Projects / Token Optimization for AI Agents — a Project Idea
Token Optimization for AI Agents — a Project Idea
AI agents burn budget on bloated context. We have screened 232 papers on token optimization into a ready-made reading list — now let's build and benchmark the best ideas.
Lead: Posted by Mapleforge Labs
The problem
Long-running AI agents re-send ever-growing context on every step, so token costs — not model quality — are often what kills a deployment. Dozens of compression and context-management techniques claim big savings; few are compared on the same tasks.
Approach
Start from our screened corpus of 232 papers (ACM, ACL Anthology, arXiv, ScienceDirect) filtered for techniques with a concrete artifact and an experimental evaluation. Pick two or three leading methods, implement them against a shared agent benchmark, and measure the real trade-off: tokens saved versus task success lost. Sponsorship can cover token/API costs for the benchmark runs.