# Weight-Space Programming

Variational quantum algorithms normally rely on a classical optimizer to tune gate angles, and every loss evaluation is a run of the quantum device. A study has the frontier model GPT-6 Astra write the angles directly from the problem, circuit and gate conventions, with no loss or gradient returned; on transverse-field Ising chains, H2 and depth-one QAOA its angles match the optimizer, and as a warm start they cut the median optimizer evaluations to best value down to one.

Variational quantum algorithms normally rely on a classical optimizer to tune gate angles, and every loss evaluation is a run of the quantum device. A study has the frontier model GPT-6 Astra write the angles directly from the problem, circuit and gate conventions, with no loss or gradient returned; on transverse-field Ising chains, H2 and depth-one QAOA its angles match the optimizer, and as a warm start they cut the median optimizer evaluations to best value down to one.

Clinical prediction models often arrive as files without the data they were trained on, so an auditor must work out what the model actually computes. Researchers trained tanh networks of 105 to 38,401 weights on simulated subarachnoid haemorrhage cohorts, planted known relationships or a label bias, and asked GPT-6 Astra to answer with no tools, with a calculation-only white-box tool, or with a black-box tool exposing only predictions. Without tools Astra recovered planted relationships in 353-weight networks but became unreliable at 3,457 weights; with the calculator it recovered the relationship in all 29 runs that had one, raised no false alarm in 15 null runs, and black-box matched white-box.

Clinical prediction models often arrive as files without the data they were trained on, so an auditor must work out what the model actually computes. Researchers trained tanh networks of 105 to 38,401 weights on simulated subarachnoid haemorrhage cohorts, planted known relationships or a label bias, and asked GPT-6 Astra to answer with no tools, with a calculation-only white-box tool, or with a black-box tool exposing only predictions. Without tools Astra recovered planted relationships in 353-weight networks but became unreliable at 3,457 weights; with the calculator it recovered the relationship in all 29 runs that had one, raised no false alarm in 15 null runs, and black-box matched white-box.

IWIBench turns gradient-free behavioural weight editing into a scored, controlled task: an editor model gets only four tools for reading and writing weights, running the target and inspecting it, and must make ten kinds of semantic change to Gemma 3 4B IT, after which fresh processes compare target success and control retention before and after the patch. Across ten configurations on the same thirty episodes, 6.1-sol at medium reasoning leads with a macro reward of 0.4601 and Astra follows at 0.4356; no editor scores on structured-response defaults, and only 5.6-terra earns positive reward on answer-length control.

IWIBench turns gradient-free behavioural weight editing into a scored, controlled task: an editor model gets only four tools for reading and writing weights, running the target and inspecting it, and must make ten kinds of semantic change to Gemma 3 4B IT, after which fresh processes compare target success and control retention before and after the patch. Across ten configurations on the same thirty episodes, 6.1-sol at medium reasoning leads with a macro reward of 0.4601 and Astra follows at 0.4356; no editor scores on structured-response defaults, and only 5.6-terra earns positive reward on answer-length control.

An empirical study tests whether a large language model can write the weights of small scoring networks directly, without gradient training and guided only by forward-pass outputs, to speed up exact algorithms such as column generation and branch-and-bound. On synthetic independent-set instances the written branching rule took 0.37 the time of SCIP's default and matched or beat networks trained by imitation of strong branching on the same features; on a bus-driver scheduling model built from Atlanta transit data, the 0.63 time ratio was fully reproduced by SCIP's own plain pseudocost rule at 0.99.

An empirical study tests whether a large language model can write the weights of small scoring networks directly, without gradient training and guided only by forward-pass outputs, to speed up exact algorithms such as column generation and branch-and-bound. On synthetic independent-set instances the written branching rule took 0.37 the time of SCIP's default and matched or beat networks trained by imitation of strong branching on the same features; on a bus-driver scheduling model built from Atlanta transit data, the 0.63 time ratio was fully reproduced by SCIP's own plain pseudocost rule at 0.99.