CAMELEON introduces a novel framework for AI agents that enhances personalization through interactive learning, suggesting improved adaptability.
We present CAMELEON — a conceptual architecture for AI agents that grow more personalised through lived interaction rather than retraining. The architecture combines emotion-weighted context compression, interpretable domain-expert routing via keyword saliency scoring, speculative context pre-activation, and time-decay relevance scoring...
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Rajkumar Arumugam (2026) studied this question.
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