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Adding retained reasoning + compaction to nanobot (model gpt-5.6-sol) raised…

2026-08-02 · 12:28 UTC ·@huang_chao4969 ·0 min read

Brief

Author reports that incorporating retained reasoning plus compaction into the nanobot agent (gpt-5.6-sol) increased solved items on 42 matched OpenBench cells from 31 to 34 (+7.1 pp). The result is presented as a preliminary, non‑statistically‑significant indication that improved context continuity may boost long‑horizon agent performance, with further experiments forthcoming.

Why it matters

Adding retained reasoning + compaction to nanobot (model gpt-5.6-sol) raised solved OpenBench cells from 31/42 to 34/42 (+3 tasks; +7.1 percentage points) on 42 matched cells.

Key details

  • Change was reported as an early, not-yet-statistically-significant signal; authors cite inspiration from OpenAI’s ARC-AGI-3 and plan more experiments.
Source evidence

A promising early signal for long-horizon agents: retained reasoning + compaction improved nanobot from 31/42 to 34/42 solved tasks (+7.1 pp) on matched OpenBench cells.

Not statistically significant yet—but a useful indication that better context continuity can translate into better agent performance. More experiments coming! 🐈🚀

Xubin Ren (@xubinrencs)

Inspired by OpenAI’s ARC-AGI-3 findings, we added retained reasoning + compaction to nanobot.

On 42 matched OpenBench cells, gpt-5.6-sol solved 34/42 after vs 31/42 before (+3; +7.1 pp).
Early signal, not conclusive yet.

openai.com/index/how-two-set…

— https://nitter.net/xubinrencs/status/2083226862566932825#m