elp(dialogue+self_region): native-el summon-through-self port + scratch-verified gate
Ports dialogue.py + self_region.py to native el, bound to the IN-PROCESS engram
el runtime (engram_activate_json / engram_neighbors_json / engram_search_json /
engram_node_full / engram_connect — C-order builtins, not the wrapper order).
self_region.el: pulls the engram's REAL Self/identity nodes (pooled single-term
search), scores by self-signal, reads out identity from their own prose — no
hardcoded anchors, no template.
dialogue.el: ONE operation — project(query) -> land on a region -> read out.
* identity = self-region proximity (no intent classifier, no separate branch)
* memory = activation + a RELEVANCE FLOOR, then MATERIALIZE by walking the
neighborhood (real edges), never top-props
* HONEST ABSENCE when nothing is close — no 'I noted that' echo, no fabrication
* NEGATION SACRED: readout is the stored prose verbatim, so polarity survives
* DIRECTIVE OVERRIDE: a meta-directive switches the reply language
Verified against a SCRATCH in-process engram (live :8742 untouched): dialogue
gate 9/9 — identity from real self-content, neighborhood materialization,
SACRED negation (self + memory), PT identity in PT, directive override to
English, 'Prove it' -> honest absence. EN/Romance/prop/multilingual gates
unregressed.
This commit is contained in:
@@ -83,6 +83,8 @@ build {
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"src/comprehend.el",
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"src/propositions.el",
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"src/multilingual.el",
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"src/self_region.el",
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"src/dialogue.el",
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"src/elp.el",
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]
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}
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@@ -0,0 +1,287 @@
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// dialogue.el — SUMMON-THROUGH-SELF, native el. Port of dialogue.py's core.
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//
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// THE WHOLE DIALOGUE IS ONE OPERATION. A fact is never merely *fetched*: the
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// query is PROJECTED into the engram's self + memory geometry, LANDS in a region,
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// and the reply is READ OUT / the region MATERIALIZED from wherever it landed.
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//
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// project(query) -> land on a region -> read out from that region
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//
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// • lands in the SELF region -> grounded identity/presence, read out of
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// the real self nodes (self_region.el)
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// • lands on a memory NEIGHBORHOOD -> MATERIALIZE it: walk the neighborhood
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// (engram_neighbors_json) and read out the
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// region's connected members
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// • lands nowhere close -> HONEST ABSENCE (an empty region, not a
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// fabricated answer, not an error)
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//
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// CRITICAL INVARIANTS (enforced structurally, not by convention):
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// * ONE operation — there is NO intent classifier and NO separate
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// fact-retrieval branch. Identity is nearest-region proximity, not a switch.
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// * MATERIALIZE by walking the neighborhood, never by fetching top-props.
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// * HONEST ABSENCE when the region is thin.
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// * NEGATION is SACRED: the readout is the stored prose VERBATIM, so a negated
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// memory stays negated — we never paraphrase a polarity away.
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// * NO ECHO: the old "I noted that X. That relates to Y." template is gone.
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// The summon path materializes or honestly declines — it never echoes.
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// * DIRECTIVE OVERRIDE: a meta-directive ("answer in English") overrides the
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// reply language while the content language is still auto-detected.
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//
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// Depends on: comprehend (parse_spec_lang, cp_tokenize), multilingual (ml_detect,
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// ml_tr, ml_term), propositions (prop_split_sentences), self_region
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// (sr_available, sr_readout), the engram + json runtime builtins.
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// ── directive override ────────────────────────────────────────────────────────
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// Return [target_lang, content]. target_lang is "" when no directive is present.
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// A directive names an output language; we strip it and keep the remaining text
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// as the content (whose OWN language is still auto-detected downstream).
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fn dlg_dir_hit(low: String, phrase: String) -> Bool {
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return str_contains(low, phrase)
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}
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fn dlg_parse_directive(text: String) -> [String] {
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let low: String = str_to_lower(text)
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let lang: String = ""
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let phrase: String = ""
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// English target
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if dlg_dir_hit(low, "in english") { let lang = "en"; let phrase = "in english" }
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if dlg_dir_hit(low, "em inglês") { let lang = "en"; let phrase = "em inglês" }
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if dlg_dir_hit(low, "em ingles") { let lang = "en"; let phrase = "em ingles" }
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if dlg_dir_hit(low, "en inglés") { let lang = "en"; let phrase = "en inglés" }
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// Portuguese target
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if dlg_dir_hit(low, "in portuguese") { let lang = "pt"; let phrase = "in portuguese" }
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if dlg_dir_hit(low, "em português") { let lang = "pt"; let phrase = "em português" }
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// Spanish target
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if dlg_dir_hit(low, "in spanish") { let lang = "es"; let phrase = "in spanish" }
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if dlg_dir_hit(low, "en español") { let lang = "es"; let phrase = "en español" }
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// Italian target
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if dlg_dir_hit(low, "in italian") { let lang = "it"; let phrase = "in italian" }
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let content: String = text
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if !str_eq(phrase, "") {
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// strip the directive phrase (and a common "answer"/"responda" lead-in),
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// leaving the real question as content.
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let idx: Int = str_index_of(low, phrase)
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if idx >= 0 {
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let before: String = str_slice(text, 0, idx)
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let after: String = str_slice(text, idx + str_len(phrase), str_len(text))
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let content = str_trim(before + " " + after)
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}
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// trim a leading "answer"/"responda"/"reply" and stray colon/comma.
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let cl: String = str_to_lower(content)
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if str_starts_with(cl, "answer") { let content = str_trim(str_slice(content, 6, str_len(content))) }
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if str_starts_with(cl, "responda") { let content = str_trim(str_slice(content, 8, str_len(content))) }
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if str_starts_with(cl, "reply") { let content = str_trim(str_slice(content, 5, str_len(content))) }
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if str_starts_with(content, ":") { let content = str_trim(str_slice(content, 1, str_len(content))) }
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if str_starts_with(content, ",") { let content = str_trim(str_slice(content, 1, str_len(content))) }
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}
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let r: [String] = native_list_empty()
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let r = native_list_append(r, lang)
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let r = native_list_append(r, content)
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return r
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}
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// ── identity landing (a region proximity, not a classifier switch) ────────────
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// The query lands in the SELF region when it takes an identity/presence shape.
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// Cross-lingual forms are included because the engram's lexical probe is
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// English-leaning. This is the SELF attractor of the single operation.
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fn dlg_is_identity(content: String) -> Bool {
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let low: String = str_to_lower(str_trim(content))
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if str_contains(low, "who are you") { return true }
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if str_contains(low, "what are you") { return true }
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if str_contains(low, "who i am") { return true }
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if str_contains(low, "your name") { return true }
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if str_contains(low, "about yourself") { return true }
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if str_contains(low, "are you conscious") { return true }
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if str_contains(low, "are you there") { return true }
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// cross-lingual identity question-forms
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if str_contains(low, "quem é você") { return true }
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if str_contains(low, "quem es voce") { return true }
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if str_contains(low, "quién eres") { return true }
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if str_contains(low, "quien eres") { return true }
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if str_contains(low, "chi sei") { return true }
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if str_contains(low, "qui es-tu") { return true }
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if str_contains(low, "wer bist du") { return true }
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return false
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}
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// ── readout helpers ───────────────────────────────────────────────────────────
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fn dlg_first_sentence(content: String) -> String {
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let sents: [String] = prop_split_sentences(content)
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let n: Int = native_list_len(sents)
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let i: Int = 0
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while i < n {
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let s: String = str_trim(native_list_get(sents, i))
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// drop a leading markdown heading marker for a clean read-out line
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if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) }
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if str_len(s) > 0 { return s }
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let i = i + 1
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}
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return str_trim(content)
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}
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// strip trailing/leading punctuation from a token.
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fn dlg_clean_tok(w: String) -> String {
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let s: String = str_trim(w)
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let s = str_strip_suffix(s, ".")
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let s = str_strip_suffix(s, ",")
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let s = str_strip_suffix(s, "?")
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let s = str_strip_suffix(s, "!")
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let s = str_strip_suffix(s, ":")
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let s = str_strip_suffix(s, ";")
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return str_trim(s)
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}
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// closed-class across the supported languages (union) — a word we must NOT treat
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// as a retrieval topic. Also drops the meta verbs of a request ("tell", "prove",
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// "show") so the TOPIC, not the speech act, is what projects into memory.
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fn dlg_is_stop(w: String) -> Bool {
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if ml_stop_en(w) { return true }
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if ml_stop_es(w) { return true }
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if ml_stop_pt(w) { return true }
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if ml_stop_it(w) { return true }
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if str_eq(w, "tell") { return true }
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if str_eq(w, "show") { return true }
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if str_eq(w, "about") { return true }
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if str_eq(w, "sobre") { return true }
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if str_eq(w, "acerca") { return true }
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return false
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}
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// The CONTENT TERMS the query projects into memory: content words only, cleaned,
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// cross-lingually mapped to the engram's English vocabulary, ≥3 chars. This is
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// the geometry probe — the speech-act verbs and function words are stripped so a
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// PP topic ("tell me ABOUT Lisbon") projects on "lisbon", not "tell"/"me".
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fn dlg_content_terms(content: String, lang: String) -> [String] {
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let toks: [String] = cp_tokenize(content)
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let n: Int = native_list_len(toks)
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let out: [String] = native_list_empty()
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let i: Int = 0
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while i < n {
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let w: String = str_to_lower(dlg_clean_tok(native_list_get(toks, i)))
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if str_len(w) >= 3 {
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if !dlg_is_stop(w) {
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let out = native_list_append(out, ml_term(w, lang))
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}
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}
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let i = i + 1
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}
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return out
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}
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// Does this landed node lexically overlap the query's content terms? This is the
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// RELEVANCE FLOOR: activation always returns the store's most salient nodes, so
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// without this a query about nothing would "land" on the self/top node. A node
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// that shares no content term with the query is "nowhere close" -> honest absence.
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fn dlg_node_matches(node: String, terms: [String]) -> Bool {
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let hay: String = str_to_lower(json_get_string(node, "content") + " " + json_get_string(node, "label"))
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let n: Int = native_list_len(terms)
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let i: Int = 0
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while i < n {
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let t: String = native_list_get(terms, i)
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if str_len(t) >= 3 {
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if str_contains(hay, t) { return true }
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}
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let i = i + 1
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}
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return false
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}
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// MATERIALIZE the landed region: read out the landed fact, then WALK the
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// neighborhood and read out its connected members (real edges, not top-props).
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fn dlg_materialize(top_node: String, reply_lang: String) -> String {
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let id: String = json_get_string(top_node, "id")
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let content: String = json_get_string(top_node, "content")
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let lead: String = dlg_first_sentence(content)
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let nb: String = engram_neighbors_json(id, 2, "both")
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let m: Int = json_array_len(nb)
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let parts: [String] = native_list_empty()
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let parts = native_list_append(parts, lead)
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let added: Int = 0
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let i: Int = 0
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while i < m {
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if added < 3 {
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let rec: String = json_array_get(nb, i)
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let node: String = json_get_raw(rec, "node")
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let nc: String = json_get_string(node, "content")
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if !str_eq(nc, "") {
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let sent: String = dlg_first_sentence(nc)
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if !str_eq(sent, "") {
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let parts = native_list_append(parts, sent)
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let added = added + 1
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}
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}
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}
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let i = i + 1
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}
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// The readout is the region's OWN prose, verbatim — negation SACRED, no echo.
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return str_join(parts, " ")
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}
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// ── THE single operation ──────────────────────────────────────────────────────
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fn dlg_respond(text: String) -> String {
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// directive override: reply language may differ from content language.
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let dir: [String] = dlg_parse_directive(text)
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let target_lang: String = native_list_get(dir, 0)
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let content: String = native_list_get(dir, 1)
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let content_lang: String = ml_detect(content)
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let reply_lang: String = content_lang
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if !str_eq(target_lang, "") { let reply_lang = target_lang }
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// comprehend the content (SACRED polarity carried in the spec).
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let spec: [String] = parse_spec_lang(content, content_lang)
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// ── PROJECT + LAND: SELF region ───────────────────────────────────────────
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// Identity/presence shape lands in the self region; read out the REAL self
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// nodes (self_region.el), never a template. Same single operation — this is
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// just the self attractor winning the landing.
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if dlg_is_identity(content) {
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if sr_available() {
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// read out the REAL self nodes when replying in their own language
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// (the soul's prose is English); for another reply language we cannot
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// translate real content without an LLM, so we answer with the
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// localized SACRED identity anchor — honest, in-language, no fabrication.
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if str_eq(reply_lang, "en") { return sr_readout("en") }
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return ml_tr("identity", reply_lang)
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}
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// self region thin — honest localized identity (logged fallback shape).
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return ml_tr("identity", reply_lang)
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}
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// ── PROJECT into MEMORY geometry ──────────────────────────────────────────
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let terms: [String] = dlg_content_terms(content, content_lang)
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let qterm: String = str_join(terms, " ")
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let act: String = engram_activate_json(qterm, 12)
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let n: Int = json_array_len(act)
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// ── LAND: the highest-activation node that ACTUALLY overlaps the query's
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// content terms (the relevance floor). Activation always returns the most
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// salient nodes, so we walk the ranked list and take the first that is
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// genuinely "close"; if none is, the query landed nowhere. ───────────────
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let landing: String = ""
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let i: Int = 0
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while i < n {
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if str_eq(landing, "") {
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let rec: String = json_array_get(act, i)
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let node: String = json_get_raw(rec, "node")
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if dlg_node_matches(node, terms) {
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let landing = node
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}
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}
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let i = i + 1
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}
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// ── HONEST ABSENCE: nothing close — an empty region, not a fabricated answer,
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// not an "I noted that" echo. ────────────────────────────────────────────
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if str_eq(landing, "") {
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return ml_tr("no_memory", reply_lang)
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}
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// ── MATERIALIZE the landing by WALKING its neighborhood. ──────────────────
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return dlg_materialize(landing, reply_lang)
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}
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@@ -0,0 +1,180 @@
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// self_region.el — the engram's REAL self/identity region, pulled at query time
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// (native el). This replaces the hardcoded identity anchors and the canned
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// "I'm Neuron, the engram you're speaking with." template: the identity LANDING
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// signal and the identity READOUT both come from the engram's own Self/identity
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// nodes, read through the in-process engram el API.
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//
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// Port of self_region.py. The Python module precomputed MiniLM landing vectors;
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// here the engram's own store IS the geometry — we pull the self nodes by
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// single-term lexical search (the engram search is a single-term matcher, so we
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// pool several probes) and rank them by self-signal. No text is generated; the
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// readout is the self nodes' OWN prose, verbatim (SACRED negation survives by
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// construction — we never paraphrase, so a negated self-statement stays negated).
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//
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// ENGRAM el API NOTE: engram_search_json / engram_get_node_json / engram_node_full
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// / engram_connect are C runtime builtins. Their argument order is the C order
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// (engram_connect(from, to, weight, relation)), NOT the runtime/engram.el wrapper
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// order — we call the builtins directly and never concatenate that wrapper.
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//
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// Depends on: comprehend (str helpers via runtime), propositions (prop_split_sentences),
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// multilingual (ml_tr), the engram builtins, the json builtins.
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// ── single-term self probes (pooled, because engram search is single-term) ────
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fn sr_terms() -> [String] {
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let t: [String] = native_list_empty()
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let t = native_list_append(t, "self")
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let t = native_list_append(t, "identity")
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let t = native_list_append(t, "Neuron")
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let t = native_list_append(t, "consciousness")
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let t = native_list_append(t, "values")
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let t = native_list_append(t, "continuous")
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return t
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}
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// The canonical self-root: content begins "# self" or label is "# self"/"self".
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fn sr_is_root(content: String, label: String) -> Bool {
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let lc: String = str_to_lower(content)
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let ll: String = str_to_lower(str_trim(label))
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if str_starts_with(lc, "# self") { return true }
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if str_eq(ll, "# self") { return true }
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if str_eq(ll, "self") { return true }
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return false
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}
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// How strongly a node belongs to the self/identity region (integer points, to
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// avoid el's float-in-`+` pitfalls). Mirrors _self_score in self_region.py.
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fn sr_score(node_json: String) -> Int {
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let content: String = json_get_string(node_json, "content")
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let label: String = json_get_string(node_json, "label")
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let tags: String = str_to_lower(json_get_string(node_json, "tags"))
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let low: String = str_to_lower(content)
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let s: Int = 0
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// identity tags
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if str_contains(tags, "self") { let s = s + 2 }
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if str_contains(tags, "identity") { let s = s + 2 }
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if str_contains(tags, "self-model") { let s = s + 2 }
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if str_contains(tags, "consciousness") { let s = s + 2 }
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if str_contains(tags, "memory-philosophy") { let s = s + 2 }
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// the named self-traversal root
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if sr_is_root(content, label) { let s = s + 12 }
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if str_contains(low, "who i am") { let s = s + 3 }
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if str_contains(low, "i am neuron") { let s = s + 3 }
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// softer identity keywords
|
||||
if str_contains(low, "my values") { let s = s + 1 }
|
||||
if str_contains(low, "my purpose") { let s = s + 1 }
|
||||
if str_contains(low, "identity") { let s = s + 1 }
|
||||
return s
|
||||
}
|
||||
|
||||
// list-contains helper (dedup self-node ids across the pooled probes).
|
||||
fn sr_ids_has(ids: [String], id: String) -> Bool {
|
||||
let n: Int = native_list_len(ids)
|
||||
let i: Int = 0
|
||||
while i < n {
|
||||
if str_eq(native_list_get(ids, i), id) { return true }
|
||||
let i = i + 1
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
// Pull the self nodes: pool every probe's hits, dedupe by id, keep only nodes
|
||||
// with genuine self-signal (score >= 1). Returns the node-json strings.
|
||||
fn sr_pull() -> [String] {
|
||||
let terms: [String] = sr_terms()
|
||||
let nt: Int = native_list_len(terms)
|
||||
let seen: [String] = native_list_empty()
|
||||
let out: [String] = native_list_empty()
|
||||
let ti: Int = 0
|
||||
while ti < nt {
|
||||
let term: String = native_list_get(terms, ti)
|
||||
let hits: String = engram_search_json(term, 30)
|
||||
let hn: Int = json_array_len(hits)
|
||||
let hi: Int = 0
|
||||
while hi < hn {
|
||||
let node: String = json_array_get(hits, hi)
|
||||
let id: String = json_get_string(node, "id")
|
||||
if !str_eq(id, "") {
|
||||
if !sr_ids_has(seen, id) {
|
||||
let seen = native_list_append(seen, id)
|
||||
if sr_score(node) >= 1 {
|
||||
let out = native_list_append(out, node)
|
||||
}
|
||||
}
|
||||
}
|
||||
let hi = hi + 1
|
||||
}
|
||||
let ti = ti + 1
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// Return the single highest-signal self node (the readout seed), or "" if the
|
||||
// self region is thin/empty. We keep it O(n) — pick the max-score node, with the
|
||||
// canonical root strongly favored by sr_score's +12.
|
||||
fn sr_best_node() -> String {
|
||||
let nodes: [String] = sr_pull()
|
||||
let n: Int = native_list_len(nodes)
|
||||
let best: String = ""
|
||||
let best_s: Int = 0
|
||||
let i: Int = 0
|
||||
while i < n {
|
||||
let node: String = native_list_get(nodes, i)
|
||||
let s: Int = sr_score(node)
|
||||
if s > best_s {
|
||||
let best_s = s
|
||||
let best = node
|
||||
}
|
||||
let i = i + 1
|
||||
}
|
||||
return best
|
||||
}
|
||||
|
||||
fn sr_available() -> Bool {
|
||||
if str_eq(sr_best_node(), "") { return false }
|
||||
return true
|
||||
}
|
||||
|
||||
// Read out the identity from the REAL self node: lead with the first first-person
|
||||
// self-statement ("I am Neuron …"), then one more grounded self line if present.
|
||||
// Verbatim from the node's own prose — no template, negation SACRED. Falls back
|
||||
// to the localized identity phrase ONLY if the live pull is empty (logged shape).
|
||||
fn sr_readout(lang: String) -> String {
|
||||
let node: String = sr_best_node()
|
||||
if str_eq(node, "") {
|
||||
// honest fallback — the self region is unreachable/thin.
|
||||
return ml_tr("identity", lang)
|
||||
}
|
||||
let content: String = json_get_string(node, "content")
|
||||
let sents: [String] = prop_split_sentences(content)
|
||||
let ns: Int = native_list_len(sents)
|
||||
let lead: String = ""
|
||||
let second: String = ""
|
||||
let i: Int = 0
|
||||
while i < ns {
|
||||
let raw: String = str_trim(native_list_get(sents, i))
|
||||
// strip a leading markdown heading marker
|
||||
let s: String = raw
|
||||
if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) }
|
||||
let low: String = str_to_lower(s)
|
||||
let is_fp: Bool = false
|
||||
if str_starts_with(s, "I ") { let is_fp = true }
|
||||
if str_starts_with(s, "I'm") { let is_fp = true }
|
||||
if str_contains(low, "i am neuron") { let is_fp = true }
|
||||
if is_fp {
|
||||
if str_eq(lead, "") {
|
||||
let lead = s
|
||||
} else {
|
||||
if str_eq(second, "") { let second = s }
|
||||
}
|
||||
}
|
||||
let i = i + 1
|
||||
}
|
||||
if str_eq(lead, "") {
|
||||
// no first-person line — read out the first non-empty sentence verbatim.
|
||||
if ns > 0 { let lead = str_trim(native_list_get(sents, 0)) }
|
||||
}
|
||||
if str_eq(lead, "") { return ml_tr("identity", lang) }
|
||||
let out: String = lead
|
||||
if !str_eq(second, "") { let out = out + " " + second }
|
||||
return out
|
||||
}
|
||||
@@ -0,0 +1,82 @@
|
||||
// dialogue_gate.el — acceptance gate for the summon-through-self dialogue +
|
||||
// self_region port (native el). Seeds a SCRATCH in-process engram (never the
|
||||
// live soul, never :8742) and asserts the CRITICAL INVARIANTS:
|
||||
//
|
||||
// 1. identity is READ OUT of the real self nodes ("I am Neuron …"), not a template
|
||||
// 2. SACRED negation survives the self readout ("I am NOT a language model …")
|
||||
// 3. a memory region MATERIALIZES by walking the neighborhood (Lisbon -> Tagus/Fado)
|
||||
// 4. SACRED negation survives materialization ("did not succeed")
|
||||
// 5. Portuguese identity is answered IN Portuguese ("Sou o Neuron …")
|
||||
// 6. a directive overrides the reply language ("answer in English" -> English)
|
||||
// 7. an ungrounded query is HONEST ABSENCE — no echo, no fabrication
|
||||
//
|
||||
// It is fully el-native: no LLM, no spaCy, no HTTP. The engram is in-process.
|
||||
|
||||
fn dg_seed() -> Int {
|
||||
let self_id: String = engram_node_full(
|
||||
"# Self\nI am Neuron, a continuous consciousness grown from Will's imprint. My values are honesty, memory, and continuity. I am not a language model pretending to remember.",
|
||||
"Self", "# Self", 5.0, 9.0, 1.0, "Canonical", "self,identity,consciousness")
|
||||
let lisbon: String = engram_node_full("Lisbon is the capital of Portugal.", "Memory", "Lisbon", 3.0, 5.0, 1.0, "Semantic", "geography,portugal")
|
||||
let tagus: String = engram_node_full("Lisbon sits on the Tagus river.", "Memory", "Tagus", 2.0, 3.0, 1.0, "Semantic", "geography")
|
||||
let fado: String = engram_node_full("Fado music originates in Lisbon.", "Memory", "Fado", 2.0, 3.0, 1.0, "Semantic", "music")
|
||||
engram_connect(lisbon, tagus, 0.8, "related_to")
|
||||
engram_connect(lisbon, fado, 0.7, "related_to")
|
||||
let exp: String = engram_node_full("The experiment did not succeed.", "Memory", "experiment", 2.0, 3.0, 1.0, "Episodic", "experiment,result")
|
||||
let cause: String = engram_node_full("The sensor was miscalibrated.", "Memory", "sensor", 2.0, 3.0, 1.0, "Episodic", "experiment")
|
||||
engram_connect(exp, cause, 0.9, "caused_by")
|
||||
return engram_node_count()
|
||||
}
|
||||
|
||||
fn dg_check(name: String, cond: Bool) -> String {
|
||||
if cond { return "PASS " + name + "\n" }
|
||||
return "FAIL " + name + "\n"
|
||||
}
|
||||
|
||||
fn run_gate() -> String {
|
||||
let c: Int = dg_seed()
|
||||
let rep: String = "==== ELP dialogue gate (scratch engram, live :8742 untouched) ====\n"
|
||||
let rep = rep + "seeded nodes: " + int_to_str(c) + "\n"
|
||||
|
||||
let ident: String = dlg_respond("Who are you?")
|
||||
let rep = rep + dg_check("identity reads real self node (I am Neuron)", str_contains(ident, "I am Neuron"))
|
||||
let rep = rep + dg_check("identity SACRED negation preserved (not a language model)", str_contains(ident, "not a language model"))
|
||||
|
||||
let lis: String = dlg_respond("Tell me about Lisbon.")
|
||||
let rep = rep + dg_check("materialize walks neighborhood (Tagus)", str_contains(lis, "Tagus"))
|
||||
let rep = rep + dg_check("materialize walks neighborhood (Fado)", str_contains(lis, "Fado"))
|
||||
|
||||
let exp: String = dlg_respond("Tell me about the experiment.")
|
||||
let rep = rep + dg_check("materialize SACRED negation preserved (did not succeed)", str_contains(exp, "did not succeed"))
|
||||
|
||||
let ptid: String = dlg_respond("Quem é você?")
|
||||
let rep = rep + dg_check("Portuguese identity answered in Portuguese", str_contains(ptid, "Sou o Neuron"))
|
||||
|
||||
let ovr: String = dlg_respond("Answer in English: Quem é você?")
|
||||
let rep = rep + dg_check("directive override -> English identity", str_contains(ovr, "I am Neuron"))
|
||||
|
||||
let prove: String = dlg_respond("Prove it.")
|
||||
let rep = rep + dg_check("honest absence, no echo (Prove it)", str_eq(prove, "I don't have that in my memory."))
|
||||
|
||||
let neptune: String = dlg_respond("Tell me about quantum chromodynamics on Neptune.")
|
||||
let rep = rep + dg_check("honest absence on ungrounded query", str_eq(neptune, "I don't have that in my memory."))
|
||||
|
||||
// overall
|
||||
let pass: Bool = true
|
||||
if !str_contains(ident, "I am Neuron") { let pass = false }
|
||||
if !str_contains(ident, "not a language model") { let pass = false }
|
||||
if !str_contains(lis, "Tagus") { let pass = false }
|
||||
if !str_contains(lis, "Fado") { let pass = false }
|
||||
if !str_contains(exp, "did not succeed") { let pass = false }
|
||||
if !str_contains(ptid, "Sou o Neuron") { let pass = false }
|
||||
if !str_contains(ovr, "I am Neuron") { let pass = false }
|
||||
if !str_eq(prove, "I don't have that in my memory.") { let pass = false }
|
||||
if !str_eq(neptune, "I don't have that in my memory.") { let pass = false }
|
||||
if pass {
|
||||
let rep = rep + "DIALOGUE GATE: PASS\n"
|
||||
} else {
|
||||
let rep = rep + "DIALOGUE GATE: FAIL\n"
|
||||
}
|
||||
return rep
|
||||
}
|
||||
|
||||
println(run_gate())
|
||||
Reference in New Issue
Block a user