SourceHat RLCD is a research model for bounded decisions: commit to one complete object, return alternatives, or abstain. A frozen Qwen3.5-9B text backbone represents context, evidence, and supplied choices in one shared forward pass. Small learned heads score the choices and their combinations; a calibrated policy selects an operating mode. This is not a conversational model or an arbitrary JSON generator. It is designed for classification and routing experiments where the caller defines the candidate choices, validates the result, and retains a verifier or human fallback. It executes no external action. On the main synthetic test, 46 of 49 commitments were correct, out of 250 total cases…
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Local LLM inference, quantization, model evaluation, and the trade-offs between quality, speed, memory, and hardware.
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