LLM Input Optimization

LLM: Large Language Model

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28 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

EffComp: Efficient Prompt Compression via Hybrid Reinforcement & Supervised Learning
Nov 2, 2026Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato +1Prompt Compression LLM Input Optimization
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand · Google, United States
A Compact Formula-Aware Spreadsheet Abstraction for Large Language Models
Oct 30, 2026Tie ChengSpreadsheet Automation LLM Input Optimization
SAS Matrix Lead, France
Understanding When Reflective Prompt and Skill Optimization Helps on Locally Served Reasoning Models: A Two-Model Study of GEPA and SkillOpt
Oct 19, 2026Andrea Giummarra, Carmelo Baglieri, Antonio Di Maria +3LLM Input Optimization Large Language Model-Guided Optimization
Evision srl, Ragusa, Italy · Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
ESPO: Error-Structured Prompt Optimization via Diagnose, Diversify, and Stabilize
Sep 3, 2026Lihao Liu, Peng Tang, Kunwar Yashraj Singh +1Evolutionary Optimization Large Language Model-Guided Optimization
AWS Agentic AI
SEPO: Evidence-Grounded Prompt Optimization via Structural Editing
Aug 28, 2026Xiaoyu Ma, Haoyue Liu, Yiwen Li +4Automatic Prompt Optimization Prompt Optimization
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen · Xi’an Jiaotong University · Shenzhen Future Network of Intelligence Institute (FNiI-Shenzhen) +1
RLMOpt: Adaptive Prompt Optimization via Recursive Language Models
Aug 11, 2026Subhash Bangalore Satheesha, Nirvik Pande, Deepthi Duddempudi +1Large Language Model-Guided Optimization LLM Agents
Autonomize AI · Carnegie Mellon University
Moral Hazard in Multi-Agent Language Models
Jul 27, 2026Dane MalenfantLLM Input Optimization LLM Prompting
School of Computer Science · McGill University · Mila - The Québec AI Institute
Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems
Jun 12, 2026Tan Zhu, Tong Yao, Kananart Kuwaranancharoen +4Multi-Agent LLM Systems LLM Input Optimization
Retail Intelligence, Walmart Global Tech, Sunnyvale, CA, USA
Navigating LLM Valley: From AdamW to Memory-Efficient and Matrix-Based Optimizers
May 9, 2026Aditya RanganathDeep Learning Optimization LLM Input Optimization
Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore, CA, USA
MoLS: SNR-Guided Module-Wise Learning-Rate Calibration for Large Language Models
May 7, 2026Ziqing Wen, Zhouyang Liu, Jiahuan Wang +5Deep Learning Optimization LLM Input Optimization
College of Computer Science and Technology, National University of Defense Technology, Changsha, Hunan, China
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
When Correct Isn't Usable: Improving Structured Output Reliability in Small Language Models
May 4, 2026Cosimo Galeone, Minsu Park, Giuseppe Ettorre +1LLM Evaluation LLM Input Optimization
Alomana, Grottaglie, Italy
Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading
Apr 30, 2026Nicholas Sadjoli, Tim Siefken, Atin Ghosh +2LLM Evaluation Language Model Generation Evaluation
SAP · Stanford University
Semantic Density Effect (SDE): Maximizing Information Per Token Improves LLM Accuracy
Apr 19, 2026Amr AhmedLLM Prompting Prompt Optimization
Public Health Department, Riyadh First Health Cluster · Ministry of Health, Saudi Arabia
ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization
Apr 19, 2026Harshavardhanan DeekeswarLLM Inference Efficiency LLM Input Optimization
Independent Researcher · Chennai, India
Self-Evolving LLM Memory Extraction Across Heterogeneous Tasks
Apr 13, 2026Yuqing Yang, Tengxiao Liu, Wang Bill Zhu +3LLM Input Optimization LLM Memory
University of Southern California · University of California, Santa Barbara
SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization
Apr 9, 2026Wooin Lee, Hyun-Tae KimLLM Input Optimization Memory-Efficient Optimization
London, United Kingdom · Seoul, Republic of Korea
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs
Apr 8, 2026Haoyue Liu, Zhichao Wang, Yongxin Guo +2LLM Input Optimization LLM Prompting
The Chinese University of Hong Kong, Shenzhen · Taobao and Tmall Group
Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
Apr 5, 2026Hanchen Li, Runyuan He, Qizheng Zhang +11LLM Input Optimization LLM Prompting
UC Berkeley · Stanford University · Independent +1
Do We Need Frontier Models to Verify Mathematical Proofs?
Apr 2, 2026Aaditya Naik, Guruprerana Shabadi, Rajeev Alur +1LLM Input Optimization Reasoning Verification
University of Pennsylvania, U.S.A.
RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models
Mar 27, 2026Rahul SoniLLM Input Optimization LLM Prompting
Independent Researcher
Enhancing LLM Training via Spectral Clipping
Mar 15, 2026Xiaowen Jiang, Andrei Semenov, Sebastian U. StichLLM Input Optimization Spectral Regularization
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Universität des Saarlandes, Saarbrücken, Germany · EPFL
NuMuon: Nuclear-Norm-Constrained Muon for Compressible LLM Training
Mar 4, 2026Hadi Mohaghegh Dolatabadi, Thalaiyasingam Ajanthan, Sameera Ramasinghe +7LLM Input Optimization LLM Compression
Pluralis Research
How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding
Feb 23, 2026Wei Chen, Guoyang Ju, Yuanyuan QiLLM Input Optimization LLM Prompting
China Jiliang University, Hangzhou, China
GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer
Feb 3, 2026Junmo Cho, Suhan Kim, Sangjune An +5LLM Input Optimization
Korea Advanced Institute of Science and Technology (KAIST) · Korea University · Mila – Québec AI Institute
BudgetMem: Training-Free Selective Memory for Cost-Efficient Long-Context Processing in Language Models
Nov 7, 2025Chandra Vamsi Krishna Alla, Harish Naidu Gaddam, Manohar Kommi +1Memory-Efficient Optimization LLM Compression
University of Texas at Arlington
Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization
Aug 8, 2025Fei Xu Yu, Gina Adam, Nathaniel D. Bastian +1Natural Language to Optimization Modeling Large Language Model-Guided Optimization
The George Washington University · United States Military Academy
Budgeted Prompt-Set Expansion and Refinement for Language Models via Simulation Optimization
Apr 12, 2024Haoting Zhang, Jinghai He, Rhonda Righter +1Prompt Optimization LLM Prompting
Department of Industrial Engineering and Operations Research, University of California Berkeley

Nov 2, 2026J/K move · Enter open · S save

EffComp: Efficient Prompt Compression via Hybrid Reinforcement & Supervised Learning

Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato, Peerapon Vateekul

Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Thailand · Google, United States

Prompt Compression LLM Input Optimization RAG Context Compression

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Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher-quality LLM predictions. However, existing prompt compression methods are often limited by modest compression gains, a risk of hallucination, and/or high compression latency. We propose EffComp, an Efficient prompt Compression framework for RAG-based question answering (QA). EffComp employs BERT-style document reranker and sentence selector models to allow fast extractive prompt compression at the sentence level. Its extractive nature prevents hallucinations in the compressed prompts. Additionally, the training process using a hybrid reinforcement and supervised learning approach is designed to optimize the compression ratio while preserving LLM accuracy. Experiments on various QA datasets demonstrate that EffComp outperforms state-of-the-art prompt compression methods achieving the best trade-off between compression ratio and accuracy with minimal latency. This makes it practical for real-world applications.