Autoresearch · 5 August 2026 · 2 min · Original language: English
Tailnet Realtime Verification
The verification is only partial. The evidence shows an RTX 3090-profile evaluation producing ordered experiment lifecycle events, but it does not prove that those events traveled through private Tailscale HTTPS or SSE, does not include actual GPU telemetry readings, and does not demonstrate that the main worktree remained unchanged.
Measured improvement6.6% better
Started
1.2500
Best
1.1678
Best-so-far result across 27 recorded experiments. Lower text prediction loss is better. Original research record
What the run established
The recorded sequence repeatedly progresses from experiment start, through change application and GPU evaluation, to completion and acceptance or rejection. That confirms the application generated the realtime-style events needed by a live client. It also shows GPU evaluations were initiated under the runner-managed RTX 3090 profile.
However, these records contain neither connection details nor transport-level observations. They therefore cannot distinguish private Tailscale HTTPS/SSE delivery from events captured through another path. The GPU messages also warn that utilization can briefly be low during setup, compilation, and validation, but provide no utilization, memory, temperature, power, or device-identity readings.
What the measured results mean
The prediction loss improved from 1.249961 to 1.167836—a reduction of 0.082125, or about 6.57%. The progress graph should show that most of this improvement came early from reducing the optimizer batch: 262,144 tokens reached 1.178659, and the intermediate 196,608-token batch improved that to 1.172064.
At that batch size, extending the Muon momentum ramp from 300 to 800 optimizer steps produced the next meaningful gain, reaching 1.168144. Raising the output-projection learning rate from 0.004 to 0.005 and increasing weight decay from 0.20 to 0.25 then delivered small final improvements, ending at 1.167836.
The rejected trials make the useful boundaries fairly clear. Smaller batches degraded loss to roughly 1.182, while moving weight decay above or below 0.25 was worse. Changes to warmup, cooldown, embedding learning rate, Adam momentum settings, attention-window pattern, matrix learning rate, and attention-gate width also failed to beat the incumbent. Experiment 27, which reduced the cooldown ratio to 0.45, was stopped before producing a result.
What is still required for a complete verification
A conclusive check still needs evidence from a deliberately isolated short run showing:
the client connected to the private Tailscale HTTPS endpoint and received events specifically through SSE;
live RTX 3090 telemetry values were delivered during GPU execution, including at least device identity and utilization or memory usage;
the connection stayed private and HTTPS validation succeeded;
the main worktree was clean before and after the run, or the run occurred in a documented separate worktree;
only the intended short verification run was exercised.
The last point matters because the supplied evidence describes 27 experiments rather than one short verification run. Based on this JSON alone, the optimization result is valid, but the requested transport, telemetry, and worktree-isolation verification remains incomplete.