ZTC
A classification and decision paradigm based on embedding similarity. Decisions are made by computing the cosine similarity between a query embedding and candidate option embeddings, generating no tokens in the process. The approach is characterized by high determinism and low computational cost.
Timeline (last 90 days)
The ZTC decision engine directly reads a certain aggregation vector after only a single forward pass, outputs a classification judgment, and produces no text tokens at all, belonging to discriminative models.
The author calls this local form ZTC (Zero-Token Confidence).
VIDRAFT Darwin-27B-ZTC-v2 is an open-weight model that uses ZTC (Zero-Token Confidence) to score decisions, generating no tokens during the decision process.
ZTC completes decisions in a single forward pass by applying calibrated probes to the model's final hidden state to produce results.
Because no tokens are generated, ZTC's decision outputs are deterministic and inexpensive.
All Facts (5)
The ZTC decision engine directly reads a certain aggregation vector after only a single forward pass, outputs a classification judgment, and produces no text tokens at all, belonging to discriminative models.
50%UnverifiedThe author calls this local form ZTC (Zero-Token Confidence).
50%UnverifiedVIDRAFT Darwin-27B-ZTC-v2 is an open-weight model that uses ZTC (Zero-Token Confidence) to score decisions, generating no tokens during the decision process.
50%UnverifiedZTC completes decisions in a single forward pass by applying calibrated probes to the model's final hidden state to produce results.
50%UnverifiedBecause no tokens are generated, ZTC's decision outputs are deterministic and inexpensive.
50%