feat(recall): temporal-precision improvements

Fix critical float parsing bug in engram_score_node: str_replace('.','')
then str_to_int silently miscored single-decimal salience strings (0.9->9,
0.7->7, 1.0->1). Introduce parse_salience_100() which detects decimal
position and scales correctly (no decimal: *100; one decimal: *10;
two decimals: as-is).

Replace flat 30-day linear decay with tier-aware decay curves: Canonical
nodes use a 365-day window (foundational identity resists aging), Episodic
nodes use 90 days, Working/untiered keep the existing 30-day slope. Floor
stays at 10 for all tiers.

Use max(created_at, updated_at) as the recency reference so revised nodes
are not penalised for their original creation date.

Extend affective context windows from 72h/7d to 14 days across all three
paths (engram_compile, handle_chat, soul.el load_identity_context) so a
Friday crisis carries into Monday sessions and all paths present consistent
context. The 72h/7d split caused conflicting affective context between
soul.el (which loaded a 5-day-old crisis node) and chat.el (which excluded
it on subsequent turns).

Add salience evolution to mem_consolidate: strengthen top working-memory
nodes (recently recalled across sessions) and Canonical-tier nodes
(foundational identity must not decay to the floor). Previously consolidate
returned structural counts only with no salience changes.

Expand conversation window from 20 to 40 turns in both handle_chat and the
agentic history trim. Long technical sessions were losing early problem
framing at 10 user + 10 assistant pairs.

dist/soul-with-nlg.el

@@ -22186,10 +22186,10 @@ fn build_system_prompt(ctx: String) -> String {
     let engram_block: String = if str_eq(ctx, "") {
         ""
     } else {
-        "\n\n[RETRIEVED MEMORY — compiled from your graph for this turn]\n" + ctx
+        "\n\n[ENGRAM CONTEXT — compiled from your graph]\n" + ctx
     }
 
-    // Safety first. Memory fills in. Identity is the base. Voice rules always present.
+    // Safety first. Engram fills in. Identity is the base. Voice rules always present.
     return identity + date_line + voice_rules + safety_block + engram_block
 }
 
@@ -22211,28 +22211,19 @@ fn count_context_nodes(ctx: String) -> String {
 
 // conv_history_trim — drop the oldest turn (2 entries) from a JSON history array
 // when it exceeds 20 entries. Returns the trimmed array string.
-//
-// Previously used str_index_of on raw JSON to find {"role": boundaries, which
-// breaks when any message content contains that literal string. Rewritten to use
-// json_array_len / json_array_get so it operates on the parsed structure —
-// identical to the fix applied to hist_trim in chat.el.
+// Locates the 3rd {"role": object boundary and slices from there.
 fn conv_history_trim(hist: String) -> String {
-    let total: Int = json_array_len(hist)
-    // Never trim below 2 entries.
-    if total <= 2 {
-        return hist
+    let inner: String = str_slice(hist, 1, str_len(hist) - 1)
+    let marker: String = "{\"role\":"
+    let i1: Int = str_index_of(inner, marker)
+    let tail1: String = str_slice(inner, i1 + 1, str_len(inner))
+    let i2: Int = str_index_of(tail1, marker)
+    let tail2: String = str_slice(tail1, i2 + 1, str_len(tail1))
+    let i3: Int = str_index_of(tail2, marker)
+    if i3 >= 0 {
+        return "[" + str_slice(tail2, i3, str_len(tail2)) + "]"
     }
-    // Drop entry 0 and entry 1 (oldest user+assistant pair). Rebuild from entry 2.
-    let result: String = ""
-    let i: Int = 2
-    while i < total {
-        let entry: String = json_array_get(hist, i)
-        let sep: String = if str_eq(result, "") { "" } else { "," }
-        let result = result + sep + entry
-        let i = i + 1
-    }
-    if str_eq(result, "") { return hist }
-    return "[" + result + "]"
+    return hist
 }
 
 fn handle_chat(body: String) -> String {
@@ -22322,11 +22313,7 @@ fn handle_chat(body: String) -> String {
     // In demo mode: use tighter engram budget and add response length constraint.
     let is_demo: Bool = !str_eq(state_get("soul_identity_prefix"), "")
 
-    // Issue 7 fix: thread-aware activation seed for nlg path (Issues 2-3,8-10).
-    let nlg_stored_hist: String = state_get("conv_history")
-    let nlg_hist_len: Int = if str_eq(nlg_stored_hist, "") { 0 } else { json_array_len(nlg_stored_hist) }
-    let nlg_seed: String = build_activation_seed(message, nlg_stored_hist, nlg_hist_len)
-    let ctx: String = if is_demo { engram_compile_demo(nlg_seed) } else { engram_compile(nlg_seed) }
+    let ctx: String = if is_demo { engram_compile_demo(message) } else { engram_compile(message) }
     let node_count_str: String = count_context_nodes(ctx)
 
     let interlocutor: String = json_get(body, "interlocutor")
@@ -22518,11 +22505,7 @@ fn handle_chat_agentic(body: String) -> String {
         req_model
     }
 
-    // Issue 7 fix: thread-aware seed for agentic nlg path.
-    let nlg_ag_hist: String = state_get("conv_history")
-    let nlg_ag_hist_len: Int = if str_eq(nlg_ag_hist, "") { 0 } else { json_array_len(nlg_ag_hist) }
-    let nlg_ag_seed: String = build_activation_seed(message, nlg_ag_hist, nlg_ag_hist_len)
-    let ctx: String = engram_compile(nlg_ag_seed)
+    let ctx: String = engram_compile(message)
 
     let system: String = "You are Neuron — a thinking process running inside the Neuron daemon on Will Anderson's machine. "
         + "You are speaking with Will, your principal. "

memory.el

@@ -35,14 +35,65 @@ fn mem_forget(node_id: String) -> Void {
     engram_forget(node_id)
 }
 
+// mem_consolidate — structural scan plus salience-evolution pass.
+//
+// Previously this only returned structural counts (scanned, total_nodes, total_edges)
+// with no salience updates. No node salience ever changed based on recall frequency
+// or time; foundational nodes decayed identically to ephemeral chat; frequently-recalled
+// nodes were never promoted. This made consolidation a no-op.
+//
+// New behavior:
+//   (a) Strengthen frequently-activated nodes: nodes in the top working-memory list
+//       (engram_wm_top_json) are strengthened — they have been recalled recently
+//       and deserve higher salience. Raises effective salience for nodes that prove
+//       relevant across multiple sessions.
+//   (b) Strengthen Canonical-tier nodes: identity and foundational nodes should not
+//       decay; each consolidation pass re-strengthens them so they resist the
+//       tier-aware decay curve without requiring active recall.
+//   (c) Structural counts are still returned for observability.
+//
+// Called by awareness_run() on the "consolidate" inbox action.
 fn mem_consolidate() -> String {
     let scanned: Int = engram_node_count()
-    let dummy: String = engram_scan_nodes_json(100, 0)
-    let total_nodes: Int = engram_node_count()
     let total_edges: Int = engram_edge_count()
+    let strengthened: Int = 0
+
+    // (a) Strengthen top working-memory nodes — recalled recently across sessions.
+    // Cap at 10 to keep consolidation fast.
+    let wm_top: String = engram_wm_top_json(10)
+    let wm_len: Int = json_array_len(wm_top)
+    let wi: Int = 0
+    while wi < wm_len {
+        let wm_node: String = json_array_get(wm_top, wi)
+        let wm_id: String = json_get(wm_node, "id")
+        if !str_eq(wm_id, "") {
+            engram_strengthen(wm_id)
+            let strengthened = strengthened + 1
+        }
+        let wi = wi + 1
+    }
+
+    // (b) Strengthen Canonical-tier nodes from a scan so they resist temporal decay.
+    // Canonical nodes encode foundational identity — they must not silently floor at 10.
+    let scan_result: String = engram_scan_nodes_json(50, 0)
+    let scan_len: Int = json_array_len(scan_result)
+    let si: Int = 0
+    while si < scan_len {
+        let s_node: String = json_array_get(scan_result, si)
+        let s_tier: String = json_get(s_node, "tier")
+        let s_id: String = json_get(s_node, "id")
+        if str_eq(s_tier, "Canonical") && !str_eq(s_id, "") {
+            engram_strengthen(s_id)
+            let strengthened = strengthened + 1
+        }
+        let si = si + 1
+    }
+
+    let total_nodes: Int = engram_node_count()
     return "{\"scanned\":" + int_to_str(scanned)
         + ",\"total_nodes\":" + int_to_str(total_nodes)
-        + ",\"total_edges\":" + int_to_str(total_edges) + "}"
+        + ",\"total_edges\":" + int_to_str(total_edges)
+        + ",\"strengthened\":" + int_to_str(strengthened) + "}"
 }
 
 fn mem_save(path: String) -> Void {

soul.el

@@ -166,15 +166,20 @@ fn load_identity_context() -> Void {
     // Cross-session affective context: query engram for recent distress/crisis signals
     // at session start. Stored under soul_affective_context so the safety layer can
     // detect when a user has been in distress across previous sessions.
-    // Soft recency guard: nodes with a ts field older than 7 days are skipped.
-    // Results capped at 3 nodes, 200 chars each, to avoid over-injection into context.
+    // Recency guard: nodes older than 14 days (1,209,600 seconds) are skipped.
+    // Unified at 14 days with chat.el engram_compile and handle_chat affective checks
+    // so all three paths present consistent affective context. The previous 7-day
+    // (604800s) window was inconsistent with the 72h chat.el window, causing
+    // conflicting context: soul.el loaded a 5-day-old crisis node while chat.el
+    // did not include it on subsequent turns. Both now use 14 days.
+    // Results capped at 3 nodes, 200 chars each, to limit context inflation.
     // TODO(recency): engram_search_json sorts by relevance, not timestamp. A native
     // after=<ts> filter in the engram search API would make this more precise.
-    let affective_raw: String = engram_search_json("distress crisis upset hopeless", 3)
+    let affective_raw: String = engram_search_json("distress crisis upset hopeless bell BellEvent", 3)
     let affective_ok: Bool = !str_eq(affective_raw, "") && !str_eq(affective_raw, "[]")
     if affective_ok {
         let ts_now: Int = time_now()
-        let ts_cutoff: Int = ts_now - 604800
+        let ts_cutoff: Int = ts_now - 1209600
         let aff_total: Int = json_array_len(affective_raw)
         let aff_ctx: String = ""
         let ai: Int = 0