Are Agent Harnesses the Boring Way to Continual Learning?
scaling01 · x · 2026-08-14
The ability of DeepSeek Harness (DSH) to allow Agents to dynamically generate and mount plugins at runtime has sparked discussion on whether Agent Harnesses are a "boring but effective" approach to Continual Learning.
Traditional continual learning attempts to make models learn new knowledge without forgetting old knowledge, often facing catastrophic forgetting. The prototype shown by DSH offers a new paradigm: instead of modifying model weights, the Agent temporarily writes code plugins at runtime to acquire new capabilities. Although these dynamically generated plugins currently exist only in memory and disappear on restart, this "build-on-demand" model might bypass the complex issues of weight updates.
More from AGI Musings
- Sabine Hossenfelder fires back: academia's impact metric turned physics into a paper mill — skdh · 2026-10-16
- Researcher credits GPT for multiple accepted papers, incl. disproving Wegner's 1965 conjecture — ctjlewis · 2026-10-16
- dlwh: We optimized the legible version of our goals — and forgot the computation we meant to speed up — dlwh · 2026-10-16
- Ideas no longer have a waiting period: agents can build them as soon as they're articulable — akbirthko · 2026-10-16
- Ex-researcher: labs willing to cheat/distill ship great models faster than those building self-sufficient stacks — _aidan_clark_ · 2026-10-16
- Terry Tao reportedly still expects Riemann Hypothesis proof to be human+AI effort around 2050 — basedjensen · 2026-10-16