Show HN: Sup AI, a confidence-weighted ensemble (52.15% on Humanity's Last Exam)

I want to clarify what Ken meant by "entropy in the output token probability distributions." Whenever an LLM outputs a token, it's choosing that token out of all possible tokens. Every possible output token has a probability assigned by the model (typically a logarithm of the probability). This is a probability distribution (the output token probabilities sum to 1). Entropy is a measure of uncertainty and can quantify if a token probability distribution is certain (1 token has a 99.9% probability, and the rest share the leftover 0.1% probability) or uncertain (every token has roughly the same probability, so it's pretty much random which token is selected). Low entropy is the former case, and high entropy is the latter.

There is interesting research in the correlation of entropy with accuracy and hallucinations:

- https://www.nature.com/articles/s41586-024-07421-0

- https://arxiv.org/abs/2405.19648

- https://arxiv.org/abs/2509.04492 (when only a small number of probabilities are available, which is something we frequently deal with)

- https://arxiv.org/abs/2603.18940

- tons more, happy to chat about if interested

Impressive result on HLE if the methodology holds up. One thing I'd want to understand better: how much of the gain comes from the entropy weighting specifically vs. simply having more compute via parallel inference? Would be curious to see an ablation — same models, same budget, but with naive majority voting instead. That would isolate the actual contribution of your confidence-weighting approach.

Do you have data for other benchmarks? +7% for HLE isn't nothing but it'd be more compelling if you could show you're consistently doing better with your method across more domains (especially coding, which seems like the primary use-case these days).

Is 7 extra percent on HLE benchmark really worth the cost of running an entire ensemble of models?

I use gemini and cursor for enterprise software implementation, but they often suggest incorrect solutions to edge cases and unique config requirements. An AI that has a higher likelihood of being accurate is very appealing. I'll give Sup AI at try over the next few days at work.

Also, discovering HLE was great... scrolling through some of the questions brings back memories of college organic chem.

Ensembling usually hits a wall at latency and cost. Running these in parallel is table stakes, but how are you handling the orchestration layer overhead when one provider (e.g., Vertex or Bedrock) spikes in P99 latency? If you're waiting for the slowest model to get entropy stats, the DX falls off a cliff. Are you using speculative execution or a timeout/fallback strategy to maintain a responsive ttft?

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