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Advancing Human Understanding

Exploring how advances in intelligence can expand what people understand, discover and solve.

Result · SENSUS Bench v1.0A 27B open model trained on our data leads SENSUS Bench
  1. Qwen3.8-27B + Lorm data0.838
  2. GPT-6 Sol0.525
  3. Opus 5.5*0.461
  4. Same base, untuned0.290
2.9× its untuned base. Mean checklist score on 76 evaluation cases excluded from training, two model graders. *Alternate first grader.Full leaderboard →

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SENSUS Bench v1.0

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Our first benchmark asks whether a model can follow how a person is reasoning and reply in a way that helps. On it, every model trained on our open data scores above the general-purpose models we tested, and the same 27B base goes from 0.290 untuned to 0.838 after training.

Mean checklist score, 0 to 1. Higher is better.

0.000.250.500.751.00Qwen3.8-27B (Lorm)0.838Qwen3-14B (Lorm)0.720Qwen3-8B (Lorm)0.626GPT-6 Sol0.525Opus 5.5*0.461Nemotron 3 Ultra (NVIDIA)0.356Kimi K30.331Qwen3.8-27B (untuned)0.290
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SENSUS Bench v1.0 leaderboard, 76 recorded evaluation cases. Best value in each column in bold.
Qwen3.8-27B (Lorm)0.83824/760/76
Qwen3-14B (Lorm)0.72014/763/76
Qwen3-8B (Lorm)0.6267/7611/76
GPT-6 Sol0.5250/764/76
Opus 5.5*0.4610/7618/76
Nemotron 3 Ultra (NVIDIA)0.3560/7619/76
Kimi K30.3310/7628/76
Qwen3.8-27B (untuned)0.2900/7626/76

Lorm rows: the released 1,015-conversation recipe, latest completed training run. * Opus 5.5 was assessed with an alternate first grader, which limits direct comparison. Task-specific checklist scores on historical cases, not overall model rankings.

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