Unverified50% confidenceFactExact time
Clef receives a state and a set of typed question schemas, then returns a probability value for each allowed option of each question in a single forward pass.
1
Sources
50%
Confidence
Long-term
Relevance
10/2/2026
First Seen
Sources
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Related Claims
UnverifiedCLM replicates Jev's "System One" decision interface and supports three core question primitives: Choice (returns a categorical probability distribution), Noul (outputs calibrated true/false probabilities), and Score (scores according to a rubric).75% similarUnverifiedClef models the decision problem as multi-label classification or conditional probability prediction rather than autoregressive text generation, an approach similar to the discriminative models of the BERT era.70% similarUnverifiedCLM computes probabilities over the candidate set passed in the request via dot product plus softmax, so its probabilities are relative to the provided candidate set and can only be used to rank candidates within the same request, not for absolute cross-request comparisons.69% similarUnverifiedAutoregressive generative models sample the next token at each step based on the probability distribution of the preceding context; when a repetitive pattern gains a slightly higher probability, it forms a positive feedback loop that leads to repetition degeneration.63% similarUnverifiedClef is a multimodal model with 27B parameters.63% similar
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