":1,"area":"inference"},{"id":"2511.12281","title":"Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor","created":"2025-11-15","cited_by_count":1,"area":"inference"},{"id":"2602.00885","title":"Reliability-Aware Determinantal Point Processes for Robust Informative Data Selection in Large Language Models","created":"2026-01-31","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":"2602.09725","title":"Efficient Remote KV Cache Reuse with GPU-native Video Codec","created":"2026-02-10","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":"2604.23277","title":"From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors","created":"2026-04-25","cited_by_count":1,"area":"inference"},{"id":"2605.17932","title":"Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA","created":"2026-05-18","cited_by_count":1,"area":"inference"},{"id":"2606.14875","title":"Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget","created":"2026-06-12","cited_by_count":1,"area":"inference"},{"id":"2606.18406","title":"CoreMem: Riemannian Retrieval and Fisher-Guided Distillation for Long-Term Memory in Dialogue Agents","created":"2026-06-16","cited_by_count":1,"area":"inference"},{"id":"2607.01241","title":"Mapping Text to Multiplex Graph: Prompt Compression as L\\'evy Walk-Guided Graph Pruning","created":"2026-05-04","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":"2608.30811","title":"TopoCompress: Long Context Compression via Graph-Wired Semantic Trajectories","created":"2026-08-31","cited_by_count":1,"area":"inference"},{"id":"2609.01507","title":"LatentPress: Context Compression Beyond Text and Vision","created":"2026-09-01","cited_by_count":1,"area":"inference"},{"id":"2609.11192","title":"FlexComp: One Model for Every Ratio in Context Compression","created":"2026-09-10","cited_by_count":1,"area":"inference"},{"id":"2609.25537","title":"Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference","created":"2026-09-22","cited_by_count":1,"area":"inference"},{"id":"2409.00855","title":"LanguaShrink: Reducing Token Overhead with Psycholinguistics","created":"2024-09-01","cited_by_count":0,"area":"inference"},{"id":"2502.13374","title":"Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor","created":"2025-02-19","cited_by_count":0,"area":"inference"},{"id":"2603.23528","title":"The Compression Paradox in LLM Inference: Provider-Dependent Energy Effects of Prompt Compression","created":"2026-03-06","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":"2604.15153","title":"Compressing Sequences in the Latent Embedding Space: $K$-Token Merging for Large Language Models","created":"2026-04-16","cited_by_count":0,"area":"inference"},{"id":"2605.11317","title":"SOMA: Efficient Multi-turn LLM Serving via Small Language Model","created":"2026-05-11","cited_by_count":0,"area":"inference"},{"id":"2607.14327","title":"PReM: Learning What to Preserve and When to Refresh for Context Compression","created":"2026-07-15","cited_by_count":0,"area":"inference"},{"id":"2607.17486","title":"SALT: Salience-Aware Lexical Trie for Long-Context Compression","created":"2026-07-20","cited_by_count":0,"area":"inference"},{"id":"2607.19368","title":"Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing","created":"2026-06-12","cited_by_count":0,"area":"inference"},{"id":"2607.25335","title":"Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors","created":"2026-07-28","cited_by_count":0,"area":"inference"},{"id":"2608.04569","title":"Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression","created":"2026-08-05","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"}],"next_cursor":null}