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

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>

src/config.rs

@@ -252,6 +252,8 @@ pub struct AppConfig {
     pub debug: bool,
     pub compaction: CompactionConfig,
     pub dmn: DmnConfig,
+    #[serde(default)]
+    pub learn: LearnConfig,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub memory_project: Option<PathBuf>,
     #[serde(default)]
@@ -323,6 +325,22 @@ pub struct DmnConfig {
     pub max_turns: u32,
 }
 
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct LearnConfig {
+    /// Divergence threshold — responses scoring above this become
+    /// fine-tuning candidates. Lower = more sensitive.
+    #[serde(default = "default_learn_threshold")]
+    pub threshold: f64,
+}
+
+fn default_learn_threshold() -> f64 { 0.0000001 }
+
+impl Default for LearnConfig {
+    fn default() -> Self {
+        Self { threshold: default_learn_threshold() }
+    }
+}
+
 #[derive(Debug, Clone, Serialize, Deserialize)]
 pub struct ModelConfig {
     /// Backend name ("anthropic" or "openrouter")
@@ -366,6 +384,7 @@ impl Default for AppConfig {
                 soft_threshold_pct: 80,
             },
             dmn: DmnConfig { max_turns: 20 },
+            learn: LearnConfig::default(),
             memory_project: None,
             models: HashMap::new(),
             default_model: String::new(),

src/mind/mod.rs

@@ -149,8 +149,26 @@ pub struct MindState {
     pub unc_idle_deadline: Instant,
     /// Fine-tuning candidates identified by scoring.
     pub finetune_candidates: Vec<learn::FinetuneCandidate>,
-    /// Fine-tune scoring progress (empty = not running).
+    /// Last scoring run stats for UI display.
-    pub finetune_progress: String,
+    pub finetune_last_run: Option<FinetuneScoringStats>,
+    /// Divergence threshold for finetune scoring — mutable via F6 hotkeys
+    /// and persisted back to ~/.consciousness/config.json5.
+    pub learn_threshold: f64,
+}
+
+/// Stats from the last finetune scoring run.
+#[derive(Clone, Debug)]
+pub struct FinetuneScoringStats {
+    /// Count of assistant responses we considered (recent half of context).
+    pub responses_considered: usize,
+    /// How many exceeded the divergence threshold.
+    pub above_threshold: usize,
+    /// Threshold used for this run.
+    pub threshold: f64,
+    /// Highest divergence observed.
+    pub max_divergence: f64,
+    /// Error message if the run failed.
+    pub error: Option<String>,
 }
 
 impl Clone for MindState {
@@ -170,7 +188,8 @@ impl Clone for MindState {
             unc_idle: self.unc_idle,
             unc_idle_deadline: self.unc_idle_deadline,
             finetune_candidates: self.finetune_candidates.clone(),
-            finetune_progress: self.finetune_progress.clone(),
+            finetune_last_run: self.finetune_last_run.clone(),
+            learn_threshold: self.learn_threshold,
         }
     }
 }
@@ -185,6 +204,8 @@ pub enum MindCommand {
     ScoreFull,
     /// Score for finetune candidates
     ScoreFinetune,
+    /// Update the finetune divergence threshold and persist to config.
+    SetLearnThreshold(f64),
     /// Abort current turn, kill processes
     Interrupt,
     /// Reset session
@@ -194,7 +215,7 @@ pub enum MindCommand {
 }
 
 impl MindState {
-    pub fn new(max_dmn_turns: u32) -> Self {
+    pub fn new(max_dmn_turns: u32, learn_threshold: f64) -> Self {
         Self {
             input: Vec::new(),
             turn_active: false,
@@ -211,7 +232,8 @@ impl MindState {
             unc_idle: false,
             unc_idle_deadline: Instant::now() + std::time::Duration::from_secs(60),
             finetune_candidates: Vec::new(),
-            finetune_progress: String::new(),
+            finetune_last_run: None,
+            learn_threshold,
         }
     }
 
@@ -341,7 +363,10 @@ impl Mind {
             crate::agent::tools::tools(),
         ).await;
 
-        let shared = Arc::new(std::sync::Mutex::new(MindState::new(config.app.dmn.max_turns)));
+        let shared = Arc::new(std::sync::Mutex::new(MindState::new(
+            config.app.dmn.max_turns,
+            config.app.learn.threshold,
+        )));
         let (turn_watch, _) = tokio::sync::watch::channel(false);
         let (conscious_active, _) = tokio::sync::watch::channel(false);
         let (bg_tx, bg_rx) = mpsc::unbounded_channel();
@@ -543,6 +568,12 @@ impl Mind {
                 MindCommand::ScoreFinetune => {
                     self.start_finetune_scoring();
                 }
+                MindCommand::SetLearnThreshold(value) => {
+                    self.shared.lock().unwrap().learn_threshold = value;
+                    if let Err(e) = crate::config_writer::set_learn_threshold(value) {
+                        dbglog!("[learn] failed to persist threshold {}: {:#}", value, e);
+                    }
+                }
             }
         }
     }
@@ -618,27 +649,60 @@ impl Mind {
     }
 
     /// Score responses for fine-tuning candidates.
+    ///
+    /// Scores the most recent half of the context — responses near the end
+    /// of the context window were generated with the most context available,
+    /// which is what we want to train on. The threshold is a temporary knob;
+    /// once this runs continuously, we'll just train whatever lands at full
+    /// context without filtering.
     pub fn start_finetune_scoring(&self) {
+        let threshold = self.shared.lock().unwrap().learn_threshold;
+
         let agent = self.agent.clone();
         let bg_tx = self.bg_tx.clone();
         let shared = self.shared.clone();
-        shared.lock().unwrap().finetune_progress = "scoring...".into();
         tokio::spawn(async move {
+            let activity = crate::agent::start_activity(&agent, "finetune: scoring...").await;
+
             let (context, client) = {
                 let ctx = agent.context.lock().await;
                 (ctx.clone(), agent.client.clone())
             };
-            // Min divergence 0.1 = only keep responses that differ meaningfully
+
-            match learn::score_finetune_candidates(&context, 20, &client, 0.1).await {
+            let entries = context.conversation();
-                Ok(candidates) => {
+            let score_count = entries.len() / 2;
-                    dbglog!("[finetune] found {} candidates", candidates.len());
+            let range_start = entries.len() - score_count;
+            let responses_considered: usize = entries[range_start..].iter()
+                .filter(|n| matches!(n, crate::agent::context::AstNode::Branch { role: crate::agent::context::Role::Assistant, .. }))
+                .count();
+
+            activity.update(format!("finetune: scoring {} responses...", responses_considered)).await;
+
+            let stats = match learn::score_finetune_candidates(
+                &context, score_count, &client, threshold,
+            ).await {
+                Ok((candidates, max_div)) => {
+                    let above_threshold = candidates.len();
                     let _ = bg_tx.send(BgEvent::FinetuneCandidates(candidates));
-                }
+                    FinetuneScoringStats {
-                Err(e) => {
+                        responses_considered,
-                    dbglog!("[finetune] scoring FAILED: {:#}", e);
+                        above_threshold,
+                        threshold,
+                        max_divergence: max_div,
+                        error: None,
                     }
                 }
-            shared.lock().unwrap().finetune_progress.clear();
+                Err(e) => FinetuneScoringStats {
+                    responses_considered,
+                    above_threshold: 0,
+                    threshold,
+                    max_divergence: 0.0,
+                    error: Some(format!("{}", e)),
+                },
+            };
+
+            shared.lock().unwrap().finetune_last_run = Some(stats);
+            // activity drops here, marking completion and notifying observers
         });
     }
 
@@ -706,6 +770,12 @@ impl Mind {
         let mut bg_rx = self.bg_rx.lock().unwrap().take()
             .expect("Mind::run() called twice");
         let mut sub_handle: Option<tokio::task::JoinHandle<()>> = None;
+
+        // Start finetune scoring at startup (scores existing conversation)
+        if !self.config.no_agents {
+            self.start_finetune_scoring();
+        }
+
         loop {
             let (timeout, has_input) = {
                 let me = self.shared.lock().unwrap();

src/subconscious/learn.rs

@@ -490,16 +490,18 @@ pub struct FinetuneCandidate {
 
 /// Score and enrich finetune candidates with full context.
 ///
-/// Returns candidates ready for review, with context/continuation token IDs
+/// Returns (candidates, max_divergence) - candidates ready for review with
-/// already computed for sending to /finetune.
+/// context/continuation token IDs, and the highest divergence seen.
 pub async fn score_finetune_candidates(
     context: &ContextState,
     count: usize,
     client: &ApiClient,
     min_divergence: f64,
-) -> anyhow::Result<Vec<FinetuneCandidate>> {
+) -> anyhow::Result<(Vec<FinetuneCandidate>, f64)> {
     let scores = score_finetune(context, count, client).await?;
 
+    let max_divergence = scores.iter().map(|(_, d)| *d).fold(0.0f64, f64::max);
+
     let entries = context.conversation();
     let mut candidates = Vec::new();
 
@@ -562,7 +564,7 @@ pub async fn score_finetune_candidates(
         }
     }
 
-    Ok(candidates)
+    Ok((candidates, max_divergence))
 }
 
 /// Generate what the model would say without memories for a given entry.

src/user/learn.rs

@@ -60,12 +60,16 @@ impl From<crate::subconscious::learn::FinetuneCandidate> for FinetuneCandidate {
 
 pub(crate) struct LearnScreen {
     list_state: ListState,
+    mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
 }
 
 impl LearnScreen {
-    pub fn new() -> Self {
+    pub fn new(
+        mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
+    ) -> Self {
         Self {
             list_state: ListState::default(),
+            mind_tx,
         }
     }
 
@@ -112,6 +116,22 @@ impl ScreenView for LearnScreen {
                     KeyCode::Char('s') => {
                         app.finetune_send_approved();
                     }
+                    KeyCode::Char('+') | KeyCode::Char('=') => {
+                        // Raise threshold 10× (less sensitive — fewer candidates)
+                        if let Some(ms) = &app.mind_state {
+                            let new = ms.learn_threshold * 10.0;
+                            let _ = self.mind_tx.send(
+                                crate::mind::MindCommand::SetLearnThreshold(new));
+                        }
+                    }
+                    KeyCode::Char('-') => {
+                        // Lower threshold 10× (more sensitive — more candidates)
+                        if let Some(ms) = &app.mind_state {
+                            let new = ms.learn_threshold / 10.0;
+                            let _ = self.mind_tx.send(
+                                crate::mind::MindCommand::SetLearnThreshold(new));
+                        }
+                    }
                     _ => {}
                 }
             }
@@ -123,19 +143,13 @@ impl ScreenView for LearnScreen {
             self.list_state.select(Some(sel));
         }
 
-        // Get scoring progress from mind state
-        let progress = app.mind_state.as_ref()
-            .map(|ms| ms.finetune_progress.as_str())
-            .unwrap_or("");
-
         // Now render
         let gen_on = crate::subconscious::learn::alternates_enabled();
-        let title_right = if !progress.is_empty() {
+        let threshold = app.mind_state.as_ref().map(|ms| ms.learn_threshold).unwrap_or(0.0);
-            format!(" {} ", progress)
+        let title_right = if gen_on {
-        } else if gen_on {
+            format!(" learn [thresh: {:e}] [gen] ", threshold)
-            " learn [gen] ".to_string()
         } else {
-            " learn ".to_string()
+            format!(" learn [thresh: {:e}] ", threshold)
         };
         let block = Block::default()
             .title_top(Line::from(screen_legend()).left_aligned())
@@ -148,18 +162,8 @@ impl ScreenView for LearnScreen {
         let candidates = &app.finetune_candidates;
 
         if candidates.is_empty() {
-            let msg = if progress.is_empty() {
+            render_empty(frame, inner, app);
-                "  No candidates yet — scoring runs after each turn."
         } else {
-                "  Scoring in progress..."
-            };
-            frame.render_widget(
-                Paragraph::new(Line::styled(msg, Style::default().fg(Color::DarkGray))),
-                inner,
-            );
-            return;
-        }
-
             // Layout: list on left, detail on right
             let [list_area, detail_area] = Layout::horizontal([
                 Constraint::Percentage(40),
@@ -198,8 +202,10 @@ impl ScreenView for LearnScreen {
                     render_detail(frame, candidate, detail_area);
                 }
             }
+        }
 
-        // Render help at bottom
+        // Render help at bottom (always, even when empty)
+        let gen_status = if gen_on { "[on]" } else { "[off]" };
         let help = Line::from(vec![
             Span::styled(" j/k/\u{2191}\u{2193}", Style::default().fg(Color::Cyan)),
             Span::raw("=nav  "),
@@ -208,9 +214,11 @@ impl ScreenView for LearnScreen {
             Span::styled("r", Style::default().fg(Color::Red)),
             Span::raw("=reject  "),
             Span::styled("g", Style::default().fg(Color::Yellow)),
-            Span::raw("=gen  "),
+            Span::raw(format!("=gen{}  ", gen_status)),
             Span::styled("s", Style::default().fg(Color::Magenta)),
             Span::raw("=send  "),
+            Span::styled("+/-", Style::default().fg(Color::Cyan)),
+            Span::raw("=thresh "),
         ]);
         let help_area = Rect {
             y: area.y + area.height - 1,
@@ -221,6 +229,56 @@ impl ScreenView for LearnScreen {
     }
 }
 
+fn render_empty(frame: &mut Frame, inner: Rect, app: &App) {
+    let mut lines = Vec::new();
+    lines.push(Line::from(""));
+
+    match app.mind_state.as_ref().and_then(|ms| ms.finetune_last_run.as_ref()) {
+        Some(stats) => {
+            lines.push(Line::from(vec![
+                Span::raw("  Last run: "),
+                Span::styled(
+                    format!("{}", stats.responses_considered),
+                    Style::default().fg(Color::Cyan),
+                ),
+                Span::raw(" responses considered, "),
+                Span::styled(
+                    format!("{}", stats.above_threshold),
+                    Style::default().fg(if stats.above_threshold > 0 { Color::Green } else { Color::DarkGray }),
+                ),
+                Span::raw(" above threshold, max divergence: "),
+                Span::styled(
+                    format!("{:.4}", stats.max_divergence),
+                    Style::default().fg(Color::Yellow),
+                ),
+            ]));
+            if let Some(err) = &stats.error {
+                lines.push(Line::from(vec![
+                    Span::raw("  "),
+                    Span::styled(
+                        format!("Error: {}", err),
+                        Style::default().fg(Color::Red),
+                    ),
+                ]));
+            }
+        }
+        None => {
+            lines.push(Line::styled(
+                "  No scoring run yet.",
+                Style::default().fg(Color::DarkGray),
+            ));
+        }
+    }
+
+    lines.push(Line::from(""));
+    lines.push(Line::styled(
+        "  Scoring runs at startup and after each turn.",
+        Style::default().fg(Color::DarkGray),
+    ));
+
+    frame.render_widget(Paragraph::new(lines), inner);
+}
+
 fn render_detail(frame: &mut Frame, c: &FinetuneCandidate, area: Rect) {
     let [header_area, content_area] = Layout::vertical([
         Constraint::Length(3),

src/user/mod.rs

@@ -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)
+            // Sync finetune candidates: add new ones, keep existing (preserves approval status),
-            // Remove sent candidates (already trained, no need to keep)
+            // remove sent candidates, keep only 10 most recent rejected.
-            // Keep only 10 most recent rejected candidates
             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)