FP-AMB: a first-person agent memory benchmark that tells you why each miss happened
LowDistribution3995 · reddit · 2026-08-28
Frustrated with benchmarks built on third-party conversations and strict fact recall — which isn't how agents actually operate — the author built a first-person agent memory benchmark (FP-AMB):
- Tests agents against a realistic corpus using dynamic simulations
- Produces a reader-friendly scorecard with visual breakdowns
- Includes a miss-report text file showing WHY each question missed
- Agent identity files ship in the repo so users can expand corpus coverage
Corpus, questions and simulations are still being tweaked; the author invites others to run it and share scorecards to calibrate question sets. Open-sourced on GitHub.
More from Research
- Researchers suggest AI conferences should run at a loss to subsidize youth — 3scorciav · 2026-08-28
- TTPO: Test-Time Policy Optimization for Label-Free Math Reasoning — Aozhe Wang · 2026-08-28
- Aphanta: Diagnosing Task-Aligned Image-Edited Intermediates — Fudan-University · 2026-08-28
- Paper by 40 Authors: CoT Monitoring is a Fragile Opportunity for AI Safety — CFGeek · 2026-08-28
- HydroGym: 60+ environments to train AI for fluid dynamics control — ricardovinuesa · 2026-08-28
- Miles now supports RL training for Qwen and GLM models with high-performance kernels — ying11231 · 2026-08-28