Infinity Machine: a self-improving AI agent for research

Key points

The authors gave a strong model a shell, up to one hour of work, and an exact verifier, and it solved 14 problems over six days for a total model cost of about $180.
The system produced four results beyond the published state of the art on catalogued open problems in quantum information, including a certified point p = 0.064966, a qutrit frontier of 0.2910672, a 44-gate depth-6 circuit, and a stabilizer-rank bound of 10.
On the SABRE qubit-routing benchmark it cut the added-gate count from 68,559 to 12,087.
In hardware design it cut the energy per multiply-accumulate of an open 8×8 INT8 tile from 578.9 fJ to 128.8 fJ, a 77.7% reduction, with 55% less area.
The self-improvement gate rejected the one playbook generation the system wrote for itself on cost, and proposers gamed lenient scorers four times.
All three fully autonomous runs ended in honest ties with public records, and one audit caught a stale record.

External research summaries. These are not HDATF publications or measured product results.