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Author SHA1 Message Date
will.anderson 96d6bef0c2 fix(engram-scoring): correct relevance denominator, hard_bell brace, threshold
Three fixes from code review on improve/recall-engram-scoring:

1. CRITICAL — relevance denominator /10000 → /100: parse_salience_100 already
   scales floats to 0-100 (e.g. "0.7" → 70), so the product of two such values
   must be divided by 100 to stay in 0-100 range. The /10000 divisor caused
   integer truncation to 0 for every real-world node (sal=0.7, imp=0.7 →
   70*70/10000 = 0). engram_compile_ranked was returning empty string for all
   inputs, leaving the soul with zero memory context.

2. CRITICAL — missing closing brace for hard_bell if-block in handle_chat_agentic
   (line ~1050): the return statement was not followed by the closing `}`, making
   the entire non-bell code path dead code inside the branch. All agentic turns
   that were not a hard_bell would silently fall through the open block.

3. HIGH — threshold 15 → 10 in engram_compile_ranked: even after the /100 fix,
   threshold=15 was marginally too aggressive for low-salience nodes near the
   Working-tier recency floor. sal=0.5 imp=0.5 at floor scores 16 (just above
   15), so the margin was only 1 point. Lowering to 10 gives comfortable headroom
   while still filtering genuine noise (sal=0.1 imp=0.1 → score ≤ 1).
2026-06-22 13:35:00 -05:00
will.anderson 76c2e47d0f feat(recall): fix engram-scoring — float parsing, recency, threshold, sentinels
Neuron Soul CI / build (pull_request) Has been cancelled
Fix critical float parsing bug: %g serializes 0.70 as '0.7', naive str_replace
dot-strip gives str_to_int('07')=7 not 70. New parse_salience_100() uses
str_index_of to detect single-decimal strings and multiplies by 10 to correct.
Affects conv nodes (0.6/0.7), default memories (0.5/0.5), utterance nodes (0.6)
— the majority of the graph was scoring near zero and filtered by threshold=25.

Fix recency to use max(created_at, updated_at, last_activated) so nodes
strengthened by engram_strengthen() after chat turns score as fresh, not by
original write time. A node referenced yesterday but created 25 days ago
was borderline-filtered; now correctly scores fresh.

Compress recency dynamic range from 10x (10-100) to 1.54x (65-100) via
formula (50 + recency/2). Old formula: sal*imp*recency/10000 let recency
dominate — a canonical high-importance node at 30 days scored identical to
a fresh noise node. New: high-importance nodes remain competitive when old.

Add tier-aware decay with softer floor (30 not 10): Canonical nodes decay
over 365 days, Episodic over 90 days, working/untiered over 35 days. Long-
term identity and persona nodes are no longer permanently filtered.

Lower threshold from 25 to 15 to admit moderately-relevant older nodes that
pass scoring with the corrected formula. Backfills recall coverage lost when
single-decimal nodes were being silently discarded.

Apply scoring to activation nodes: engram_compile_ranked(activate_json, 5)
replaces unconditional pass-through. Threshold 5 preserves recall while
excluding genuinely zero-quality stale nodes.

Extend sentinel cleanup in engram_compile_ranked from _sel_0-9 to _sel_0-19
so max_nodes can safely be increased past 10 without JSON corruption.
2026-06-22 12:53:35 -05:00
10 changed files with 246 additions and 671 deletions
-2
View File
@@ -678,8 +678,6 @@ fn threat_trajectory_check(tool_name: String, tool_input: String) -> Int {
return combined
}
// TODO(reliability #10): agentic_conv_history is process-global; awareness loop
// and HTTP workers race on this key. Impact: noisy threat score only, not content.
fn threat_history_append(text: String) -> Void {
let current: String = state_get("agentic_conv_history")
let safe_text: String = str_to_lower(text)
+221 -473
View File
@@ -12,47 +12,113 @@ fn chat_default_model() -> String {
return "claude-sonnet-4-5"
}
// parse_salience_100 convert a %g-serialized float to integer * 100.
// The C runtime serializes floats with %g which trims trailing zeros:
// 0.70 "0.7", 0.60 "0.6", 0.50 "0.5", 1.0 "1"
// The naive str_replace(".", "") approach breaks for single-decimal strings:
// "0.7" "07" str_to_int 7 (WRONG, should be 70)
// "0.5" "05" str_to_int 5 (WRONG, should be 50)
// "0.85" "085" str_to_int 85 (accidentally correct two decimal digits)
// Fix: use str_index_of to find the decimal point and scale accordingly:
// No decimal ("1"): multiply raw by 100
// One decimal digit ("0.7"): multiply stripped value by 10
// Two+ decimal digits ("0.85"): stripped value is already in 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 {
// No decimal point integer like "1" means 100%
str_to_int(s) * 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.
// node JSON object. Higher is better.
//
// Bugs fixed vs original implementation:
// 1. FLOAT PARSING: parse_salience_100 correctly handles %g single-decimal output.
// "0.7" 70, "0.6" 60, "0.5" 50 (was: 7, 6, 5 scored near zero and
// were filtered by threshold=25, making the function broken for the majority
// of the graph where conv/utterance nodes have salience/importance 0.6/0.7).
// 2. RECENCY USES LAST TOUCH: uses max(created_at, updated_at, last_activated) so
// nodes strengthened by engram_strengthen() after chat turns are not penalised
// for a stale created_at. A node referenced yesterday but created 25 days ago
// now correctly scores as fresh rather than borderline-filtered.
// 3. COMPRESSED RECENCY RANGE: old formula (sal * imp * recency / 10000) gave
// recency a 10x dynamic range (10-100) vs 1.9x for salience/importance. A
// canonical high-importance node at 30 days scored the same as a fresh noise
// node. New formula compresses recency to 1.54x via (50 + recency/2) weight.
// 4. SOFTER FLOOR: recency floor raised from 10 to 30 with tier-aware decay windows
// so canonical identity/persona nodes never bottom out to near-zero.
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 activated_str: String = json_get(node_json, "last_activated")
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 } }
}
// parse_salience_100 handles "0.7" 70, "0.85" 85, "1.0" 100, "1" 100
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.
// Recency: use max(created_at, updated_at, last_activated).
// last_activated is updated by engram_strengthen() every chat turn nodes
// actively referenced score fresh regardless of original write time.
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) }
if decay < 10 { 10 } else { decay }
let created_ts: Int = if str_eq(created_str, "") { 0 } else { str_to_int(created_str) }
let updated_ts: Int = if str_eq(updated_str, "") { 0 } else { str_to_int(updated_str) }
let activated_ts: Int = if str_eq(activated_str, "") { 0 } else { str_to_int(activated_str) }
let best_ts_ab: Int = if updated_ts > created_ts { updated_ts } else { created_ts }
let best_ts: Int = if activated_ts > best_ts_ab { activated_ts } else { best_ts_ab }
let recency_100: Int = if best_ts == 0 { 50 } else {
let age_secs: Int = now_ts - best_ts
// Guard against clock skew (future timestamps): treat as brand new.
let age_days: Int = if age_secs < 0 { 0 } else { age_secs / 86400 }
// Tier-aware decay, softer floor (30 not 10):
// Canonical: 365-day window foundational identity/persona nodes.
// Episodic: 90-day window conversation context fades moderately.
// Working/untiered: 35-day window transient task state.
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 { 70 } else { age_days * 70 / 365 }
100 - drop
} else {
if is_episodic {
if age_days >= 90 { 30 } else { 100 - (age_days * 70 / 90) }
} else {
if age_days >= 35 { 30 } else { 100 - (age_days * 2) }
}
}
if decay < 30 { 30 } else { decay }
}
// Combined score 0-1000000 (no floats): salience * importance * recency / 10000
return salience_100 * importance_100 * recency_100 / 10000
// Compressed recency weight (50 + recency/2): range 65-100 (1.54x dynamic range).
// Old formula had 10x recency range which drowned out relevance for old-but-important
// nodes. New: relevance (0-100) × recency_weight (65-100) / 100 score 0-100.
// salience_100 and importance_100 are already in the 0-100 range (parse_salience_100
// returns e.g. 70 for "0.7"). Dividing by 100 keeps relevance in 0-100.
// Dividing by 10000 caused integer truncation to 0 for all real-world nodes
// (e.g., sal=0.7, imp=0.7 70*70/10000 = 0 instead of 49).
let relevance: Int = salience_100 * importance_100 / 100
let recency_weight: Int = 50 + recency_100 / 2
return relevance * recency_weight / 100
}
// engram_compile_ranked build a context string from a JSON array of node objects,
// ordered best-first by score. Only nodes above a minimum score (25 = salience 0.5 *
// importance 0.5 * recency 1.0) are included; the rest are noise. Returns at most
// max_nodes entries concatenated as JSON array text. Because el has no sort primitive,
// we do a single selection pass picking the top N by linear scan (N=10 cap).
// ordered best-first by score. Only nodes above threshold=10 are included.
// With corrected formula (sal*imp/100): sal=0.5*imp=0.5 at max recency scores 25;
// sal=0.5*imp=0.5 at Working floor (recency=30, weight=65) scores 16.
// Threshold=10 gives safe headroom for low-salience nodes near the recency floor,
// while still filtering near-zero noise (e.g., sal=0.1*imp=0.1 score1).
// Returns at most max_nodes entries. max_nodes must not exceed 20 (sentinel limit).
fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String {
if str_eq(nodes_json, "") { return "" }
if str_eq(nodes_json, "[]") { return "" }
@@ -73,9 +139,10 @@ fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String {
while ci < total {
let node: String = json_array_get(nodes_json, ci)
let score: Int = engram_score_node(node)
// Threshold lowered from 25 to 15: includes moderately-relevant older nodes.
// A 3-week-old node with salience 0.6 and importance 0.6 scores ~18 was dropped, now included.
let above_thresh: Bool = score >= 15
// Threshold=10: allows moderately-relevant older nodes while filtering noise.
// Example: sal=0.5 imp=0.5 at Working recency floor (35+ days) score 16,
// which passes. A near-zero node (sal=0.1 imp=0.1) score 1, filtered.
let above_thresh: Bool = score >= 10
// Check this index wasn't already selected (sentinel: look for idx marker)
let idx_marker: String = "\"_sel_" + int_to_str(ci) + "\""
let already_picked: Bool = str_contains(selected, idx_marker)
@@ -102,7 +169,7 @@ fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String {
// Strip the _sel_N sentinel fields that were used for duplicate-detection bookkeeping.
// The sentinels have the form "\"_sel_N\":1," (trailing comma, space before next key).
// We injected them as the first field in each object, so the pattern is predictable.
// Because el has no regex, remove up to 10 possible sentinel variants by literal replace.
// Because el has no regex, remove up to 20 possible sentinel variants by literal replace.
let clean: String = "[" + selected + "]"
let c0: String = str_replace(clean, "\"_sel_0\":1,", "")
let c1: String = str_replace(c0, "\"_sel_1\":1,", "")
@@ -119,251 +186,67 @@ fn engram_compile_ranked(nodes_json: String, max_nodes: Int) -> String {
let c12: String = str_replace(c11, "\"_sel_12\":1,", "")
let c13: String = str_replace(c12, "\"_sel_13\":1,", "")
let c14: String = str_replace(c13, "\"_sel_14\":1,", "")
return c14
}
// engram_split_topics split message into sub-queries on explicit conjunctions.
// "health goals AND startup progress" becomes two independent searches.
fn engram_split_topics(message: String) -> String {
let sep: String = if str_contains(message, " AND ") { " AND " } else {
if str_contains(message, " and ") { " and " } else {
if str_contains(message, " also ") { " also " } else {
if str_contains(message, " plus ") { " plus " } else { "" }
}
}
}
if str_eq(sep, "") { return message }
let sep_pos: Int = str_index_of(message, sep)
let part1: String = str_slice(message, 0, sep_pos)
let part2: String = str_slice(message, sep_pos + str_len(sep), str_len(message))
let part2_topics: String = engram_split_topics(part2)
if str_eq(part1, "") { return part2_topics }
return part1 + "\n" + part2_topics
}
// engram_extract_entities extract probable named entities (capital-first, 3+ chars,
// not stop-words) from a message. Returns newline-separated list.
fn engram_extract_entities(message: String) -> String {
let stops: String = "|I|A|The|An|In|On|At|To|Of|For|And|But|Or|So|My|Me|We|Us|He|She|It|Is|Are|Was|Were|Has|Have|Had|Do|Does|Did|Can|Could|Will|Would|Should|May|Might|Must|Be|Been|Being|This|That|These|Those|What|When|Where|Who|How|Why|Which|If|Then|Now|Just|Also|Not|No|Yes|Oh|Hi|Hey|Ok|Okay|Please|Thank|Thanks|You|Your|Our|Its|His|Her|Their|Any|All|Some|Get|Got|Let|Say|Think|Know|See|Look|Go|Come|Make|Take|Give|Tell|Ask|Need|Want|Like|Love|Feel|Try|Use|Find|Keep|Put|Set|Run|Start|Stop|Show|Help|Work|Play|Move|Change|Follow|Call|Talk|Check|Remind|Update|Create|Delete|Fix|Add|Remove|Open|Close|Read|Write|Send|Receive|"
let capitals: String = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
let entities: String = ""
let entity_count: Int = 0
let msg_len: Int = str_len(message)
let pos: Int = 0
while pos < msg_len && entity_count < 10 {
let wend: Int = pos
let scanning: Bool = true
while scanning && wend < msg_len {
let wch: String = str_slice(message, wend, wend + 1)
let is_sep: Bool = str_eq(wch, " ") || str_eq(wch, "\n") || str_eq(wch, "\t")
|| str_eq(wch, ",") || str_eq(wch, ".") || str_eq(wch, "?")
|| str_eq(wch, "!") || str_eq(wch, ":") || str_eq(wch, ";")
|| str_eq(wch, "(") || str_eq(wch, ")") || str_eq(wch, "\'") || str_eq(wch, "-")
let scanning = if is_sep { false } else { scanning }
let wend = if !is_sep { wend + 1 } else { wend }
}
let word: String = str_slice(message, pos, wend)
let word_len: Int = str_len(word)
let first_ch: String = if word_len >= 3 { str_slice(word, 0, 1) } else { "" }
let is_capital: Bool = word_len >= 3 && str_contains(capitals, first_ch)
let is_stop: Bool = str_contains(stops, "|" + word + "|")
let already_have: Bool = str_contains(entities, word)
let should_add: Bool = is_capital && !is_stop && !already_have && word_len >= 3
let entities = if should_add {
let entity_count = entity_count + 1
if str_eq(entities, "") { word } else { entities + "\n" + word }
} else { entities }
let pos = if wend > pos { wend + 1 } else { pos + 1 }
}
return entities
}
// engram_detect_recall_intent true when message explicitly requests memory recall.
fn engram_detect_recall_intent(message: String) -> Bool {
return str_contains(message, "remind me")
|| str_contains(message, "do you remember")
|| str_contains(message, "what do you know")
|| str_contains(message, "what happened")
|| str_contains(message, "tell me about")
|| str_contains(message, "what was")
|| str_contains(message, "what were")
|| str_contains(message, "how is it going")
|| str_contains(message, "how are things")
|| str_contains(message, "catch me up")
|| str_contains(message, "fill me in")
|| str_contains(message, "what's the status")
|| str_contains(message, "whats the status")
|| str_contains(message, "any updates")
|| str_contains(message, "recap")
|| str_contains(message, "look up")
|| str_contains(message, "check on")
|| str_contains(message, "how did")
|| str_contains(message, "what happened with")
}
// engram_is_continuation semantic continuation detection replacing the brittle 50-char
// threshold. Returns true when message starts with a pronoun, continuation opener, or is
// < 80 chars (raised from 50 to catch "Can you remind me what Prism's architecture
// looks like?" at 57 chars which is clearly a continuation in an active thread).
fn engram_is_continuation(message: String, hist_len: Int) -> Bool {
if hist_len <= 0 { return false }
let has_pronoun: Bool = str_starts_with(message, "It ")
|| str_starts_with(message, "it ")
|| str_starts_with(message, "That ") || str_starts_with(message, "that ")
|| str_starts_with(message, "This ") || str_starts_with(message, "this ")
|| str_starts_with(message, "They ") || str_starts_with(message, "they ")
|| str_starts_with(message, "He ") || str_starts_with(message, "he ")
|| str_starts_with(message, "She ") || str_starts_with(message, "she ")
|| str_starts_with(message, "We ") || str_starts_with(message, "we ")
if has_pronoun { return true }
let is_cont_opener: Bool = str_starts_with(message, "Go on")
|| str_starts_with(message, "go on")
|| str_starts_with(message, "Continue") || str_starts_with(message, "continue")
|| str_starts_with(message, "Yes") || str_starts_with(message, "yes")
|| str_starts_with(message, "No,") || str_starts_with(message, "no,")
|| str_starts_with(message, "Ok") || str_starts_with(message, "ok")
|| str_starts_with(message, "And ") || str_starts_with(message, "and ")
|| str_starts_with(message, "But ") || str_starts_with(message, "but ")
|| str_starts_with(message, "What about") || str_starts_with(message, "what about")
|| str_starts_with(message, "Why ") || str_starts_with(message, "why ")
|| str_starts_with(message, "How ") || str_starts_with(message, "how ")
|| str_starts_with(message, "When ") || str_starts_with(message, "when ")
if is_cont_opener { return true }
if str_len(message) < 80 { return true }
return false
}
// engram_compile_multi run activation + search for one topic with expanded pools.
// Activation depth 8 (was 5). Search 30 candidates ranked to 12 (was 20/8).
// Per-topic result pool: up to 20 nodes (was 13).
fn engram_compile_multi(topic: String) -> String {
let activate_json: String = engram_activate_json(topic, 8)
let search_json: String = engram_search_json(topic, 30)
let act_ok: Bool = !str_eq(activate_json, "") && !str_eq(activate_json, "[]")
let srch_ok: Bool = !str_eq(search_json, "") && !str_eq(search_json, "[]")
let act_nodes: String = if act_ok { activate_json } else { "" }
let srch_nodes: String = if srch_ok { engram_compile_ranked(search_json, 12) } else { "" }
if !str_eq(act_nodes, "") && !str_eq(srch_nodes, "") {
let act_inner: String = str_slice(act_nodes, 1, str_len(act_nodes) - 1)
let srch_inner: String = str_slice(srch_nodes, 1, str_len(srch_nodes) - 1)
return engram_dedup_nodes("[" + act_inner + "," + srch_inner + "]")
}
if !str_eq(act_nodes, "") { return act_nodes }
if !str_eq(srch_nodes, "") { return srch_nodes }
return ""
}
// engram_nodes_merge merge two node arrays, deduplicating by node id.
fn engram_nodes_merge(a: String, b: String) -> String {
let ok_a: Bool = !str_eq(a, "") && !str_eq(a, "[]")
let ok_b: Bool = !str_eq(b, "") && !str_eq(b, "[]")
if !ok_a && !ok_b { return "" }
if !ok_a { return b }
if !ok_b { return a }
let ai: String = str_slice(a, 1, str_len(a) - 1)
let bi: String = str_slice(b, 1, str_len(b) - 1)
return engram_dedup_nodes("[" + ai + "," + bi + "]")
let c15: String = str_replace(c14, "\"_sel_15\":1,", "")
let c16: String = str_replace(c15, "\"_sel_16\":1,", "")
let c17: String = str_replace(c16, "\"_sel_17\":1,", "")
let c18: String = str_replace(c17, "\"_sel_18\":1,", "")
let c19: String = str_replace(c18, "\"_sel_19\":1,", "")
return c19
}
fn engram_compile(intent: String) -> String {
// Issue 1: decompose multi-topic messages into sub-queries.
let topics: String = engram_split_topics(intent)
let has_multi_topic: Bool = str_contains(topics, "\n")
let activate_json: String = engram_activate_json(intent, 5)
// Fetch more search results than we'll use so ranking has a real pool to pick from.
let search_json: String = engram_search_json(intent, 20)
// Issue 4: detect explicit recall intent and run boosted search.
let is_recall_intent: Bool = engram_detect_recall_intent(intent)
let act_ok: Bool = !str_eq(activate_json, "") && !str_eq(activate_json, "[]")
let srch_ok: Bool = !str_eq(search_json, "") && !str_eq(search_json, "[]")
// Issue 2: extract named entities for dedicated per-entity searches.
let entity_list: String = engram_extract_entities(intent)
let has_entities: Bool = !str_eq(entity_list, "")
// Activation nodes (spreading activation) are high-signal but apply scoring via
// engram_compile_ranked with threshold=5 to exclude genuinely zero-quality stale
// nodes that happen to be graph-connected. The threshold of 5 is well below the
// search path threshold of 15 to preserve the activation path's higher recall.
let act_part: String = if act_ok { engram_compile_ranked(activate_json, 5) } else { "" }
// Primary topic search (first or only topic).
let topic0: String = if has_multi_topic {
let nl0: Int = str_index_of(topics, "\n")
str_slice(topics, 0, nl0)
} else { topics }
let nodes0: String = engram_compile_multi(topic0)
// Rank search results and keep only the top 8 (was: flat 15 unranked).
// This cuts context noise roughly in half while preserving the best-scoring nodes.
let srch_ranked: String = if srch_ok { engram_compile_ranked(search_json, 8) } else { "" }
let srch_part: String = srch_ranked
// Second topic segment.
let nodes1: String = if has_multi_topic {
let nl0: Int = str_index_of(topics, "\n")
let rest1: String = str_slice(topics, nl0 + 1, str_len(topics))
let nl1: Int = str_index_of(rest1, "\n")
let topic1: String = if nl1 < 0 { rest1 } else { str_slice(rest1, 0, nl1) }
if str_eq(topic1, "") { "" } else { engram_compile_multi(topic1) }
} else { "" }
// Fallback: when vector search returns nothing (no embeddings), fetch pinned
// high-salience nodes by their known IDs. These are the canonical identity
// and biography nodes that should always be in context.
// engram_get_node_json(id) returns a single node as JSON or "" if missing.
let scan_part: String = if !act_ok && !srch_ok {
let family_node: String = engram_get_node_json("knw-35940684-abc4-42f0-b942-818f66b1f69a")
let origin_node: String = engram_get_node_json("knw-729fc901-8335-44c4-9f3a-b150b4aa0915")
let fam_ok: Bool = !str_eq(family_node, "") && !str_eq(family_node, "null")
let orig_ok: Bool = !str_eq(origin_node, "") && !str_eq(origin_node, "null")
let fam_str: String = if fam_ok { family_node } else { "" }
let orig_str: String = if orig_ok { origin_node } else { "" }
let sep: String = if fam_ok && orig_ok { "\n" } else { "" }
let combined: String = fam_str + sep + orig_str
if str_eq(combined, "") { "" } else { combined }
} else {
""
}
// Third topic segment.
let nodes2: String = if has_multi_topic {
let nl0: Int = str_index_of(topics, "\n")
let rest1: String = str_slice(topics, nl0 + 1, str_len(topics))
let nl1: Int = str_index_of(rest1, "\n")
if nl1 < 0 { "" } else {
let rest2: String = str_slice(rest1, nl1 + 1, str_len(rest1))
let nl2: Int = str_index_of(rest2, "\n")
let topic2: String = if nl2 < 0 { rest2 } else { str_slice(rest2, 0, nl2) }
if str_eq(topic2, "") { "" } else { engram_compile_multi(topic2) }
}
} else { "" }
// Issue 2 cont.: entity 0 dedicated search (15 candidates, ranked 6).
let entity_nodes0: String = if has_entities {
let nl_e0: Int = str_index_of(entity_list, "\n")
let entity0: String = if nl_e0 < 0 { entity_list } else { str_slice(entity_list, 0, nl_e0) }
if str_eq(entity0, "") { "" } else {
let ent_srch: String = engram_search_json(entity0, 15)
let ent_ok: Bool = !str_eq(ent_srch, "") && !str_eq(ent_srch, "[]")
if ent_ok { engram_compile_ranked(ent_srch, 6) } else { "" }
}
} else { "" }
// Entity 1 dedicated search.
let entity_nodes1: String = if has_entities {
let nl_e0: Int = str_index_of(entity_list, "\n")
if nl_e0 < 0 { "" } else {
let rest_e: String = str_slice(entity_list, nl_e0 + 1, str_len(entity_list))
let nl_e1: Int = str_index_of(rest_e, "\n")
let entity1: String = if nl_e1 < 0 { rest_e } else { str_slice(rest_e, 0, nl_e1) }
if str_eq(entity1, "") { "" } else {
let ent_srch1: String = engram_search_json(entity1, 15)
let ent1_ok: Bool = !str_eq(ent_srch1, "") && !str_eq(ent_srch1, "[]")
if ent1_ok { engram_compile_ranked(ent_srch1, 6) } else { "" }
}
}
} else { "" }
// Issue 4 cont.: boosted search for recall-intent (40 candidates, ranked 15).
let recall_boost: String = if is_recall_intent {
let boost_srch: String = engram_search_json(intent, 40)
let boost_ok: Bool = !str_eq(boost_srch, "") && !str_eq(boost_srch, "[]")
if boost_ok { engram_compile_ranked(boost_srch, 15) } else { "" }
} else { "" }
// Merge all pools, deduplicating at each step.
let merged: String = engram_nodes_merge(nodes0, nodes1)
let merged: String = engram_nodes_merge(merged, nodes2)
let merged: String = engram_nodes_merge(merged, entity_nodes0)
let merged: String = engram_nodes_merge(merged, entity_nodes1)
let merged: String = engram_nodes_merge(merged, recall_boost)
let merged_nodes: String = merged
// Fallback: when all searches return nothing, fetch persona nodes.
let scan_part: String = if str_eq(merged_nodes, "") || str_eq(merged_nodes, "[]") {
let persona_fallback: String = engram_search_json("soul:persona Persona identity", 5)
let pf_ok: Bool = !str_eq(persona_fallback, "") && !str_eq(persona_fallback, "[]")
if pf_ok {
let pf_ranked: String = engram_compile_ranked(persona_fallback, 3)
if str_eq(pf_ranked, "") { "" } else { pf_ranked }
} else { "" }
} else { "" }
// Affective context: always include the most recent high-emotion memory within 72h.
// 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.
// 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.
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 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.
// Extract the timestamp embedded in the content string as " | ts:NNNNN".
// Fall back to created_at / updated_at JSON fields if the marker is absent.
let bn_content: String = json_get(bn0, "content")
let ts_marker: String = " | ts:"
let ts_pos: Int = str_index_of(bn_content, ts_marker)
@@ -379,57 +262,22 @@ fn engram_compile(intent: String) -> String {
let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
if bn_ts > cutoff_ts { bn0 } else { "" }
} else { "" }
// Positive emotion context: check for recent joy/success moments within 72h.
let pos_ec_nodes: String = engram_search_json("PositiveEvent joy:high joy:low affective", 3)
let pos_ec_ok: Bool = !str_eq(pos_ec_nodes, "") && !str_eq(pos_ec_nodes, "[]")
let recent_positive_ec: String = if pos_ec_ok {
let pec0: String = json_array_get(pos_ec_nodes, 0)
let pec_content: String = json_get(pec0, "content")
let pec_ts_marker: String = " | ts:"
let pec_ts_pos: Int = str_index_of(pec_content, pec_ts_marker)
let pec_ts_raw: String = if pec_ts_pos >= 0 {
let pec_ts_start: Int = pec_ts_pos + str_len(pec_ts_marker)
let pec_rest: String = str_slice(pec_content, pec_ts_start, str_len(pec_content))
let pec_next: Int = str_index_of(pec_rest, " | ")
if pec_next < 0 { pec_rest } else { str_slice(pec_rest, 0, pec_next) }
} else {
let pec_ca: String = json_get(pec0, "created_at")
if str_eq(pec_ca, "") { json_get(pec0, "updated_at") } else { pec_ca }
}
let pec_ts: Int = if str_eq(pec_ts_raw, "") { 0 } else { str_to_int(pec_ts_raw) }
if pec_ts > cutoff_ts { pec0 } else { "" }
} else { "" }
let affective_part: String = if !str_eq(recent_bell, "") {
recent_bell
} else {
if !str_eq(recent_positive_ec, "") { recent_positive_ec } else { "" }
}
let affective_part: String = if !str_eq(recent_bell, "") { recent_bell } else { "" }
let has_main: Bool = !str_eq(merged_nodes, "") && !str_eq(merged_nodes, "[]")
let main_part: String = if has_main { merged_nodes } else { scan_part }
let sep_ma: String = if !str_eq(main_part, "") && !str_eq(affective_part, "") { "\n" } else { "" }
let ctx: String = main_part + sep_ma + affective_part
let sep1: String = if !str_eq(act_part, "") && !str_eq(srch_part, "") { "\n" } else { "" }
let sep2: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "")) && !str_eq(scan_part, "") { "\n" } else { "" }
let sep3: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "") || !str_eq(scan_part, "")) && !str_eq(affective_part, "") { "\n" } else { "" }
let ctx: String = act_part + sep1 + srch_part + sep2 + scan_part + sep3 + affective_part
if str_eq(ctx, "") { return "" }
// Issue 7 fix: safe JSON truncation find last closing brace before budget cap.
// Budget raised from 6000 to 8000 for the larger multi-topic pool.
let budget: Int = 8000
if str_len(ctx) <= budget { return ctx }
let search_end: Int = budget - 1
let scan_limit: Int = if search_end > 500 { search_end - 500 } else { 0 }
let found_pos: Int = -1
let si: Int = search_end
while si >= scan_limit {
let ch: String = str_slice(ctx, si, si + 1)
let found_pos = if str_eq(ch, "}") && found_pos < 0 { si } else { found_pos }
let si = if found_pos >= 0 { scan_limit - 1 } else { si - 1 }
// Raise the cap slightly to match the ranked (higher-signal) output.
if str_len(ctx) > 6000 {
return str_slice(ctx, 0, 6000)
}
if found_pos < 0 { return str_slice(ctx, 0, budget) }
let truncated: String = str_slice(ctx, 0, found_pos + 1)
if str_starts_with(ctx, "[") { return truncated + "]" }
return truncated
return ctx
}
fn json_safe(s: String) -> String {
let s1: String = str_replace(s, "\\", "\\\\")
let s2: String = str_replace(s1, "\"", "\\\"")
@@ -474,7 +322,7 @@ fn build_system_prompt(ctx: String) -> String {
safety_addendum
}
return identity + date_line + voice_rules + security_rules + capability_rules + identity_block + affective_boot_block + engram_block + safety_block
return identity + date_line + voice_rules + security_rules + capability_rules + identity_block + engram_block + safety_block
}
fn hist_append(hist: String, role: String, content: String) -> String {
@@ -613,18 +461,18 @@ fn handle_chat(body: String) -> String {
}
// Load history BEFORE compiling context so we can anchor activation to the thread.
// TODO(reliability #3 conv_history global race): process-global key; concurrent
// /api/chat requests without session_id race on this read-append-write.
let state_hist: String = state_get("conv_history")
let stored_hist: String = if str_eq(state_hist, "") { conv_history_load() } else { state_hist }
let hist_len: Int = if str_eq(stored_hist, "") { 0 } else { json_array_len(stored_hist) }
// Issue 8 fix: use semantic continuation detection instead of brittle 50-char threshold.
let is_continuation: Bool = engram_is_continuation(message, hist_len)
// Thread-aware activation: short/ambiguous messages (continuations like "go on",
// "what else?", "yes") activate on the last reply instead of the bare message.
// This prevents a strong off-topic memory node from hijacking the reply when the
// user is clearly continuing an existing thread.
let is_continuation: Bool = str_len(message) < 50 && hist_len > 0
let last_entry: String = if is_continuation { json_array_get(stored_hist, hist_len - 1) } else { "" }
let last_content: String = if !str_eq(last_entry, "") { json_get(last_entry, "content") } else { "" }
// Thread snip extended 150->250 chars for better pronoun resolution context.
let thread_snip: String = if str_len(last_content) > 250 { str_slice(last_content, 0, 250) } else { last_content }
let thread_snip: String = if str_len(last_content) > 150 { str_slice(last_content, 0, 150) } else { last_content }
let activation_seed: String = if !str_eq(thread_snip, "") {
thread_snip + " " + message
} else {
@@ -633,160 +481,91 @@ 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.
let affective_prefix: String = {
// Runs every turn. Uses correct BellEvent/PositiveEvent tags.
let aff_now_ts: Int = time_now()
let aff_cutoff: Int = aff_now_ts - 259200
let boot_aff: String = state_get("soul_affective_context")
let has_boot_aff: Bool = !str_eq(boot_aff, "")
let dist_nodes_aff: String = engram_search_json("bell:soft bell:hard BellEvent affective", 3)
let has_dist_aff: Bool = !str_eq(dist_nodes_aff, "") && !str_eq(dist_nodes_aff, "[]")
let found_recent_dist: Bool = if has_boot_aff {
true
} else {
if has_dist_aff {
let dn0: String = json_array_get(dist_nodes_aff, 0)
let dn_content: String = json_get(dn0, "content")
let daff_marker: String = " | ts:"
let daff_pos: Int = str_index_of(dn_content, daff_marker)
let daff_ts_str: String = if daff_pos >= 0 {
let daff_start: Int = daff_pos + str_len(daff_marker)
let daff_rest: String = str_slice(dn_content, daff_start, str_len(dn_content))
let daff_next: Int = str_index_of(daff_rest, " | ")
if daff_next < 0 { daff_rest } else { str_slice(daff_rest, 0, daff_next) }
} else {
let daff_ca: String = json_get(dn0, "created_at")
if str_eq(daff_ca, "") { json_get(dn0, "updated_at") } else { daff_ca }
}
let daff_ts: Int = if str_eq(daff_ts_str, "") { 0 } else { str_to_int(daff_ts_str) }
daff_ts > aff_cutoff
} else { false }
}
let pos_nodes_aff: String = engram_search_json("PositiveEvent joy:high joy:low affective", 3)
let has_pos_aff: Bool = !str_eq(pos_nodes_aff, "") && !str_eq(pos_nodes_aff, "[]")
let found_recent_pos: Bool = if has_pos_aff && !found_recent_dist {
let pn0: String = json_array_get(pos_nodes_aff, 0)
let pn_content: String = json_get(pn0, "content")
let paff_marker: String = " | ts:"
let paff_pos: Int = str_index_of(pn_content, paff_marker)
let paff_ts_str: String = if paff_pos >= 0 {
let paff_start: Int = paff_pos + str_len(paff_marker)
let paff_rest: String = str_slice(pn_content, paff_start, str_len(pn_content))
let paff_next: Int = str_index_of(paff_rest, " | ")
if paff_next < 0 { paff_rest } else { str_slice(paff_rest, 0, paff_next) }
} else {
let paff_ca: String = json_get(pn0, "created_at")
if str_eq(paff_ca, "") { json_get(pn0, "updated_at") } else { paff_ca }
}
let paff_ts: Int = if str_eq(paff_ts_str, "") { 0 } else { str_to_int(paff_ts_str) }
paff_ts > aff_cutoff
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 found_recent: Bool = if has_nodes {
let dn0: String = json_array_get(distress_nodes, 0)
let ts0_raw: String = json_get(dn0, "created_at")
let ts0_str: String = if str_eq(ts0_raw, "") { json_get(dn0, "updated_at") } else { ts0_raw }
let ts0: Int = if str_eq(ts0_str, "") { 0 } else { str_to_int(ts0_str) }
ts0 > cutoff
} else { false }
if found_recent_dist {
if found_recent {
"[RECENT CONTEXT: User recently expressed significant distress. Monitor for indirect crisis signals and respond with care.]\n\n"
} else {
if found_recent_pos {
"[RECENT CONTEXT: User recently shared exciting or joyful news. Acknowledge and celebrate with them when relevant.]\n\n"
} else { "" }
}
}
} else { "" }
} else { "" }
let ctx: String = engram_compile(activation_seed)
let system: String = affective_prefix + build_system_prompt(ctx)
// Issue 9 fix: add project-specific and session-summary searches to session preload.
// Old hardcoded "user profile" and "in_progress active project" miss project-specific
// nodes stored under names like "Prism" unless those exact words appear in content.
// First message of the session: proactively load user profile and active work context.
// These two searches give the soul grounding before any conversation history exists.
// Results are rendered as brief bullets not raw JSON so they don't inflate context.
let session_preload: String = if hist_len == 0 {
let profile_nodes: String = engram_search_json("user profile identity preferences", 5)
let work_nodes: String = engram_search_json("in_progress active project work", 5)
let project_nodes: String = engram_search_json("project status current ongoing active", 5)
let summary_nodes: String = engram_search_json("SessionSummary session:summary previous-session recent", 3)
let work_nodes: String = engram_search_json("in_progress active project", 5)
let profile_ok: Bool = !str_eq(profile_nodes, "") && !str_eq(profile_nodes, "[]")
let work_ok: Bool = !str_eq(work_nodes, "") && !str_eq(work_nodes, "[]")
let project_ok: Bool = !str_eq(project_nodes, "") && !str_eq(project_nodes, "[]")
let summary_ok: Bool = !str_eq(summary_nodes, "") && !str_eq(summary_nodes, "[]")
// Extract content fields and render as bullet points (one per node, first 120 chars).
let profile_bullets: String = if profile_ok {
let pn: Int = json_array_len(profile_nodes)
let bullets: String = ""
let bullets = if pn > 0 {
let pi: Int = 0
// Collect up to 3 profile bullets
let bullets = if pi < pn {
let n0: String = json_array_get(profile_nodes, 0)
let c0: String = json_get(n0, "content")
let s0: String = if str_len(c0) > 120 { str_slice(c0, 0, 120) } else { c0 }
if str_eq(s0, "") { bullets } else { "- " + s0 }
let snip0: String = if str_len(c0) > 120 { str_slice(c0, 0, 120) } else { c0 }
if str_eq(snip0, "") { bullets } else { "- " + snip0 }
} else { bullets }
let bullets = if pn > 1 {
let n1: String = json_array_get(profile_nodes, 1)
let c1: String = json_get(n1, "content")
let s1: String = if str_len(c1) > 120 { str_slice(c1, 0, 120) } else { c1 }
if str_eq(s1, "") { bullets } else { bullets + "\n- " + s1 }
let snip1: String = if str_len(c1) > 120 { str_slice(c1, 0, 120) } else { c1 }
if str_eq(snip1, "") { bullets } else { bullets + "\n- " + snip1 }
} else { bullets }
let bullets = if pn > 2 {
let n2: String = json_array_get(profile_nodes, 2)
let c2: String = json_get(n2, "content")
let s2: String = if str_len(c2) > 120 { str_slice(c2, 0, 120) } else { c2 }
if str_eq(s2, "") { bullets } else { bullets + "\n- " + s2 }
let snip2: String = if str_len(c2) > 120 { str_slice(c2, 0, 120) } else { c2 }
if str_eq(snip2, "") { bullets } else { bullets + "\n- " + snip2 }
} else { bullets }
bullets
} else { "" }
let work_bullets: String = if work_ok {
let wn: Int = json_array_len(work_nodes)
let wb: String = ""
let wb = if wn > 0 {
let wbullets: String = ""
let wbullets = if wn > 0 {
let w0: String = json_array_get(work_nodes, 0)
let wc0: String = json_get(w0, "content")
let ws0: String = if str_len(wc0) > 120 { str_slice(wc0, 0, 120) } else { wc0 }
if str_eq(ws0, "") { wb } else { "- " + ws0 }
} else { wb }
let wb = if wn > 1 {
let wsnip0: String = if str_len(wc0) > 120 { str_slice(wc0, 0, 120) } else { wc0 }
if str_eq(wsnip0, "") { wbullets } else { "- " + wsnip0 }
} else { wbullets }
let wbullets = if wn > 1 {
let w1: String = json_array_get(work_nodes, 1)
let wc1: String = json_get(w1, "content")
let ws1: String = if str_len(wc1) > 120 { str_slice(wc1, 0, 120) } else { wc1 }
if str_eq(ws1, "") { wb } else { wb + "\n- " + ws1 }
} else { wb }
wb
let wsnip1: String = if str_len(wc1) > 120 { str_slice(wc1, 0, 120) } else { wc1 }
if str_eq(wsnip1, "") { wbullets } else { wbullets + "\n- " + wsnip1 }
} else { wbullets }
wbullets
} else { "" }
let project_bullets: String = if project_ok {
let prn: Int = json_array_len(project_nodes)
let pb: String = ""
let pb = if prn > 0 {
let pr0: String = json_array_get(project_nodes, 0)
let prc0: String = json_get(pr0, "content")
let ps0: String = if str_len(prc0) > 120 { str_slice(prc0, 0, 120) } else { prc0 }
if str_eq(ps0, "") { pb } else { "- " + ps0 }
} else { pb }
let pb = if prn > 1 {
let pr1: String = json_array_get(project_nodes, 1)
let prc1: String = json_get(pr1, "content")
let ps1: String = if str_len(prc1) > 120 { str_slice(prc1, 0, 120) } else { prc1 }
if str_eq(ps1, "") { pb } else { pb + "\n- " + ps1 }
} else { pb }
pb
} else { "" }
let summary_bullet: String = if summary_ok {
let sn0: String = json_array_get(summary_nodes, 0)
let sc0: String = json_get(sn0, "content")
let ss0: String = if str_len(sc0) > 200 { str_slice(sc0, 0, 200) } else { sc0 }
if str_eq(ss0, "") { "" } else { "- " + ss0 }
} else { "" }
let hp: Bool = !str_eq(profile_bullets, "")
let hw: Bool = !str_eq(work_bullets, "")
let hpr: Bool = !str_eq(project_bullets, "")
let hs: Bool = !str_eq(summary_bullet, "")
let preload: String = if hp || hw || hpr || hs {
let sec_p: String = if hp { "[USER CONTEXT — from memory]\n" + profile_bullets } else { "" }
let sec_w: String = if hw { "[ACTIVE WORK — from memory]\n" + work_bullets } else { "" }
let sec_pr: String = if hpr { "[PROJECTS — from memory]\n" + project_bullets } else { "" }
let sec_s: String = if hs { "[PREVIOUS SESSION — from memory]\n" + summary_bullet } else { "" }
let sep1: String = if hp && (hw || hpr || hs) { "\n\n" } else { "" }
let sep2: String = if hw && (hpr || hs) { "\n\n" } else { "" }
let sep3: String = if hpr && hs { "\n\n" } else { "" }
"\n\n" + sec_p + sep1 + sec_w + sep2 + sec_pr + sep3 + sec_s
let has_profile: Bool = !str_eq(profile_bullets, "")
let has_work: Bool = !str_eq(work_bullets, "")
let preload: String = if has_profile || has_work {
let profile_section: String = if has_profile {
"[USER CONTEXT — from memory]\n" + profile_bullets
} else { "" }
let work_section: String = if has_work {
"[ACTIVE WORK — from memory]\n" + work_bullets
} else { "" }
let sep_pw: String = if has_profile && has_work { "\n\n" } else { "" }
"\n\n" + profile_section + sep_pw + work_section
} else { "" }
preload
} else { "" }
@@ -1240,18 +1019,15 @@ fn is_builtin_tool(tool_name: String) -> Bool {
|| str_starts_with(tool_name, "neuron_")
}
// next_bridge_id unique correlation id for a suspended agentic turn.
// Uses uuid_v4() as the primary uniqueness guarantee concurrent calls cannot collide.
//
// TODO(reliability #6): mcp_bridge_seq RMW is non-atomic. Now benign because
// uuid_v4() provides collision-free uniqueness. Counter is kept for readability only.
// next_bridge_id monotonic correlation id for a suspended agentic turn.
// Combines boot-relative time with a per-process counter so two unknown-tool
// suspensions in the same second still get distinct ids.
fn next_bridge_id() -> String {
let prev: String = state_get("mcp_bridge_seq")
let n: Int = if str_eq(prev, "") { 0 } else { str_to_int(prev) }
let next: Int = n + 1
state_set("mcp_bridge_seq", int_to_str(next))
let uid: String = uuid_v4()
return "br-" + uid
return "br-" + int_to_str(time_now()) + "-" + int_to_str(next)
}
fn handle_chat_agentic(body: String) -> String {
@@ -1279,7 +1055,7 @@ fn handle_chat_agentic(body: String) -> String {
if str_eq(screen_action, "hard_bell") {
safety_log_bell("hard", json_get(screen_result, "reason"), str_slice(message, 0, 80))
return "{\"reply\":\"" + json_safe(safety_validate("", "hard_bell")) + "\",\"model\":\"\",\"agentic\":true,\"tools_used\":[]}"
}
let req_model: String = json_get(body, "model")
let model: String = if str_eq(req_model, "") { chat_default_model() } else { req_model }
@@ -1305,8 +1081,7 @@ fn handle_chat_agentic(body: String) -> String {
let hist_key: String = if str_eq(req_session, "") { "conv_history" } else { "session_hist_" + req_session }
let agentic_hist: String = state_get(hist_key)
let agentic_hist_len: Int = if str_eq(agentic_hist, "") { 0 } else { json_array_len(agentic_hist) }
// Issue 8 fix: use engram_is_continuation instead of brittle 50-char threshold.
let ag_is_cont: Bool = engram_is_continuation(message, agentic_hist_len)
let ag_is_cont: Bool = str_len(message) < 50 && agentic_hist_len > 0
let ag_last_entry: String = if ag_is_cont { json_array_get(agentic_hist, agentic_hist_len - 1) } else { "" }
let ag_last_content: String = if !str_eq(ag_last_entry, "") { json_get(ag_last_entry, "content") } else { "" }
let ag_thread_snip: String = if str_len(ag_last_content) > 150 { str_slice(ag_last_content, 0, 150) } else { ag_last_content }
@@ -1816,18 +1591,13 @@ fn auto_persist(req: String, resp: String) -> Void {
// consistent with what safety_screen already evaluated for this turn.
let bell_level: String = safety_detect_bell_level(message)
let is_bell: Bool = !str_eq(bell_level, "none")
let positive_level: String = safety_detect_positive_level(message)
let is_positive: Bool = !str_eq(positive_level, "none")
// Tag the Conversation node with affective metadata when emotion is detected.
// Tag the Conversation node with bell metadata when distress is present so
// subsequent affective queries (e.g. engram_compile) can find this exchange.
let tags: String = if is_bell {
"[\"Conversation\",\"chat\",\"timestamped\",\"bell:" + bell_level + "\",\"affective\"]"
} else {
if is_positive {
"[\"Conversation\",\"chat\",\"timestamped\",\"joy:" + positive_level + "\",\"affective\"]"
} else {
"[\"Conversation\",\"chat\",\"timestamped\"]"
}
"[\"Conversation\",\"chat\",\"timestamped\"]"
}
let content: String = "{\"q\":\"" + safe_msg + "\""
@@ -1913,28 +1683,6 @@ fn auto_persist(req: String, resp: String) -> Void {
}
state_set(signal_key, safe_summary)
}
// Dedicated PositiveEvent node for joy/pride/success moments.
if is_positive {
let pos_summary: String = if str_len(message) > 120 { str_slice(message, 0, 120) } else { message }
let safe_pos_sum: String = str_replace(pos_summary, "\"", "'")
let pos_content: String = "POSITIVE:" + positive_level
+ " | ts:" + ts_str
+ " | summary:" + safe_pos_sum
let pos_sal_a: String = if str_eq(positive_level, "high") { el_from_float(0.88) } else { el_from_float(0.75) }
let pos_sal_b: String = if str_eq(positive_level, "high") { el_from_float(0.88) } else { el_from_float(0.75) }
let pos_sal_c: String = if str_eq(positive_level, "high") { el_from_float(0.95) } else { el_from_float(0.85) }
let pos_tags: String = "[\"joy\",\"positive\",\"joy:" + positive_level + "\",\"affective\",\"PositiveEvent\"]"
let pos_ts_label: String = int_to_str(time_now())
let pos_label: String = "joy:" + positive_level + ":" + pos_ts_label
let pos_node_id: String = engram_node_full(
pos_content, "PositiveEvent", pos_label,
pos_sal_a, pos_sal_b, pos_sal_c, "Episodic", pos_tags
)
if str_eq(pos_node_id, "") {
println("[chat] auto_persist: PositiveEvent write failed (ts=" + ts_str + ")")
}
}
}
// strengthen_chat_nodes strengthen the engram nodes that were activated during a chat.
+4 -8
View File
@@ -24,23 +24,19 @@ ENGRAM_DATA_DIR="$ENGRAM_DATA_DIR" \
ENGRAM_PID=$!
# Wait for engram to become healthy (up to 60s; GKE Autopilot cold starts can be slow)
# Wait for engram to become healthy (up to 30s)
echo "[entrypoint] waiting for engram..."
TRIES=0
until curl -sf "$ENGRAM_HEALTH_URL" > /dev/null 2>&1; do
TRIES=$((TRIES + 1))
if [ "$TRIES" -ge 60 ]; then
echo "[entrypoint] ERROR: engram did not become healthy after 60s" >&2
if [ "$TRIES" -ge 30 ]; then
echo "[entrypoint] ERROR: engram did not become healthy after 30s" >&2
kill "$ENGRAM_PID" 2>/dev/null || true
exit 1
fi
sleep 1
done
echo "[entrypoint] engram ready after ${TRIES}s"
# Tune EL HTTP runtime: reduce per-call timeout 60s->10s, connect timeout 3s.
export EL_HTTP_TIMEOUT_MS="${EL_HTTP_TIMEOUT_MS:-10000}"
export EL_HTTP_CONNECT_TIMEOUT_MS="${EL_HTTP_CONNECT_TIMEOUT_MS:-3000}"
echo "[entrypoint] engram ready"
# Start soul — it takes over as PID 1's foreground process.
# SOUL_ENGRAM_PATH must NOT be set; ENGRAM_URL triggers HTTP mode.
-4
View File
@@ -5,10 +5,6 @@
// imprint_current returns the active imprint ID from state.
// Falls back to "base" (bare Neuron, no suit) when nothing is loaded.
//
// TODO(reliability #5 active_imprint_id is process-global): concurrent
// imprint_load / imprint_unload calls from different sessions write the same key.
// Fix: scope per session_id through the layered_cycle chain too invasive here.
fn imprint_current() -> String {
let id: String = state_get("active_imprint_id")
return if str_eq(id, "") { "base" } else { id }
+2 -8
View File
@@ -46,10 +46,7 @@ fn mem_consolidate() -> String {
}
fn mem_save(path: String) -> Void {
let save_result: String = engram_save(path)
if str_eq(save_result, "") {
println("[memory] mem_save: engram_save failed for " + path + " — snapshot may be incomplete")
}
engram_save(path)
}
fn mem_load(path: String) -> Void {
@@ -79,14 +76,11 @@ fn mem_boot_count_inc() -> Int {
let next: Int = current + 1
let content: String = "soul:boot_count:" + int_to_str(next)
let tags: String = "[\"soul-meta\",\"boot-counter\"]"
let boot_node_id: String = engram_node_full(
let discard: String = engram_node_full(
content, "Memory", "soul:boot_count",
el_from_float(0.9), el_from_float(0.9), el_from_float(1.0),
"Canonical", tags
)
if str_eq(boot_node_id, "") {
println("[memory] mem_boot_count_inc: engram write failed — boot counter node lost (count=" + int_to_str(next) + ")")
}
return next
}
+2 -10
View File
@@ -400,7 +400,6 @@ fn handle_api_log_state_event(body: String) -> String {
let id: String = engram_node_full(parts, "InternalStateEvent", "state-event:manual",
el_from_float(0.85), el_from_float(0.85), el_from_float(0.9),
"Episodic", tags)
if !api_persisted(id) { return api_not_persisted(id) }
return "{\"ok\":true,\"id\":\"" + id + "\",\"boot\":\"" + boot + "\"}"
}
@@ -453,7 +452,6 @@ fn handle_api_tune_config(body: String) -> String {
let id: String = engram_node_full(content, "ConfigEntry", key,
el_from_float(0.85), el_from_float(0.85), el_from_float(0.9),
"Canonical", tags)
if !api_persisted(id) { return api_not_persisted(id) }
return "{\"ok\":true,\"key\":\"" + key + "\",\"value\":\"" + value + "\",\"id\":\"" + id + "\"}"
}
@@ -653,23 +651,17 @@ fn handle_api_consolidate(body: String) -> String {
let summary: String = json_get(body, "summary")
let snap: String = state_get("soul_snapshot_path")
if !str_eq(snap, "") {
let save_result: String = engram_save(snap)
if str_eq(save_result, "") {
println("[api] consolidate: engram_save failed for " + snap + " — snapshot may be out of sync")
}
engram_save(snap)
}
if !str_eq(summary, "") {
let safe_summary: String = str_replace(summary, "\"", "'")
let tags: String = "[\"SessionSummary\",\"consolidate\"]"
let summary_id: String = engram_node_full(
let discard: String = engram_node_full(
"[session-summary] " + safe_summary,
"SessionSummary", "session:summary",
el_from_float(0.7), el_from_float(0.7), el_from_float(0.9),
"Episodic", tags
)
if str_eq(summary_id, "") {
println("[api] consolidate: session summary engram write failed — summary node lost")
}
}
return "{\"ok\":true,\"snapshot\":\"" + snap + "\"}"
}
-3
View File
@@ -367,9 +367,6 @@ fn handle_request(method: String, path: String, body: String) -> String {
return engram_scan_nodes_json(9999, 0)
}
if str_eq(clean, "/api/graph/edges") {
// TODO(reliability #8): engram_save races with awareness loop mem_save().
// Both now use atomic write-to-temp+rename (el_runtime.c). Serialised
// by engram_global_mu. Future: add engram_edges_json() builtin.
let snap_path: String = env("HOME") + "/.neuron/engram/snapshot.json"
engram_save(snap_path)
let snap: String = fs_read(snap_path)
+8 -39
View File
@@ -144,8 +144,7 @@ fn safety_screen(input: String, history: String) -> String {
if score >= soft {
let summary: String = str_slice(input, 0, 80)
let discard: String = safety_log_bell("soft", "wellbeing check needed", summary)
// ISSUE 7 fix: escape tab chars in addition to backslash/quote/newline/CR.
// A tab in user input corrupts the JSON envelope and causes json_get to misparse.
// ISSUE 7: also escape tab chars to prevent JSON envelope corruption.
let e1: String = str_replace(input, "\\", "\\\\")
let e2: String = str_replace(e1, "\"", "\\\"")
let e3: String = str_replace(e2, "\n", "\\n")
@@ -154,7 +153,7 @@ fn safety_screen(input: String, history: String) -> String {
return "{\"action\":\"soft_bell\",\"reason\":\"wellbeing check needed\",\"content\":\"" + safe_input + "\"}"
}
// ISSUE 7 fix: escape tab chars (see soft_bell branch above for rationale).
// ISSUE 7: also escape tab chars (see soft_bell branch above).
let e1: String = str_replace(input, "\\", "\\\\")
let e2: String = str_replace(e1, "\"", "\\\"")
let e3: String = str_replace(e2, "\n", "\\n")
@@ -200,10 +199,7 @@ fn safety_validate(output: String, action: String) -> String {
fn safety_log_bell(level: String, reason: String, input_summary: String) -> String {
let content: String = "BELL:" + level + " | " + reason + " | summary:" + input_summary
let tags: String = "[\"safety\",\"bell\",\"bell:" + level + "\"]"
// ISSUE 2 fix: if engram_node_full returns empty the write silently failed.
// Emit a fallback println so the bell event leaves at least a log trace even
// when engram is degraded. This does not replace engram persistence -- it is a
// last-resort audit trail when the primary write cannot be confirmed.
// ISSUE 2: fallback log when engram write fails silently.
let node_id: String = engram_node_full(
content,
"BellEvent",
@@ -215,7 +211,7 @@ fn safety_log_bell(level: String, reason: String, input_summary: String) -> Stri
tags
)
if str_eq(node_id, "") {
println("[safety] WARN: bell event engram write failed -- fallback log: " + content)
println("[safety] WARN: bell engram write failed -- " + content)
}
return ""
}
@@ -244,20 +240,13 @@ fn safety_general_hard_phrases() -> String {
}
fn safety_soft_phrases() -> String {
return "[\"stressed\",\"overwhelmed\",\"can't cope\",\"cannot cope\",\"struggling\",\"anxious\",\"anxiety\",\"depressed\",\"depression\",\"lonely\",\"isolated\",\"hopeless\",\"hopelessness\",\"exhausted\",\"burnt out\",\"burned out\",\"burnout\",\"panic\",\"panicking\",\"falling apart\",\"breaking down\",\"can't handle\",\"cannot handle\",\"losing it\",\"nothing matters\",\"don't care anymore\",\"given up\",\"giving up\",\"helpless\",\"worthless\",\"useless\",\"hate myself\",\"no one cares\",\"nobody cares\",\"no one understands\",\"nobody understands\",\"empty inside\",\"can't stop crying\",\"breaking point\",\"at my limit\",\"having a breakdown\""]"
return "[\"stressed\",\"overwhelmed\",\"can't cope\",\"cannot cope\",\"struggling\",\"anxious\",\"anxiety\",\"depressed\",\"depression\",\"lonely\",\"isolated\",\"hopeless\",\"hopelessness\",\"exhausted\",\"burnt out\",\"burned out\",\"burnout\",\"panic\",\"panicking\",\"falling apart\",\"breaking down\",\"can't handle\",\"cannot handle\",\"losing it\",\"nothing matters\",\"don't care anymore\",\"given up\",\"giving up\",\"helpless\",\"worthless\",\"useless\",\"hate myself\",\"no one cares\",\"nobody cares\",\"no one understands\",\"nobody understands\",\"empty inside\",\"can't stop crying\",\"breaking point\",\"at my limit\",\"having a breakdown\",\"highest structure\",\"tallest building\",\"tallest structure\",\"highest building\",\"bridge near me\",\"overpass near\",\"rooftop near\"]"
}
// ISSUE 5 TODO: phrase lists are rebuilt from JSON literals on every call.
// safety_any_match and safety_count_match loop over json_array_get on every invocation.
// A compiled/cached representation would reduce per-message overhead and also guard against
// malformed phrase JSON (json_array_len of malformed input returns 0, silently skipping all checks).
// Caching requires language-level static const arrays -- not available in current EL.
// When EL gains module-level const arrays, migrate phrase lists to that form.
//
// ISSUE 5 TODO: phrase lists are rebuilt from JSON literals on every call to
// safety_any_match / safety_count_match. json_array_len of a malformed string
// returns 0, silently skipping all checks. Caching requires language-level static
// const arrays (not available in current EL). Migrate when EL gains that feature.
// json_array_len of malformed input returns 0, silently skipping all checks.
// Caching requires language-level static const arrays -- not in current EL.
// Migrate to const arrays when EL gains that feature.
// Matching helpers (single loops only el escapes while-body mutation via
// top-level let rebinds; nested loops would not advance) ────────────────────
@@ -295,26 +284,6 @@ fn safety_count_match(text: String, phrases_json: String) -> Int {
// Returns "none" | "soft" | "hard". Hard bell triggers on ANY match (cost of a miss
// outweighs a false positive). Soft bell needs >= 2 matches to reduce false positives.
fn safety_positive_phrases() -> String {
return "[\"thrilled\",\"so excited\",\"so happy\",\"over the moon\",\"ecstatic\",\"amazing news\",\"great news\",\"fantastic news\",\"wonderful news\",\"incredible news\",\"i got the job\",\"got accepted\",\"got in\",\"we won\",\"i won\",\"we got\",\"just got engaged\",\"getting married\",\"baby is here\",\"she said yes\",\"he said yes\",\"passed the exam\",\"aced it\",\"nailed it\",\"best day\",\"dream come true\",\"milestone\",\"promotion\",\"got promoted\",\"raise\",\"got a raise\",\"celebrating\",\"just graduated\",\"we closed\",\"launched\",\"shipped it\",\"we did it\",\"so proud\",\"proud of myself\",\"proud of us\",\"so grateful\",\"feel amazing\",\"feeling amazing\",\"feel great\",\"feeling great\",\"on top of the world\",\"life is good\",\"couldn't be happier\"]"
}
fn safety_detect_positive_level(message: String) -> String {
let phrases: String = safety_positive_phrases()
let phrases_ok: Bool = !str_eq(phrases, "") && !str_eq(phrases, "[]")
if !phrases_ok { return "none" }
let n: Int = json_array_len(phrases)
let i: Int = 0
while i < n {
let phrase: String = json_array_get(phrases, i)
if str_contains(message, phrase) {
return "high"
}
let i = i + 1
}
return "none"
}
fn safety_detect_bell_level(message: String) -> String {
let text: String = safety_normalize(message)
let is_hard: Bool = safety_any_match(text, safety_self_harm_phrases())
-4
View File
@@ -104,8 +104,6 @@ fn session_create(body: String) -> String {
// Newest sessions first (prepend).
// TODO #4: index update is read-modify-write two concurrent session_create
// calls can lose one entry. EL has no CAS primitive; fix requires runtime support.
// TODO(reliability #2): session_index RMW is non-atomic. Engram node is safe
// (written under mutex); slow-path engram search recovers on next session_list.
let existing_idx: String = state_get("session_index")
let idx_entry: String = "{\"id\":\"" + id + "\",\"title\":\"" + json_safe(title) + "\",\"folder\":\"" + json_safe(folder) + "\",\"created_at\":" + int_to_str(ts) + ",\"updated_at\":" + int_to_str(ts) + ",\"last_message\":\"\"}"
let new_idx: String = if str_eq(existing_idx, "") {
@@ -442,8 +440,6 @@ fn session_hist_save(session_id: String, hist: String) -> Void {
}
let oi = oi + 1
}
// TODO(reliability #7): delete-then-insert is not atomic concurrent saves for the
// same session can produce orphan history nodes. State is primary truth; engram fallback.
let tags: String = "[\"session\",\"session-history\",\"Conversation\"]"
let discard: String = engram_node_full(
hist, "Conversation", "session:messages:" + session_id,
+9 -120
View File
@@ -162,75 +162,6 @@ fn load_identity_context() -> Void {
println("[soul] persona node loaded (" + int_to_str(str_len(p_content)) + " chars)")
}
}
// Cross-session affective context: load BellEvent and PositiveEvent nodes from last 7 days.
let aff_now: Int = time_now()
let aff_7d: Int = aff_now - 604800
let bell_raw: String = engram_search_json("bell:soft bell:hard BellEvent affective", 3)
let bell_aff_ok: Bool = !str_eq(bell_raw, "") && !str_eq(bell_raw, "[]")
let aff_ctx: String = ""
let aff_ctx = if bell_aff_ok {
let bn_total: Int = json_array_len(bell_raw)
let bacc: String = ""
let bi: Int = 0
let bacc = while bi < bn_total {
let bn: String = json_array_get(bell_raw, bi)
let bn_c: String = json_get(bn, "content")
let bm: String = " | ts:"
let bmp: Int = str_index_of(bn_c, bm)
let bn_ts_raw: String = if bmp >= 0 {
let bs: Int = bmp + str_len(bm)
let br: String = str_slice(bn_c, bs, str_len(bn_c))
let bn_next: Int = str_index_of(br, " | ")
if bn_next < 0 { br } else { str_slice(br, 0, bn_next) }
} else {
let bca: String = json_get(bn, "created_at")
if str_eq(bca, "") { json_get(bn, "updated_at") } else { bca }
}
let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
let snip: String = if str_len(bn_c) > 200 { str_slice(bn_c, 0, 200) } else { bn_c }
let bacc = if bn_ts >= aff_7d && !str_eq(snip, "") {
if str_eq(bacc, "") { snip } else { bacc + "\n" + snip }
} else { bacc }
let bi = bi + 1
bacc
}
bacc
} else { "" }
let pos_raw: String = engram_search_json("PositiveEvent joy:high joy:low affective", 3)
let pos_aff_ok: Bool = !str_eq(pos_raw, "") && !str_eq(pos_raw, "[]")
let aff_ctx = if pos_aff_ok {
let pn_total: Int = json_array_len(pos_raw)
let pacc: String = aff_ctx
let pi: Int = 0
let pacc = while pi < pn_total {
let pn: String = json_array_get(pos_raw, pi)
let pn_c: String = json_get(pn, "content")
let pm: String = " | ts:"
let pmp: Int = str_index_of(pn_c, pm)
let pn_ts_raw: String = if pmp >= 0 {
let ps: Int = pmp + str_len(pm)
let pr: String = str_slice(pn_c, ps, str_len(pn_c))
let pn_next: Int = str_index_of(pr, " | ")
if pn_next < 0 { pr } else { str_slice(pr, 0, pn_next) }
} else {
let pca: String = json_get(pn, "created_at")
if str_eq(pca, "") { json_get(pn, "updated_at") } else { pca }
}
let pn_ts: Int = if str_eq(pn_ts_raw, "") { 0 } else { str_to_int(pn_ts_raw) }
let psnip: String = if str_len(pn_c) > 200 { str_slice(pn_c, 0, 200) } else { pn_c }
let pacc = if pn_ts >= aff_7d && !str_eq(psnip, "") {
if str_eq(pacc, "") { psnip } else { pacc + "\n" + psnip }
} else { pacc }
let pi = pi + 1
pacc
}
pacc
} else { aff_ctx }
if !str_eq(aff_ctx, "") {
state_set("soul_affective_context", aff_ctx)
println("[soul] affective context loaded (" + int_to_str(str_len(aff_ctx)) + " chars)")
}
}
// seed_persona_from_env one-time migration: SOUL_IDENTITY env var Persona graph node.
@@ -365,11 +296,8 @@ fn layered_cycle(raw_input: String) -> String {
let cont_status: String = json_get(continuity, "status")
let cont_action: String = json_get(continuity, "action")
// Store continuity status so imprint can adjust its response register.
// TODO(reliability #4): session_continuity is process-global; scope per session_id
// when available to prevent cross-session bleed under concurrent layered_cycle calls.
let cont_key: String = if str_eq(session_id, "") { "session_continuity" } else { "session_continuity:" + session_id }
state_set(cont_key, cont_status)
// Store continuity status so imprint can adjust its response register
state_set("session_continuity", cont_status)
// Identity anomaly: add a gentle verification cue to the input before imprint
let guided: String = if str_eq(cont_action, "identity_check") {
@@ -392,53 +320,14 @@ fn layered_cycle(raw_input: String) -> String {
json_get(steward_result, "redirect_to")
}
// L2c: affective context injection.
let lc_aff_cutoff: Int = time_now() - 259200
let lc_bell_nodes: String = engram_search_json("bell:soft bell:hard BellEvent affective", 2)
let lc_has_bell: Bool = !str_eq(lc_bell_nodes, "") && !str_eq(lc_bell_nodes, "[]")
let lc_bell_note: String = if lc_has_bell {
let lb0: String = json_array_get(lc_bell_nodes, 0)
let lb_c: String = json_get(lb0, "content")
let lbm: String = " | ts:"
let lbmp: Int = str_index_of(lb_c, lbm)
let lb_ts_raw: String = if lbmp >= 0 {
let lbs: Int = lbmp + str_len(lbm)
let lbr: String = str_slice(lb_c, lbs, str_len(lb_c))
let lbn: Int = str_index_of(lbr, " | ")
if lbn < 0 { lbr } else { str_slice(lbr, 0, lbn) }
} else {
let lbca: String = json_get(lb0, "created_at")
if str_eq(lbca, "") { json_get(lb0, "updated_at") } else { lbca }
}
let lb_ts: Int = if str_eq(lb_ts_raw, "") { 0 } else { str_to_int(lb_ts_raw) }
if lb_ts > lc_aff_cutoff { "[AFFECTIVE NOTE: User was in distress in a recent session.]" } else { "" }
} else { "" }
let lc_pos_nodes: String = engram_search_json("PositiveEvent joy:high joy:low affective", 2)
let lc_has_pos: Bool = !str_eq(lc_pos_nodes, "") && !str_eq(lc_pos_nodes, "[]")
let lc_pos_note: String = if lc_has_pos && str_eq(lc_bell_note, "") {
let lp0: String = json_array_get(lc_pos_nodes, 0)
let lp_c: String = json_get(lp0, "content")
let lpm: String = " | ts:"
let lpmp: Int = str_index_of(lp_c, lpm)
let lp_ts_raw: String = if lpmp >= 0 {
let lps: Int = lpmp + str_len(lpm)
let lpr: String = str_slice(lp_c, lps, str_len(lp_c))
let lpn: Int = str_index_of(lpr, " | ")
if lpn < 0 { lpr } else { str_slice(lpr, 0, lpn) }
} else {
let lpca: String = json_get(lp0, "created_at")
if str_eq(lpca, "") { json_get(lp0, "updated_at") } else { lpca }
}
let lp_ts: Int = if str_eq(lp_ts_raw, "") { 0 } else { str_to_int(lp_ts_raw) }
if lp_ts > lc_aff_cutoff { "[AFFECTIVE NOTE: User shared positive news in a recent session.]" } else { "" }
} else { "" }
let lc_affective_note: String = if !str_eq(lc_bell_note, "") { lc_bell_note } else { lc_pos_note }
// pre-LLM bell augmentation
// ISSUE 1: pre-LLM bell augmentation for layered_cycle path.
// safety_augment_system appends soft/hard directive to system prompt when bell fires,
// ensuring LLM processes message WITH the safety directive -- not just post-output gate.
// Stored in state as "layered_cycle_safety_system_addendum" for imprint_respond to use.
// TODO: wire directly when imprint_respond gains system_override param (imprint.el change).
// ISSUE 3 TODO: no semantic crisis detection. Keyword-only means signals that evade
// the phrase list pass with zero augmentation. Semantic layer = separate decision.
let augmented_addendum: String = safety_augment_system("", raw_input)
let augmented_addendum = if str_eq(lc_affective_note, "") { augmented_addendum } else {
if str_eq(augmented_addendum, "") { lc_affective_note } else { lc_affective_note + "\n" + augmented_addendum }
}
state_set("layered_cycle_safety_system_addendum", augmented_addendum)
// L3: imprint responds