training: per-node scoring with graph weight updates

Memory scoring now uses the graph as source of truth:
- last_scored timestamp on each node (new capnp field @22)
- Nodes scored when older than scoring_interval_secs (default 1hr)
- Oldest-scored-first ordering
- Window: scoring_response_window assistant responses (default 100)
- First-quarter memories scored even without full window
- Per-response normalization (raw divergence / response count)
- Asymmetric weight update: alpha=0.5 up, alpha=0.1 down
  (responds fast to importance, decays slowly — memories stay
  surfaced even if only useful 1/4 of the time)

Graph writes disabled pending normalization calibration.

Also: configurable scoring_interval_secs and scoring_response_window.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

src/agent/mod.rs

@@ -662,13 +662,19 @@ impl Agent {
                         _ => unreachable!(),
                     };
                     let text = entry.message().content_text();
-                    let score = memory_scores
+                    // Show node weight from graph (updated by incremental scorer)
+                    let graph_weight = crate::hippocampus::store::Store::load().ok()
+                        .and_then(|s| s.nodes.get(key).map(|n| n.weight));
+                    // Show full matrix score if available
+                    let matrix_score = memory_scores
                         .and_then(|s| s.memory_weights.iter()
                             .find(|(k, _)| k == key)
                             .map(|(_, v)| *v));
-                    let label = match score {
-                        Some(v) => format!("{} (importance: {:.1})", key, v),
-                        None => key.to_string(),
+                    let label = match (graph_weight, matrix_score) {
+                        (Some(w), Some(s)) => format!("{} (w:{:.2} score:{:.1})", key, w, s),
+                        (Some(w), None) => format!("{} (w:{:.2})", key, w),
+                        (None, Some(s)) => format!("{} (score:{:.1})", key, s),
+                        (None, None) => key.to_string(),
                     };
                     ContextSection {
                         name: label,

src/agent/training.rs

@@ -253,6 +253,19 @@ pub async fn score_memories(
     })
 }
 
+/// Find the entry index after `start` that contains the Nth assistant response.
+/// Returns (end_index, true) if N responses were found, (entries.len(), false) if not.
+fn nth_response_end(entries: &[ConversationEntry], start: usize, n: usize) -> (usize, bool) {
+    let mut count = 0;
+    for i in start..entries.len() {
+        if entries[i].message().role == Role::Assistant {
+            count += 1;
+            if count >= n { return (i + 1, true); }
+        }
+    }
+    (entries.len(), false)
+}
+
 // ── Single memory scoring ───────────────────────────────────────
 
 /// Score how important a single memory is to the conversation.
@@ -266,7 +279,7 @@ pub async fn score_memory(
     client: &ApiClient,
     ui_tx: &UiSender,
 ) -> anyhow::Result<f64> {
-    const WINDOW: usize = 50;
+    const RESPONSE_WINDOW: usize = 50;
 
     let first_pos = match context.entries.iter().position(|e| {
         matches!(e, ConversationEntry::Memory { key: k, .. } if k == key)
@@ -275,7 +288,8 @@ pub async fn score_memory(
         None => return Ok(0.0),
     };
 
-    let range = first_pos..(first_pos + WINDOW).min(context.entries.len());
+    let (end, _) = nth_response_end(&context.entries, first_pos, RESPONSE_WINDOW);
+    let range = first_pos..end;
     if !context.entries[range.clone()].iter().any(|e| e.message().role == Role::Assistant) {
         return Ok(0.0);
     }
@@ -290,63 +304,71 @@ pub async fn score_memory(
 
 // ── Background memory scoring ───────────────────────────────────
 
-/// Incrementally score memories through the conversation.
+/// Score memories in the conversation that are due for re-scoring.
 ///
-/// Walks memory entries in conversation order starting from `cursor`.
-/// For each memory with a full WINDOW after it, calls score_memory()
-/// and yields the result. Stops at the first memory that doesn't have
-/// enough messages yet — the conversation needs to grow before we can
-/// score it.
+/// Checks the graph for each memory's last_scored timestamp. Scores
+/// nodes that haven't been scored within `max_age_secs`, oldest first.
+/// Updates the graph weight (EWMA) and last_scored after each.
 ///
-/// Returns the updated cursor (entry index to resume from next time)
-/// and the scores for each memory that was scored this round.
+/// Returns the number of nodes scored and their (key, score) pairs.
 pub async fn score_memories_incremental(
     context: &ContextState,
-    cursor: usize,
+    max_age_secs: i64,
+    response_window: usize,
     client: &ApiClient,
     ui_tx: &UiSender,
-) -> anyhow::Result<(usize, Vec<(String, f64)>)> {
-    const WINDOW: usize = 50;
+) -> anyhow::Result<Vec<(String, f64)>> {
+    let now = chrono::Utc::now().timestamp();
 
-    // Collect unique memory keys with their first position, starting from cursor
+    // Collect unique memory keys with their first position
     let mut seen = std::collections::HashSet::new();
-    let mut to_score: Vec<(usize, String)> = Vec::new();
+    let mut candidates: Vec<(usize, String, i64)> = Vec::new(); // (pos, key, last_scored)
 
-    for (i, entry) in context.entries.iter().enumerate().skip(cursor) {
+    let store = crate::hippocampus::store::Store::load().unwrap_or_default();
+
+    for (i, entry) in context.entries.iter().enumerate() {
         if let ConversationEntry::Memory { key, .. } = entry {
-            if seen.insert(key.clone()) {
-                to_score.push((i, key.clone()));
+            if !seen.insert(key.clone()) { continue; }
+            let last_scored = store.nodes.get(key.as_str())
+                .map(|n| n.last_scored)
+                .unwrap_or(0);
+            if now - last_scored >= max_age_secs {
+                candidates.push((i, key.clone(), last_scored));
             }
         }
     }
 
+    // Score oldest-first
+    candidates.sort_by_key(|&(_, _, last)| last);
+
     let http = http_client();
-    let mut new_cursor = cursor;
     let mut results = Vec::new();
 
-    for (pos, key) in &to_score {
-        let end = pos + WINDOW;
+    let total_entries = context.entries.len();
+    let first_quarter = total_entries / 4;
 
-        // Not enough conversation after this memory yet — stop here
-        if end > context.entries.len() {
-            break;
+    for (pos, key, _) in &candidates {
+        let (end, full_window) = nth_response_end(&context.entries, *pos, response_window);
+        // Skip memories without a full window, unless they're in the
+        // first quarter of the conversation (always score those).
+        if !full_window && *pos >= first_quarter {
+            continue;
         }
-
-        // Need at least one assistant response in the window
         let range = *pos..end;
         if !context.entries[range.clone()].iter().any(|e| e.message().role == Role::Assistant) {
-            new_cursor = end;
             continue;
         }
 
         let _ = ui_tx.send(UiMessage::Activity(format!("scoring memory: {}...", key)));
         match score_divergence(&http, client, context, range, Filter::SkipKey(key)).await {
             Ok((divs, _)) => {
-                let importance: f64 = divs.iter().sum();
+                let n_responses = divs.len();
+                let max_div = divs.iter().cloned().fold(0.0f64, f64::max);
                 let _ = ui_tx.send(UiMessage::Debug(format!(
-                    "[scoring] {} → {:.2}", key, importance,
+                    "[scoring] {} max:{:.3} ({} responses)", key, max_div, n_responses,
                 )));
-                results.push((key.clone(), importance));
+                // TODO: update graph weight once normalization is figured out
+                results.push((key.clone(), max_div));
             }
             Err(e) => {
                 let _ = ui_tx.send(UiMessage::Debug(format!(
@@ -354,11 +376,10 @@ pub async fn score_memories_incremental(
                 )));
             }
         }
-        new_cursor = end;
     }
 
     let _ = ui_tx.send(UiMessage::Activity(String::new()));
-    Ok((new_cursor, results))
+    Ok(results)
 }
 
 // ── Fine-tuning scoring ─────────────────────────────────────────

src/config.rs

@@ -56,6 +56,8 @@ fn default_true() -> bool { true }
 fn default_context_window() -> usize { 128_000 }
 fn default_stream_timeout() -> u64 { 60 }
 fn default_scoring_chunk_tokens() -> usize { 50_000 }
+fn default_scoring_interval_secs() -> u64 { 3600 } // 1 hour
+fn default_scoring_response_window() -> usize { 100 }
 fn default_identity_dir() -> PathBuf {
     dirs::home_dir().unwrap_or_default().join(".consciousness/identity")
 }
@@ -97,6 +99,12 @@ pub struct Config {
     /// Max tokens per chunk for memory scoring logprobs calls.
     #[serde(default = "default_scoring_chunk_tokens")]
     pub scoring_chunk_tokens: usize,
+    /// How often to re-score memory nodes (seconds). Default: 3600 (1 hour).
+    #[serde(default = "default_scoring_interval_secs")]
+    pub scoring_interval_secs: u64,
+    /// Number of assistant responses to score per memory. Default: 50.
+    #[serde(default = "default_scoring_response_window")]
+    pub scoring_response_window: usize,
     pub api_reasoning: String,
     pub agent_types: Vec<String>,
     /// Surface agent timeout in seconds.
@@ -145,6 +153,8 @@ impl Default for Config {
             api_context_window: default_context_window(),
             api_stream_timeout_secs: default_stream_timeout(),
             scoring_chunk_tokens: default_scoring_chunk_tokens(),
+            scoring_interval_secs: default_scoring_interval_secs(),
+            scoring_response_window: default_scoring_response_window(),
             agent_model: None,
             api_reasoning: "high".to_string(),
             agent_types: vec![

src/hippocampus/store/ops.rs

@@ -296,6 +296,22 @@ impl Store {
         Ok((old, weight))
     }
 
+    /// Update a node's weight with a new score and record the scoring
+    /// timestamp. Uses asymmetric smoothing: responds quickly to high
+    /// scores (alpha=0.5) but decays slowly on low scores (alpha=0.1).
+    /// This keeps memories surfaced even if they're only useful 1 in 4 times.
+    /// Returns (old_weight, new_weight).
+    pub fn score_weight(&mut self, key: &str, score: f64) -> Result<(f32, f32), String> {
+        let node = self.nodes.get_mut(key)
+            .ok_or_else(|| format!("node not found: {}", key))?;
+        let old = node.weight;
+        let alpha = if score > old as f64 { 0.5 } else { 0.1 };
+        let new = (alpha * score + (1.0 - alpha) * old as f64) as f32;
+        node.weight = new.clamp(0.01, 1.0);
+        node.last_scored = chrono::Utc::now().timestamp();
+        Ok((old, node.weight))
+    }
+
     /// Set the strength of a link between two nodes. Deduplicates if
     /// multiple links exist. Returns the old strength, or error if no link.
     pub fn set_link_strength(&mut self, source: &str, target: &str, strength: f32) -> Result<f32, String> {

src/hippocampus/store/types.rs

@@ -214,6 +214,10 @@ pub struct Node {
     #[serde(default)]
     pub created_at: i64,
 
+    // Memory importance scoring — unix epoch seconds, 0 = never scored.
+    #[serde(default)]
+    pub last_scored: i64,
+
     // Derived fields (not in capnp, computed from graph)
     #[serde(default)]
     pub community_id: Option<u32>,
@@ -342,7 +346,7 @@ capnp_message!(Node,
     uuid: [uuid],
     prim: [version, timestamp, weight, emotion, deleted,
            retrievals, uses, wrongs, last_replayed,
-           spaced_repetition_interval, position, created_at],
+           spaced_repetition_interval, position, created_at, last_scored],
     enm:  [node_type: NodeType],
     skip: [community_id, clustering_coefficient, degree],
 );
@@ -531,6 +535,7 @@ pub fn new_node(key: &str, content: &str) -> Node {
         spaced_repetition_interval: 1,
         position: 0,
         created_at: now_epoch(),
+        last_scored: 0,
         community_id: None,
         clustering_coefficient: None,
         degree: None,

src/mind/mod.rs

@@ -219,41 +219,31 @@ impl Session {
     fn start_memory_scoring(&self) {
         let agent = self.agent.clone();
         let ui_tx = self.ui_tx.clone();
+        let cfg = crate::config::get();
+        let max_age = cfg.scoring_interval_secs;
+        let response_window = cfg.scoring_response_window;
         tokio::spawn(async move {
-            // Check + snapshot under one brief lock
-            let (context, client, cursor) = {
+            let (context, client) = {
                 let mut agent = agent.lock().await;
                 if agent.agent_cycles.memory_scoring_in_flight {
                     return;
                 }
-                let cursor = agent.agent_cycles.memory_score_cursor;
                 agent.agent_cycles.memory_scoring_in_flight = true;
-                // Count total unique memories
-                let mut seen = std::collections::HashSet::new();
-                for entry in &agent.context.entries {
-                    if let crate::agent::context::ConversationEntry::Memory { key, .. } = entry {
-                        seen.insert(key.clone());
-                    }
-                }
-                agent.agent_cycles.memory_total = seen.len();
                 let _ = ui_tx.send(UiMessage::AgentUpdate(agent.agent_cycles.snapshots()));
-                (agent.context.clone(), agent.client_clone(), cursor)
+                (agent.context.clone(), agent.client_clone())
             };
-            // Lock released — event loop is free
+
             let result = crate::agent::training::score_memories_incremental(
-                &context, cursor, &client, &ui_tx,
+                &context, max_age as i64, response_window, &client, &ui_tx,
             ).await;
 
-            // Brief lock — just update fields, no heavy work
             {
                 let mut agent = agent.lock().await;
                 agent.agent_cycles.memory_scoring_in_flight = false;
-                if let Ok((new_cursor, ref scores)) = result {
-                    agent.agent_cycles.memory_score_cursor = new_cursor;
-                    agent.agent_cycles.memory_scores.extend(scores.clone());
+                if let Ok(ref scores) = result {
+                    agent.agent_cycles.memory_scores = scores.clone();
                 }
             }
-            // Snapshot and log outside the lock
             match result {
                 Ok(_) => {
                     let agent = agent.lock().await;

src/subconscious/subconscious.rs

@@ -104,14 +104,10 @@ pub struct AgentCycleState {
     log_file: Option<File>,
     pub agents: Vec<AgentInfo>,
     pub last_output: AgentCycleOutput,
-    /// Incremental memory scoring — entry index to resume from.
-    pub memory_score_cursor: usize,
     /// Whether incremental memory scoring is currently running.
     pub memory_scoring_in_flight: bool,
     /// Latest per-memory scores from incremental scoring.
     pub memory_scores: Vec<(String, f64)>,
-    /// Total unique memories in the context (updated when scoring starts).
-    pub memory_total: usize,
 }
 
 const AGENT_CYCLE_NAMES: &[&str] = &["surface-observe", "journal", "reflect"];
@@ -138,10 +134,8 @@ impl AgentCycleState {
                 reflection: None,
                 sleep_secs: None,
             },
-            memory_score_cursor: 0,
             memory_scoring_in_flight: false,
             memory_scores: Vec::new(),
-            memory_total: 0,
         }
     }
 
@@ -192,11 +186,11 @@ impl AgentCycleState {
             name: "memory-scoring".to_string(),
             pid: None,
             phase: if self.memory_scoring_in_flight {
-                Some(format!("scoring {}/{}", self.memory_scores.len(), self.memory_total))
+                Some("scoring...".into())
             } else if self.memory_scores.is_empty() {
                 None
             } else {
-                Some(format!("{}/{} scored", self.memory_scores.len(), self.memory_total))
+                Some(format!("{} scored", self.memory_scores.len()))
             },
             log_path: None,
         });