@shahsubham140230340

Accuracy is gated before token efficiency, so AegisRouter uses a conservative local allowlist. Exact arithmetic, explicit text counting, and single-format extraction can consume zero Fireworks tokens. Mathematical word problems, factual questions, sentiment, constrained summarization, typed NER, code, logic, structured outputs, and uncertain cases escalate. The router combines deterministic features with a trained word/character TF-IDF classifier. Training compares logistic regression against a probability-calibrated linear SVM and selects by validation macro-F1. A sentence-transformer is intentionally not bundled: the TF-IDF artifact is fast on the evaluator's 2-vCPU/4-GB environment and avoids a larger image or model download.

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