From fd58386951ca48aebcb6a3a7d8bc288ea75d41b4 Mon Sep 17 00:00:00 2001
From: Kent Overstreet <kent.overstreet@linux.dev>
Date: Tue, 7 Apr 2026 21:10:09 -0400
Subject: [PATCH] Incremental memory scoring with per-score persistence
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score_memories_incremental now takes an async callback that fires
after each memory is scored. The callback:
- Writes the score to the conversation entry via set_score()
- Persists to memory-scores.json immediately
- Notifies the UI so the context screen updates live

Scoring no longer batches — each score is visible and persisted
as it completes. Does not touch the memory store.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
---
 src/mind/mod.rs           | 32 +++++++++++++++++---------------
 src/subconscious/learn.rs | 16 +++++++++++-----
 2 files changed, 28 insertions(+), 20 deletions(-)

diff --git a/src/mind/mod.rs b/src/mind/mod.rs
index 90c711a..9bf9677 100644
--- a/src/mind/mod.rs
+++ b/src/mind/mod.rs
@@ -396,26 +396,28 @@ impl Mind {
                 ag.memory_scoring_in_flight = true;
                 (ag.context.clone(), ag.client_clone())
             };
-            let result = learn::score_memories_incremental(
+            let _result = learn::score_memories_incremental(
                 &context, max_age as i64, response_window, &client, &agent,
+                |key: String, score: f64| {
+                    let agent = agent.clone();
+                    let path = scores_path.clone();
+                    async move {
+                        let mut ag = agent.lock().await;
+                        for i in 0..ag.context.conversation.len() {
+                            if let ConversationEntry::Memory { key: k, .. } = &ag.context.conversation.entries()[i].entry {
+                                if *k == key {
+                                    ag.context.conversation.set_score(i, Some(score));
+                                }
+                            }
+                        }
+                        save_memory_scores(&ag.context.conversation, &path);
+                        ag.changed.notify_one();
+                    }
+                },
             ).await;
             {
                 let mut ag = agent.lock().await;
                 ag.memory_scoring_in_flight = false;
-                if let Ok(ref scores) = result {
-                    // Write scores onto Memory entries
-                    for (key, weight) in scores {
-                        for i in 0..ag.context.conversation.len() {
-                            if let ConversationEntry::Memory { key: k, .. } = &ag.context.conversation.entries()[i].entry {
-                                if k == key {
-                                    ag.context.conversation.set_score(i, Some(*weight));
-                                }
-                            }
-                        }
-                    }
-                    // Persist all scores to disk
-                    save_memory_scores(&ag.context.conversation, &scores_path);
-                }
             }
             let _ = bg_tx.send(BgEvent::ScoringDone);
         });
diff --git a/src/subconscious/learn.rs b/src/subconscious/learn.rs
index b6e293e..cc4d614 100644
--- a/src/subconscious/learn.rs
+++ b/src/subconscious/learn.rs
@@ -299,13 +299,18 @@ pub async fn score_memory(
 /// Updates the graph weight (EWMA) and last_scored after each.
 ///
 /// Returns the number of nodes scored and their (key, score) pairs.
-pub async fn score_memories_incremental(
+pub async fn score_memories_incremental<F, Fut>(
     context: &ContextState,
     max_age_secs: i64,
     response_window: usize,
     client: &ApiClient,
     agent: &std::sync::Arc<tokio::sync::Mutex<crate::agent::Agent>>,
-) -> anyhow::Result<Vec<(String, f64)>> {
+    mut on_score: F,
+) -> anyhow::Result<usize>
+where
+    F: FnMut(String, f64) -> Fut,
+    Fut: std::future::Future<Output = ()>,
+{
     let now = chrono::Utc::now().timestamp();
 
     // Collect unique memory keys with their first position
@@ -330,7 +335,7 @@ pub async fn score_memories_incremental(
     candidates.sort_by_key(|&(_, _, last)| last);
 
     let http = http_client();
-    let mut results = Vec::new();
+    let mut scored = 0;
 
     let total_entries = context.conversation.entries().len();
     let first_quarter = total_entries / 4;
@@ -355,7 +360,8 @@ pub async fn score_memories_incremental(
                 dbglog!(
                     "[scoring] {} max:{:.3} ({} responses)", key, max_div, n_responses,
                 );
-                results.push((key.clone(), max_div));
+                on_score(key.clone(), max_div).await;
+                scored += 1;
             }
             Err(e) => {
                 dbglog!(
@@ -365,7 +371,7 @@ pub async fn score_memories_incremental(
         }
     }
 
-    Ok(results)
+    Ok(scored)
 }
 
 // ── Fine-tuning scoring ─────────────────────────────────────────