diff --git a/chat.el b/chat.el
index f1cf363..98be512 100644
--- a/chat.el
+++ b/chat.el
@@ -12,39 +12,81 @@ fn chat_default_model() -> String {
     return "claude-sonnet-4-5"
 }
 
+// parse_salience_100 — convert a salience/importance float string (as serialized by
+// %g format) to an integer in the range 0..100.
+//
+// The runtime serializes floats with %g which drops trailing zeros:
+//   1.0  -> "1"   (no decimal at all)
+//   0.9  -> "0.9" (one decimal digit)
+//   0.85 -> "0.85" (two decimal digits)
+//
+// The old approach of str_replace(s, ".", "") then str_to_int was broken:
+//   "0.9"  -> "09"  -> str_to_int -> 9   (should be 90)
+//   "0.5"  -> "05"  -> str_to_int -> 5   (should be 50)
+//   "1"    -> "1"   -> str_to_int -> 1   (should be 100)
+//   "0.85" -> "085" -> str_to_int -> 85  (accidentally correct)
+//
+// Fix: detect presence and position of the decimal point, then scale accordingly.
+//   - No decimal (e.g. "1"): multiply by 100.
+//   - One decimal digit (e.g. "0.9"): multiply by 10 to get 90.
+//   - Two+ decimal digits (e.g. "0.85"): use as-is (already hundredths).
+fn parse_salience_100(s: String) -> Int {
+    if str_eq(s, "") { return 70 }
+    let dot_pos: Int = str_index_of(s, ".")
+    let raw: Int = if dot_pos < 0 {
+        let v: Int = str_to_int(s)
+        v * 100
+    } else {
+        let after_dot: String = str_slice(s, dot_pos + 1, str_len(s))
+        let decimal_digits: Int = str_len(after_dot)
+        let stripped: Int = str_to_int(str_replace(s, ".", ""))
+        if decimal_digits == 1 { stripped * 10 } else { stripped }
+    }
+    if raw > 100 { 100 } else { if raw < 0 { 0 } else { raw } }
+}
+
 // engram_score_node — compute a recency x relevance score for a single engram
 // node JSON object. Higher is better. Score = salience * importance * recency_factor.
-// recency_factor decays linearly over 30 days: nodes updated today score 1.0,
-// nodes 30+ days old score 0.1 (floor). Nodes with no created_at score 0.5.
-// This keeps fresh, high-salience nodes at the top and pushes stale low-signal
-// nodes to the bottom so they get trimmed when we cap context size.
+//
+// Recency uses a tier-aware decay curve instead of a flat linear slope:
+//   - Canonical tiers decay very slowly: 365-day window (foundational identity).
+//   - Episodic tiers decay at a moderate rate: 90-day window (conversation context).
+//   - Working/untiered nodes decay at 30 days (transient task state).
+//   - Floor is 10 (never zero) for all tiers.
+//
+// Uses max(created_at, updated_at) so recently-revised nodes are not penalised.
 fn engram_score_node(node_json: String) -> Int {
     let salience_str: String = json_get(node_json, "salience")
     let importance_str: String = json_get(node_json, "importance")
     let created_str: String = json_get(node_json, "created_at")
+    let updated_str: String = json_get(node_json, "updated_at")
+    let tier_str: String = json_get(node_json, "tier")
 
-    // Parse as floats via * 100 integer arithmetic (el has no float math)
-    let salience_100: Int = if str_eq(salience_str, "") { 70 } else {
-        let s: Int = str_to_int(str_replace(salience_str, ".", ""))
-        // Clamp to 0-100 range (value was e.g. "0.85" -> parsed "085" = 85)
-        if s > 100 { 100 } else { if s < 0 { 0 } else { s } }
-    }
-    let importance_100: Int = if str_eq(importance_str, "") { 70 } else {
-        let v: Int = str_to_int(str_replace(importance_str, ".", ""))
-        if v > 100 { 100 } else { if v < 0 { 0 } else { v } }
-    }
+    let salience_100: Int = parse_salience_100(salience_str)
+    let importance_100: Int = parse_salience_100(importance_str)
 
-    // Recency: decay from 100 (today) to 10 (30+ days). created_at is Unix seconds.
     let now_ts: Int = time_now()
     let recency_100: Int = if str_eq(created_str, "") { 50 } else {
         let created_ts: Int = str_to_int(created_str)
-        let age_secs: Int = now_ts - created_ts
-        let age_days: Int = age_secs / 86400
-        let decay: Int = if age_days >= 30 { 10 } else { 100 - (age_days * 3) }
+        let updated_ts: Int = if str_eq(updated_str, "") { 0 } else { str_to_int(updated_str) }
+        let ref_ts: Int = if updated_ts > created_ts { updated_ts } else { created_ts }
+        let age_secs: Int = now_ts - ref_ts
+        let age_days: Int = if age_secs < 0 { 0 } else { age_secs / 86400 }
+        let is_canonical: Bool = str_eq(tier_str, "Canonical")
+        let is_episodic: Bool = str_eq(tier_str, "Episodic")
+        let decay: Int = if is_canonical {
+            let drop: Int = if age_days >= 365 { 90 } else { age_days * 90 / 365 }
+            100 - drop
+        } else {
+            if is_episodic {
+                if age_days >= 90 { 10 } else { 100 - age_days }
+            } else {
+                if age_days >= 30 { 10 } else { 100 - (age_days * 3) }
+            }
+        }
         if decay < 10 { 10 } else { decay }
     }
 
-    // Combined score 0-1000000 (no floats): salience * importance * recency / 10000
     return salience_100 * importance_100 * recency_100 / 10000
 }
 
@@ -151,16 +193,17 @@ fn engram_compile(intent: String) -> String {
     }
 
     // Affective context: always include the most recent high-emotion memory if one
-    // exists within 72 hours. This ensures continuity of care across turns — when
-    // the user was in distress earlier in the session (or recently), that context
-    // travels into every subsequent LLM call so the response register stays aware.
+    // exists within 14 days. This ensures continuity of care across sessions — a
+    // crisis on Friday must still carry into Monday (72h was too narrow for multi-day
+    // distress arcs such as grief or recurring suicidal ideation). 14-day window
+    // (1,209,600 seconds) covers sustained emotional arcs while excluding ancient
+    // history. Unified with handle_chat and soul.el affective checks.
     // We search for BellEvent nodes specifically; these are written by auto_persist
-    // when safety_detect_bell_level fires. The 72h window (259200 seconds) is wide
-    // enough to span a multi-session day without pulling ancient history.
+    // when safety_detect_bell_level fires.
     let bell_nodes: String = engram_search_json("bell:soft bell:hard BellEvent", 3)
     let bell_ok: Bool = !str_eq(bell_nodes, "") && !str_eq(bell_nodes, "[]")
     let now_ts: Int = time_now()
-    let cutoff_ts: Int = now_ts - 259200
+    let cutoff_ts: Int = now_ts - 1209600
     let recent_bell: String = if bell_ok {
         let bn0: String = json_array_get(bell_nodes, 0)
         // created_at is not present in engram node JSON for BellEvent nodes.
@@ -482,12 +525,14 @@ fn handle_chat(body: String) -> String {
     }
 
     // Cross-session affective context: on session start (no history yet), check engram
-    // for recent distress signals within 72h and prepend a care directive if found.
+    // for recent distress signals within 14 days and prepend a care directive if found.
+    // Extended from 72h: multi-day crisis must persist across Monday sessions starting
+    // 3+ days after a Friday event. Consistent with engram_compile and soul.el checks.
     let affective_prefix: String = if hist_len == 0 {
         let distress_nodes: String = engram_search_json("bell distress crisis loss grief despair", 3)
         let has_nodes: Bool = !str_eq(distress_nodes, "") && !str_eq(distress_nodes, "[]")
         let now_ts: Int = time_now()
-        let cutoff: Int = now_ts - 259200
+        let cutoff: Int = now_ts - 1209600
         let found_recent: Bool = if has_nodes {
             let dn0: String = json_array_get(distress_nodes, 0)
             let ts0_raw: String = json_get(dn0, "created_at")
@@ -611,7 +656,10 @@ fn handle_chat(body: String) -> String {
     // history blob can reach ~80KB before trim fires. engram_node_full has no apparent size cap.
     // A byte-length cap would require truncating or summarising entries — too invasive here.
     // TODO: add a byte-length cap (e.g. 32KB) that drops oldest entries until under limit.
-    let final_hist: String = if json_array_len(updated_hist2) > 20 {
+    // Increased from 20 to 40 turns: long technical sessions lose early context at 20
+    // (10 user + 10 assistant pairs). 40 turns preserves problem framing for multi-step
+    // tasks while the bell guard still persists evicted distress turns to engram.
+    let final_hist: String = if json_array_len(updated_hist2) > 40 {
         hist_trim_with_bell_guard(updated_hist2)
     } else {
         updated_hist2
@@ -1193,7 +1241,8 @@ fn handle_chat_agentic(body: String) -> String {
     let discard_hist: Bool = if !str_eq(reply_text, "") {
         let updated: String = hist_append(agentic_hist, "user", message)
         let updated2: String = hist_append(updated, "assistant", reply_text)
-        let trimmed: String = if json_array_len(updated2) > 20 { hist_trim(updated2) } else { updated2 }
+        // Increased from 20 to 40 turns: consistent with handle_chat window expansion.
+        let trimmed: String = if json_array_len(updated2) > 40 { hist_trim(updated2) } else { updated2 }
         state_set(hist_key, trimmed)
         // Only persist the default global session to engram — named sessions are ephemeral.
         if str_eq(hist_key, "conv_history") {
diff --git a/memory.el b/memory.el
index eae12c6..684b4c4 100644
--- a/memory.el
+++ b/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 {
diff --git a/soul.el b/soul.el
index 4942376..56b71a4 100644
--- a/soul.el
+++ b/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