Prompt Compression

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
Sep 28, 2026Ali Alfageeh, Rahul Gopinath, Amin AlipourLLM Prompting Few-Shot Prompting
University of Houston · The University of Sydney
Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression
Aug 5, 2026Zhengpei Hu, Kai Li, Dapeng Fu +5LLM Compression Long-Context Inference
School of Computer Technology and Application, Qinghai University · Tsinghua University · Ant Group Security and Intelligence Laboratory (SIL)
Control Under Compression: Reliability Frontiers for Tool-Using Agents
Aug 2, 2026Yinghan Hou, Zongyou YangAI Agent Reliability AI Agent Benchmarks
Department of Electrical and Electronic Engineering Imperial College London London, United Kingdom · Dyson School of Design Engineering Imperial College London London, United Kingdom
Every Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
Jul 28, 2026Jianfei Ma, Zhaoxin Feng, Emmanuele Chersoni +1LLM Compression Efficient Language Model Inference
The Hong Kong Polytechnic University
SALT: Salience-Aware Lexical Trie for Long-Context Compression
Jul 20, 2026Oteo Mamo, Hyunjin Yi, Joydhriti Choudhury +2LLM Compression Long-Context Language Model Inference
Florida State University
Cache-Aware Prompt Compression:A Two-Tier Cost Model for LLM API Caching
Jul 17, 2026Yan SongLLM Serving LLM Inference
PayPal Inc.
Prompt Compression via Activation Aggregation
Jul 9, 2026Thibaud Ardoin, Semira Einsele, Evis Bregu +1LLM Inference Acceleration Representation Geometry in Language Models
Freie Universität Berlin
What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents
Jul 9, 2026Ashwin Gerard Colaco, Nada LahjoujiRate-Distortion Theory LLM Memory
University of California, Irvine Irvine, California, USA
CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression
Jun 23, 2026Morayo Danielle Adeyemi, Ryan A. Rossi, Franck DernoncourtLLM Evaluation LLM Compression
Independent · Adobe Research
Safe to Check, Unsafe to Use: Relinking at the Compression Boundary of LLM Agents
Jun 19, 2026Zesen Liu, Zihan Zhang, Dongdong SheAdversarial Attacks on LLMs LLM Agent Security
The Hong Kong University of Science and Technology
SoftSkill: Behavioral Compression for Contextual Adaptation
Jun 18, 2026Xijia Tao, Yihua Teng, Xinyu Fu +6Prompt Tuning LLM Agent Skill Learning
The University of Hong Kong · Huawei Research · City University of Hong Kong +1
Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Jun 17, 2026Xilun Chen, Shao-Chuan Wang, Baykal Cakici +6Recommender Systems Large Language Model-Based Recommendation
Google · USA
Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Jun 10, 2026Changyue Wang, Weihang Su, Qingyao Ai +5LLM Compression Efficient Language Model Inference
Department of Computer Science and Technology, Tsinghua University
Closing the Social-Semantic Gap: SPSD for Edge-Based Prompt Compression in Cloud LLM Inference
Jun 10, 2026Abhinit Sen, Ajeet Kumar, Manaranjan PradhanLLM Inference Efficiency On-Device Language Model Inference
Indian School of Business, Hyderabad, India
Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing
Jun 2, 2026Mehmet Utku ColakAI Coding Agents LLM Prompting
Department of Computer Engineering, Istanbul Technical University · Istanbul, Turkey
Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migration
May 27, 2026Oleg Grynets, Dmytro Babarytskyi, Vasyl LyashkevychToken Efficiency Prompt Compression
EPAM Systems McLean, Virginia, USA · EPAM Systems Kharkiv, Ukraine · EPAM Systems Lviv, Ukraine
AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents
May 26, 2026Haoran Zhang, Zhaohua SunLLM Compression LLM Agents
AI Agent Technologies (Hong Kong) Limited · Department of Mechanical Engineering, The University of Hong Kong
Prompt Compression in Diffusion Large Language Models: Evaluating LLMLingua-2 on LLaDA
May 18, 2026Sterling Huang, Abigayle Brown, Jiyoo Noh +4LLM Evaluation LLM Compression
University of Toronto Toronto, Canada · King Mongkut’s University of Technology Thonburi Bangkok, Thailand
From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
May 12, 2026Mingcheng Zhu, Zhiyao Luo, Yu Liu +1LLM Compression Clinical Prediction
Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
Telegraph English: Semantic Prompt Compression via Structured Symbolic Rewriting
May 6, 2026Mikhail L. Arbuzov, Sisong Bei, Ziwei Dong +2LLM Compression Prompt Compression
Independent Researcher · Palo Alto Networks
Mapping Text to Multiplex Graph: Prompt Compression as Lévy Walk-Guided Graph Pruning
May 4, 2026Yaxin Gao, Yao Lu, Jinhong Deng +7LLM Compression Long-Context Modeling
Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices
Apr 27, 2026Zihuai Xu, Ruofei Hou, Yang Xu +3Retrieval-Augmented Generation On-Device Language Model Inference
Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China, 215123
A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
Apr 21, 2026Jincheng Ren, Siwei Wu, Yizhi Li +8Terminal Agents Long-Horizon Agent Tasks
University of Manchester · 2MAP · 4HKUST(GZ) +2
Compressing Sequences in the Latent Embedding Space: $K$ -Token Merging for Large Language Models
Apr 16, 2026Zihao Xu, John Harvill, Ziwei Fan +3Token Merging LLM Compression
♢Rutgers University · ♡Mistral AI · ♠AWS AI Labs +3

Sep 28, 2026, cs.SEJ/K move · Enter open · S save

How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs

Ali Alfageeh, Rahul Gopinath, Amin Alipour

University of Houston · The University of Sydney

LLM Prompting Few-Shot Prompting LLM Reliability Prompt Optimization Prompt Compression

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Prompts are the primary mechanism for directing the behavior of large language models (LLMs). Yet the internal structure and causal hierarchy of prompts remain poorly understood: which parts are causally necessary and which are redundant is an open question. This opacity can have severe consequences. Subtle prompt variations can silently shift model outputs in critical software systems, and engineers lack techniques to reason about prompt reliability. We present \framework, a blackbox prompt-minimization framework that reduces few-shot prompts to their necessary minimal subset. We use a case study to apply \framework to a few-shot learning system and demonstrate the insights that this framework can provide. Our experiments show that few-shot exemplars can be reduced by a mean of 65.3%~

\pm

~15.8% in character count while fully preserving propositional output fidelity. The models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations. Our analysis also shows that some models are universal encoders, able to produce highly legible yet minimized prompts, while others are universal decoders, able to interpret minimized prompts from most other models. By identifying which components are indispensable, \framework provides a principled basis for prompt compression and structural analysis of few-shot exemplars.

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