Token Efficiency

Follow this topic Ask about this topic

Token efficiency asks how language models and agent workflows can accomplish the same work with fewer tokens, lowering costs and extending usable context. Approaches include tokenizer redesign, compact structured-data formats, prompt and context management, and pruning or routing agents and harness components. Evaluations compare token counts with language-model loss, task accuracy, latency, semantic fidelity, and performance across languages and benchmarks. A central tension is that shorter representations do not always preserve meaning or predictive quality, and savings can shift costs across languages, models, and workflow stages.

Latest papers

30 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents
Oct 15, 2026Aaron J. Li, Weijia Xu, Chandan Singh +8Token Efficiency Coding Agents
UC Berkeley · Microsoft Research · Microsoft Security
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service
Oct 12, 2026Yuquan Xiao, Qinghe Du, Chaoyang Zhang +4Wireless Communications Token Efficiency
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China. · State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China. · VTT Technical Research Centre of Finland, 02140 Espoo, Finland. +1
Language-Conditioned Token and Reasoning Efficiency in Large Language Models: A Paired Cross-Lingual Study Protocol
Oct 3, 2026Genliang Zhu, Chu WangToken Efficiency Multilingual Language Model Evaluation
Accentrust, Vancouver, Canada · Georgia Institute of Technology, Atlanta, Georgia, USA · University of Illinois Urbana-Champaign, Urbana, Illinois, USA
SHarP: Saliency-based Pruning of Agent Harnesses
Oct 3, 2026Xinyi Gao, Qiucheng Wu, Kaizhi Qian +3Agent Harness Optimization LLM Agent Harnesses
University of California, Santa Barbara · MIT-IBM Watson AI Lab · Adobe Research
Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities
Oct 1, 2026Hyunsik Kim, Youngmoon JungMultilingual Language Models Token Efficiency
Samsung Research
The Invisible Language Tax: Token Premiums of French and Regional Languages in 2026 LLM Tokenizers, and a French-Optimized Prototype
Sep 30, 2026Thomas ServalMultilingual Language Models Token Efficiency
Baracoda AI Labs & NEOMA Business School
The Price of Token Boundaries: Compression Certificates and Prediction
Sep 26, 2026Yuhao Du, Shunian ChenToken Compression Token Efficiency
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
Sep 17, 2026Haozhe Liu, Tian Ye, Sensen Gao +11Agent Harness Optimization LLM Agent Workflow Optimization
NVIDIA · NTU · MIT
Fewer Words, Not Fewer Tokens: Measuring the Sanskrit Tokenization Penalty per Proposition
Sep 11, 2026Devansh SharmaToken Efficiency Low-Resource Language Processing
Independent researcher
How Far Should Tokenization Go? Predictive Effectiveness and Relational Losslessness
Aug 18, 2026Yi WangTokenization Token Efficiency
Department of Electronic Engineering, Tsinghua University
Token Optimization and Context Window Management in Multi-Agent AI Workflows
Aug 17, 2026Dvir ShamayLLM Agent Workflow Optimization LLM Context Management
Independent Research
Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities
Aug 10, 2026Avijit Roy, Proma Roy, Hrishitva PatelAlgorithmic Fairness Token Efficiency
John Jay College of Criminal Justice, City University of New York, USA · The City College of New York, City University of New York, USA · University of Texas at San Antonio, USA
Explicit Boundary Markers for Subword Vocabularies
Aug 9, 2026Sander Land, Clara MeisterToken Efficiency Language Modeling
EPFL
The Best Programming Language for Tokenmaxxing: An Investigation of Coding Agent Behavior Across Programming Languages
Jul 24, 2026Zixuan Wu, Carolyn Jane Anderson, Arjun GuhaAI Coding Agents Code Generation
Northeastern University · Wellesley College
Pixels for Programs? A Cross-Provider Case Study of Input-Token Accounting for Source Code as Text and Images
Jul 23, 2026Ronak BhalgamiImage Tokenization Token Efficiency
Mach-Mind-4-Flash Technical Report
Jul 10, 2026Foundation Model TeamAgentic RL Token Efficiency
Foundation Model, Li Auto Inc.
The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI
Jul 8, 2026Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally +28LLM Inference Efficiency LLM Agent Orchestration
Writer, Inc.
MinGram: A Minimalist Unigram Tokenizer with High Compression and Competitive Morphological Alignment
Jun 25, 2026Sander LandToken Efficiency Subword Tokenization
Writer, Inc.
Token Complexity Theory for AI-Augmented Computing
Jun 10, 2026Jie WangToken Efficiency
Richard Miner School of Computing and Information Sciences, University of Massachusetts, Lowell, MA 01854, USA.
AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models
Jun 10, 2026Quanyan ZhuCost-Aware Inference Token Efficiency
Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, 370 Jay Street, Room 1004, Brooklyn, NY 11201, USA
Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems
May 28, 2026Lorenz Kutschka, Bernhard GeigerTool-Augmented Language Model Agents AI Agent Evaluation
Know Center Research GmbH / Sandgasse 34, A-8010 Graz · Signal Processing and Speech Communication, Graz University of Technology / Inffeldgasse 16c, A-8010 Graz · Graz Center for Machine Learning // A-8010 Graz
BrahmicTokenizer-131K: An Indic-Capable Drop-In Replacement for o200k_base
May 28, 2026Rohan ShravanToken Efficiency Subword Tokenization
The School of AI India
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
Tokenisation via Convex Relaxations
May 21, 2026Jan Tempus, Philip Whittington, Craig W. Schmidt +2Token Efficiency Linear Programming
ETH Zurich · Kensho Technologies
Tokenization with Split Trees
May 21, 2026Craig W. Schmidt, Michael Krumdick, Adam Wiemerslage +4Token Efficiency Subword Tokenization
Kensho Technologies · Ben-Gurion University · MIT Cambridge, MA
Token Economics for LLM Agents: A Dual-View Study from Computing and Economics
May 9, 2026Yuxi Chen, Junming Chen, Chenyu He +9Token Efficiency
College of Computer Science and Technology, Zhejiang University · School of Economics, Zhejiang University · The State Key Laboratory of Blockchain and Data Security, Zhejiang University +1
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
May 9, 2026Yulang Chen, Haoxuan Peng, Jinyan Liu +3Multi-Agent LLM Systems Multi-Agent System Optimization
1Shanghai Jiao Tong University · 2Nanjing University · 3The University of Sydney +2
ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization
Apr 19, 2026Harshavardhanan DeekeswarLLM Inference Efficiency LLM Input Optimization
Independent Researcher · Chennai, India
JTON: A Token-Efficient JSON Superset with Zen Grid Tabular Encoding for Large Language Models
Apr 7, 2026Gowthamkumar NandakishoreToken Efficiency LLM Inference Efficiency
Less Is More: Reducing Token Counts Without Compromising Performance
Jun 18, 2025Gyeongje Cho, Yeonkyoung So, Sangmin Lee +1LLM Inference Acceleration Token Efficiency
Graduate School of Data Science, Seoul National University · Department of Computer Science, Seoul National University

Oct 15, 2026J/K move · Enter open · S save

TokenEconomizer: Experience-Driven Black-Box Cost Control for Coding Agents

Aaron J. Li, Weijia Xu, Chandan Singh, Zixiao Chen+7

UC Berkeley · Microsoft Research · Microsoft Security

Token Efficiency Coding Agents LLM Agent Workflow Optimization

Open paper AskPDF arXiv

Advances in frontier coding agents have sharply increased the token costs of AI-assisted software engineering. Their token efficiency is typically evaluated on isolated tasks, whereas real-world developers use them to continuously build, update, and maintain large-scale projects. This mismatch motivates us to evaluate agents over sequences of realistic coding tasks, where we find that agents exhibit recurring environment-dependent token costs that existing methods struggle to reduce. To minimize these costs, we introduce TokenEconomizer, an external controller that cuts environment-specific token costs over a sequence of tasks. These cuts come from determining (i) which operational micro-skills to inject, (ii) which persistently irrelevant tools to prune, and (iii) when to intervene in unproductive failure loops. Across Claude Code, Codex, and GitHub Copilot CLI paired with frontier models, TokenEconomizer reduces the average token cost by 18.4-30.9% with comparable task success. Savings hold in every repository under every tested agent-model setting and generalize to diverse domains such as sequential web development and security auditing, where costs fall by 6.4-21.2% depending on task structure. Our work establishes adaptive black-box cost control as a promising direction for reducing token costs of coding agents in realistic settings.