Robust Failure, Conservative Repair: Textual Knowledge Distillation from Cross-Model Failures
Failure-based textual knowledge distillation aims to discover gaps in a model's knowledge by examining its task errors. The distilled knowledge can be useful for the reasoning of both this model ("source model") and other models. However, this transfer of knowledge may not be stable. We define a rule atom to be a standalone rule injected into a model's textual input at inference time. A rule atom can encode transferable task knowledge or model-specific reasoning patches that can confuse other models. Also, the injected rule atoms can be misapplied to unrelated cases, causing the model to incorrectly flip its answer based on irrelevant information. Building on a pipeline that distills trai...
| Authors | Andrew Ren, Haokun Liu, Chenhao Tan |
|---|---|
| Primary category | cs.AI |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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