#long-horizon
4 articles
01
·Agents·★ MEMBER·PAPER·15 min read
Paper Walkthrough: PILOT in the Loop — Fixing the Run While It Is Still Running
Agent self-improvement that only starts after execution ends arrives too late. A ground-up walkthrough of PILOT, a supervisor–worker harness that redirects the live run while distilling skills from it, from intuition to equations to measured numbers and stated limits.
02
·Agents·★ MEMBER·PAPER·11 min read
Paper walkthrough: StateM — 95.3% on Terminal-Bench 2.1 and a USD 15 run, without touching a single weight
Long-horizon agents fail even when the model underneath can solve every individual step. StateM leaves the weights alone and hardens only the execution system around the agent, reporting 95.3% on Terminal-Bench 2.1 and a final scoring run billed at roughly USD 15 instead of USD 574.68. A ground-up walkthrough of the harness-scaling bet.
03
·Agents·★ MEMBER·PAPER·10 min read
Paper Walkthrough: MerchantBench — Can an LLM Agent Run an Online Store for a Year? Why It Earns Only 27.3% of What Humans Do
A close read of MerchantBench, which measures the long-term coherence of LLM agents across a 365-day simulation of running an online store. Why the best configuration reaches only 27.3% of the average human's final net worth, read through three failure patterns: decaying activity, premature withdrawal, and policies frozen against the evidence.
04
·Agents·★ MEMBER·PAPER·8 min read
Paper Explained: LongHorizon-Harness — Long-Horizon Agent Tasks Are a State-Management Problem, Not an Execution Problem
Without touching the model at all, keeping task state outside execution and running a Manage-Execute-Audit loop dramatically improves long-horizon task performance — a walkthrough of Alibaba DreamX team's harness-design paper, from analogy to pseudocode.