nwhitehouse rebuilds legal research as a five-stage pipeline
Instead of one lookup and an answer, nwhitehouse's fork now runs a structured research loop across legal and web sources, automatically.
When a user turns on legal or web sources, the fork stops doing a single search and instead kicks off a multi-step process behind the scenes. It rewrites the question into a handful of sharper sub-queries, searches several public legal databases at once - court opinions, federal regulations, the Federal Register and the like, plus general web results - ranks what comes back, then writes a short tailored summary of each top result before composing a final answer. The interesting bet is that extra step: the model digests each source into a clean two-or-three-sentence extract before synthesising, trading more model calls for higher-quality inputs and, in theory, fewer errors. To keep that from running away, nwhitehouse added hard limits on both how many calls and how many seconds a single research run can consume; if it hits the ceiling, it returns what it has rather than failing. Users can also expand a panel to watch the model's reasoning.
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