dnhkng
Joined 3 years ago
Joined 3 years ago
The performance changes are so big with the right harness that is makes sense to engineer the harness and fine-tune the model to one another from the start.
• Terminal Bench: 56.9 → 82.7 (+25.8)
• Toolathlon: 51.8 → 70.3 (+18.5)
Compared to GPT-5.6 Terra:
• Terminal Bench: Flash 82.7 vs Terra 78.4
• Toolathlon: Flash 70.3 vs Terra 53.1
• DeepSWE: Flash 54.4 vs Terra 69.6
• Agents' Last Exam: Flash 25.2 vs Terra 50.4
Trading blows with Terra, which is pretty interesting. No clear winner on these benchmarks, and wildy differeing scores. Very interesting!
Yeah, the encoder and decoder stuff is explicit, but the internal structure in generated during training. I don't think the big labs were doing this back when I did the research; no one was back in '24.
I just didn't get round to publishing for years, because I have a day job.
By the way, it still works! I tested it earlier this year on Qen3.6 and you still see improvements, so either a) no one actually paid attention, or b) it has more room to scale.
curl -X POST https://quantumlever.stream/api/magic-8-ball \ -H "Content-Type: application/json" \ -d '{"question":"Will my sampler touch the multiverse?"}'