Isaac framework boosts model performance by reusing cross-domain experience at inference time
机器之心 · wechat · 2026-08-26
Addressing the reliance on external scaffolding, the team proposes "Inspirational Learning" and the inference-side framework Isaac. The core is injecting cross-domain experience during inference without modifying base weights.
Technical Paths:
- Prompt Injection (DIN): Identifies domain-invariant neurons to retrieve structurally aligned demonstrations in a subspace.
- Network Layer Injection (CoDA): Adds a lightweight adapter in the middle layer to modulate hidden states via residuals, aligning source and target domain distributions.
Results:
Significant gains were observed on HumanEval, StrategyQA, ScienceQA, and SciCode. For instance, Claude reached 100% on HumanEval, and Qwen3-8B reached 82.3% on StrategyQA. Weaker models often saw larger absolute improvements.
This approach aims to internalize model cognition for self-evolution.
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