Unverified50% confidenceFactExact time
Behaviors learned by LARA can be loaded, removed, blended, or routed during inference while the model itself remains frozen.
1
Sources
50%
Confidence
Long-term
Relevance
9/16/2026
First Seen
Sources
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Related Claims
UnverifiedA frozen model refers to a model whose weight parameters all remain unchanged during the inference phase, with no fine-tuning, adapter training, or online learning performed.75% similarUnverifiedWhen faced with questions outside the training distribution (out-of-distribution queries), models may degrade into guessing based on surface-level patterns.74% similarUnverifiedA model's actual behavior is jointly determined by training weights, system prompts, and runtime context; looking at the prompt alone cannot fully reproduce the complete picture of the product.73% similarUnverifiedOn-device inference adopts the paradigm of 'data stays, model moves': a pre-trained model is downloaded to the local device, inference is completed on the device's NPU or GPU, and input data remains within the secure sandbox.72% similar
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