"TruthForge team on AMD AI League Hackathon"

Conflicting headlines about the same company are often both true: "Intel lost $0.38" (GAAP) and "Intel earned $0.02" (non-GAAP) both quote Intel. Reconciling them with LLMs is expensive and error-prone — an agentic loop burns 5 calls and 46k tokens yet still merges the two figures into a half-truth. This submission solves the problem the other way around: almost everything happens in a deterministic discriminative pipeline (filter → rank → reconcile → guard) that costs about 7 ms and uses zero LLM calls; the LLM is called exactly once, on 2-3 already-validated sources, to produce a reconciled answer with citations — or to refuse when the evidence is insufficient. Measured on a 10-case real-world financial corpus (53 snippets, every URL verified): 1 call and 1,572 tokens in 440 ms vs 5 calls and 46,563 tokens in 30.5 s for the agentic baseline (−96.6% tokens, 69.5× faster). The gate drops 64% of the pool before any LLM call, and a confidence guard makes the generator refuse (INSUFFICIENT) when no primary source supports the winning claim. All numbers are generated from run.json, never hand-written, and protected by a 17-check gate with 77 unit tests.