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learn: F6 screen — scoring stats, ActivityGuard, configurable threshold

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Three changes that together reshape the F6 fine-tune-review screen:

1. Finetune scoring reports through the standard agent activity system
   instead of a separate finetune_progress String. The previous design
   ran an independent progress field that forced a cross-lock dance and
   bespoke UI plumbing. start_finetune_scoring now uses start_activity
   + activity.update, so the usual status line and notifications
   capture scoring progress uniformly with other background work.

2. MindState gains a FinetuneScoringStats snapshot (responses seen,
   above threshold, max divergence, error). The F6 empty screen shows
   this instead of a loading message — so after a scoring run that
   produced zero candidates, you can see *why* (e.g., max_divergence
   below threshold).

3. The divergence threshold is configurable from F6 via +/- hotkeys
   (scales by 10×) and persisted to ~/.consciousness/config.json5 via
   config_writer::set_learn_threshold. AppConfig grows a learn section
   with a threshold field (default 1e-7).

Also: user/mod.rs no longer uses try_lock() for the per-tick
unconscious/mind state sync — we fixed the locking hot paths that
made try_lock necessary, so lock().await is now the right choice.
And subconscious::learn::score_finetune_candidates now returns
(candidates, max_divergence) so the stats can be populated.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
This commit is contained in:
Kent Overstreet 2026-04-16 11:49:26 -04:00
parent ac40c2cb98
commit e5dd8312c7
5 changed files with 237 additions and 85 deletions
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15
src/user/mod.rs
View file

@ -389,7 +389,7 @@ async fn run(
Box::new(crate::user::subconscious::SubconsciousScreen::new()),
Box::new(crate::user::unconscious::UnconsciousScreen::new()),
Box::new(crate::user::thalamus::ThalamusScreen::new()),
Box::new(crate::user::learn::LearnScreen::new()),
Box::new(crate::user::learn::LearnScreen::new(mind_tx.clone())),
];
let mut active_screen: usize = 1; // F-key number
tui::set_screen_legend(tui::screen_legend_from(&*screens));
@ -466,7 +466,8 @@ async fn run(
idle_state.decay_ewma();
app.update_idle(&idle_state);
app.agent_state = mind.subconscious_snapshots().await;
if let Ok(mut unc) = mind.unconscious.try_lock() {
{
let mut unc = mind.unconscious.lock().await;
let toggles: Vec<String> = app.agent_toggles.drain(..).collect();
for name in &toggles {
if mind.subconscious.lock().await.toggle(name).is_none() {
@ -480,10 +481,13 @@ async fn run(
};
app.unconscious_state = unc.snapshots(store_guard.as_deref());
app.graph_health = unc.graph_health.clone();
}
// Sync mind state (finetune candidates, last scoring run, etc.)
{
let ms = mind.shared.lock().unwrap();
// Sync finetune candidates: add new ones, keep existing (preserves approval status)
// Remove sent candidates (already trained, no need to keep)
// Keep only 10 most recent rejected candidates
// Sync finetune candidates: add new ones, keep existing (preserves approval status),
// remove sent candidates, keep only 10 most recent rejected.
app.finetune_candidates.retain(|c| c.status != learn::CandidateStatus::Sent);
for c in &ms.finetune_candidates {
let exists = app.finetune_candidates.iter()
@ -492,7 +496,6 @@ async fn run(
app.finetune_candidates.push(learn::FinetuneCandidate::from(c.clone()));
}
}
// Limit rejected candidates to 10 most recent
let mut rejected: Vec<_> = app.finetune_candidates.iter()
.enumerate()
.filter(|(_, c)| c.status == learn::CandidateStatus::Rejected)