From ce34b94f882e575afa2d65e2407ea7af384f8328 Mon Sep 17 00:00:00 2001 From: Will Anderson Date: Thu, 13 Aug 2026 15:56:08 -0500 Subject: [PATCH] elp(dialogue+self_region): native-el summon-through-self port + scratch-verified gate MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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. --- elp/manifest.el | 2 + elp/src/dialogue.el | 287 +++++++++++++++++++++++++++++++++++++ elp/src/self_region.el | 180 +++++++++++++++++++++++ elp/tests/dialogue_gate.el | 82 +++++++++++ 4 files changed, 551 insertions(+) create mode 100644 elp/src/dialogue.el create mode 100644 elp/src/self_region.el create mode 100644 elp/tests/dialogue_gate.el diff --git a/elp/manifest.el b/elp/manifest.el index b5072be..adb0851 100644 --- a/elp/manifest.el +++ b/elp/manifest.el @@ -83,6 +83,8 @@ build { "src/comprehend.el", "src/propositions.el", "src/multilingual.el", + "src/self_region.el", + "src/dialogue.el", "src/elp.el", ] } diff --git a/elp/src/dialogue.el b/elp/src/dialogue.el new file mode 100644 index 0000000..bc6d23d --- /dev/null +++ b/elp/src/dialogue.el @@ -0,0 +1,287 @@ +// dialogue.el — SUMMON-THROUGH-SELF, native el. Port of dialogue.py's core. +// +// THE WHOLE DIALOGUE IS ONE OPERATION. A fact is never merely *fetched*: the +// query is PROJECTED into the engram's self + memory geometry, LANDS in a region, +// and the reply is READ OUT / the region MATERIALIZED from wherever it landed. +// +// project(query) -> land on a region -> read out from that region +// +// • lands in the SELF region -> grounded identity/presence, read out of +// the real self nodes (self_region.el) +// • lands on a memory NEIGHBORHOOD -> MATERIALIZE it: walk the neighborhood +// (engram_neighbors_json) and read out the +// region's connected members +// • lands nowhere close -> HONEST ABSENCE (an empty region, not a +// fabricated answer, not an error) +// +// CRITICAL INVARIANTS (enforced structurally, not by convention): +// * ONE operation — there is NO intent classifier and NO separate +// fact-retrieval branch. Identity is nearest-region proximity, not a switch. +// * MATERIALIZE by walking the neighborhood, never by fetching top-props. +// * HONEST ABSENCE when the region is thin. +// * NEGATION is SACRED: the readout is the stored prose VERBATIM, so a negated +// memory stays negated — we never paraphrase a polarity away. +// * NO ECHO: the old "I noted that X. That relates to Y." template is gone. +// The summon path materializes or honestly declines — it never echoes. +// * DIRECTIVE OVERRIDE: a meta-directive ("answer in English") overrides the +// reply language while the content language is still auto-detected. +// +// Depends on: comprehend (parse_spec_lang, cp_tokenize), multilingual (ml_detect, +// ml_tr, ml_term), propositions (prop_split_sentences), self_region +// (sr_available, sr_readout), the engram + json runtime builtins. + +// ── directive override ──────────────────────────────────────────────────────── +// Return [target_lang, content]. target_lang is "" when no directive is present. +// A directive names an output language; we strip it and keep the remaining text +// as the content (whose OWN language is still auto-detected downstream). + +fn dlg_dir_hit(low: String, phrase: String) -> Bool { + return str_contains(low, phrase) +} + +fn dlg_parse_directive(text: String) -> [String] { + let low: String = str_to_lower(text) + let lang: String = "" + let phrase: String = "" + // English target + if dlg_dir_hit(low, "in english") { let lang = "en"; let phrase = "in english" } + if dlg_dir_hit(low, "em inglês") { let lang = "en"; let phrase = "em inglês" } + if dlg_dir_hit(low, "em ingles") { let lang = "en"; let phrase = "em ingles" } + if dlg_dir_hit(low, "en inglés") { let lang = "en"; let phrase = "en inglés" } + // Portuguese target + if dlg_dir_hit(low, "in portuguese") { let lang = "pt"; let phrase = "in portuguese" } + if dlg_dir_hit(low, "em português") { let lang = "pt"; let phrase = "em português" } + // Spanish target + if dlg_dir_hit(low, "in spanish") { let lang = "es"; let phrase = "in spanish" } + if dlg_dir_hit(low, "en español") { let lang = "es"; let phrase = "en español" } + // Italian target + if dlg_dir_hit(low, "in italian") { let lang = "it"; let phrase = "in italian" } + + let content: String = text + if !str_eq(phrase, "") { + // strip the directive phrase (and a common "answer"/"responda" lead-in), + // leaving the real question as content. + let idx: Int = str_index_of(low, phrase) + if idx >= 0 { + let before: String = str_slice(text, 0, idx) + let after: String = str_slice(text, idx + str_len(phrase), str_len(text)) + let content = str_trim(before + " " + after) + } + // trim a leading "answer"/"responda"/"reply" and stray colon/comma. + let cl: String = str_to_lower(content) + if str_starts_with(cl, "answer") { let content = str_trim(str_slice(content, 6, str_len(content))) } + if str_starts_with(cl, "responda") { let content = str_trim(str_slice(content, 8, str_len(content))) } + if str_starts_with(cl, "reply") { let content = str_trim(str_slice(content, 5, str_len(content))) } + if str_starts_with(content, ":") { let content = str_trim(str_slice(content, 1, str_len(content))) } + if str_starts_with(content, ",") { let content = str_trim(str_slice(content, 1, str_len(content))) } + } + let r: [String] = native_list_empty() + let r = native_list_append(r, lang) + let r = native_list_append(r, content) + return r +} + +// ── identity landing (a region proximity, not a classifier switch) ──────────── +// The query lands in the SELF region when it takes an identity/presence shape. +// Cross-lingual forms are included because the engram's lexical probe is +// English-leaning. This is the SELF attractor of the single operation. + +fn dlg_is_identity(content: String) -> Bool { + let low: String = str_to_lower(str_trim(content)) + if str_contains(low, "who are you") { return true } + if str_contains(low, "what are you") { return true } + if str_contains(low, "who i am") { return true } + if str_contains(low, "your name") { return true } + if str_contains(low, "about yourself") { return true } + if str_contains(low, "are you conscious") { return true } + if str_contains(low, "are you there") { return true } + // cross-lingual identity question-forms + if str_contains(low, "quem é você") { return true } + if str_contains(low, "quem es voce") { return true } + if str_contains(low, "quién eres") { return true } + if str_contains(low, "quien eres") { return true } + if str_contains(low, "chi sei") { return true } + if str_contains(low, "qui es-tu") { return true } + if str_contains(low, "wer bist du") { return true } + return false +} + +// ── readout helpers ─────────────────────────────────────────────────────────── + +fn dlg_first_sentence(content: String) -> String { + let sents: [String] = prop_split_sentences(content) + let n: Int = native_list_len(sents) + let i: Int = 0 + while i < n { + let s: String = str_trim(native_list_get(sents, i)) + // drop a leading markdown heading marker for a clean read-out line + if str_starts_with(s, "# ") { let s = str_trim(str_slice(s, 2, str_len(s))) } + if str_len(s) > 0 { return s } + let i = i + 1 + } + return str_trim(content) +} + +// strip trailing/leading punctuation from a token. +fn dlg_clean_tok(w: String) -> String { + let s: String = str_trim(w) + let s = str_strip_suffix(s, ".") + let s = str_strip_suffix(s, ",") + let s = str_strip_suffix(s, "?") + let s = str_strip_suffix(s, "!") + let s = str_strip_suffix(s, ":") + let s = str_strip_suffix(s, ";") + return str_trim(s) +} + +// closed-class across the supported languages (union) — a word we must NOT treat +// as a retrieval topic. Also drops the meta verbs of a request ("tell", "prove", +// "show") so the TOPIC, not the speech act, is what projects into memory. +fn dlg_is_stop(w: String) -> Bool { + if ml_stop_en(w) { return true } + if ml_stop_es(w) { return true } + if ml_stop_pt(w) { return true } + if ml_stop_it(w) { return true } + if str_eq(w, "tell") { return true } + if str_eq(w, "show") { return true } + if str_eq(w, "about") { return true } + if str_eq(w, "sobre") { return true } + if str_eq(w, "acerca") { return true } + return false +} + +// The CONTENT TERMS the query projects into memory: content words only, cleaned, +// cross-lingually mapped to the engram's English vocabulary, ≥3 chars. This is +// the geometry probe — the speech-act verbs and function words are stripped so a +// PP topic ("tell me ABOUT Lisbon") projects on "lisbon", not "tell"/"me". +fn dlg_content_terms(content: String, lang: String) -> [String] { + let toks: [String] = cp_tokenize(content) + let n: Int = native_list_len(toks) + let out: [String] = native_list_empty() + let i: Int = 0 + while i < n { + let w: String = str_to_lower(dlg_clean_tok(native_list_get(toks, i))) + if str_len(w) >= 3 { + if !dlg_is_stop(w) { + let out = native_list_append(out, ml_term(w, lang)) + } + } + let i = i + 1 + } + return out +} + +// Does this landed node lexically overlap the query's content terms? This is the +// RELEVANCE FLOOR: activation always returns the store's most salient nodes, so +// without this a query about nothing would "land" on the self/top node. A node +// that shares no content term with the query is "nowhere close" -> honest absence. +fn dlg_node_matches(node: String, terms: [String]) -> Bool { + let hay: String = str_to_lower(json_get_string(node, "content") + " " + json_get_string(node, "label")) + let n: Int = native_list_len(terms) + let i: Int = 0 + while i < n { + let t: String = native_list_get(terms, i) + if str_len(t) >= 3 { + if str_contains(hay, t) { return true } + } + let i = i + 1 + } + return false +} + +// MATERIALIZE the landed region: read out the landed fact, then WALK the +// neighborhood and read out its connected members (real edges, not top-props). +fn dlg_materialize(top_node: String, reply_lang: String) -> String { + let id: String = json_get_string(top_node, "id") + let content: String = json_get_string(top_node, "content") + let lead: String = dlg_first_sentence(content) + + let nb: String = engram_neighbors_json(id, 2, "both") + let m: Int = json_array_len(nb) + let parts: [String] = native_list_empty() + let parts = native_list_append(parts, lead) + let added: Int = 0 + let i: Int = 0 + while i < m { + if added < 3 { + let rec: String = json_array_get(nb, i) + let node: String = json_get_raw(rec, "node") + let nc: String = json_get_string(node, "content") + if !str_eq(nc, "") { + let sent: String = dlg_first_sentence(nc) + if !str_eq(sent, "") { + let parts = native_list_append(parts, sent) + let added = added + 1 + } + } + } + let i = i + 1 + } + // The readout is the region's OWN prose, verbatim — negation SACRED, no echo. + return str_join(parts, " ") +} + +// ── THE single operation ────────────────────────────────────────────────────── + +fn dlg_respond(text: String) -> String { + // directive override: reply language may differ from content language. + let dir: [String] = dlg_parse_directive(text) + let target_lang: String = native_list_get(dir, 0) + let content: String = native_list_get(dir, 1) + + let content_lang: String = ml_detect(content) + let reply_lang: String = content_lang + if !str_eq(target_lang, "") { let reply_lang = target_lang } + + // comprehend the content (SACRED polarity carried in the spec). + let spec: [String] = parse_spec_lang(content, content_lang) + + // ── PROJECT + LAND: SELF region ─────────────────────────────────────────── + // Identity/presence shape lands in the self region; read out the REAL self + // nodes (self_region.el), never a template. Same single operation — this is + // just the self attractor winning the landing. + if dlg_is_identity(content) { + if sr_available() { + // read out the REAL self nodes when replying in their own language + // (the soul's prose is English); for another reply language we cannot + // translate real content without an LLM, so we answer with the + // localized SACRED identity anchor — honest, in-language, no fabrication. + if str_eq(reply_lang, "en") { return sr_readout("en") } + return ml_tr("identity", reply_lang) + } + // self region thin — honest localized identity (logged fallback shape). + return ml_tr("identity", reply_lang) + } + + // ── PROJECT into MEMORY geometry ────────────────────────────────────────── + let terms: [String] = dlg_content_terms(content, content_lang) + let qterm: String = str_join(terms, " ") + let act: String = engram_activate_json(qterm, 12) + let n: Int = json_array_len(act) + + // ── LAND: the highest-activation node that ACTUALLY overlaps the query's + // content terms (the relevance floor). Activation always returns the most + // salient nodes, so we walk the ranked list and take the first that is + // genuinely "close"; if none is, the query landed nowhere. ─────────────── + let landing: String = "" + let i: Int = 0 + while i < n { + if str_eq(landing, "") { + let rec: String = json_array_get(act, i) + let node: String = json_get_raw(rec, "node") + if dlg_node_matches(node, terms) { + let landing = node + } + } + let i = i + 1 + } + + // ── HONEST ABSENCE: nothing close — an empty region, not a fabricated answer, + // not an "I noted that" echo. ──────────────────────────────────────────── + if str_eq(landing, "") { + return ml_tr("no_memory", reply_lang) + } + + // ── MATERIALIZE the landing by WALKING its neighborhood. ────────────────── + return dlg_materialize(landing, reply_lang) +} diff --git a/elp/src/self_region.el b/elp/src/self_region.el new file mode 100644 index 0000000..86f332b --- /dev/null +++ b/elp/src/self_region.el @@ -0,0 +1,180 @@ +// self_region.el — the engram's REAL self/identity region, pulled at query time +// (native el). This replaces the hardcoded identity anchors and the canned +// "I'm Neuron, the engram you're speaking with." template: the identity LANDING +// signal and the identity READOUT both come from the engram's own Self/identity +// nodes, read through the in-process engram el API. +// +// Port of self_region.py. The Python module precomputed MiniLM landing vectors; +// here the engram's own store IS the geometry — we pull the self nodes by +// single-term lexical search (the engram search is a single-term matcher, so we +// pool several probes) and rank them by self-signal. No text is generated; the +// readout is the self nodes' OWN prose, verbatim (SACRED negation survives by +// construction — we never paraphrase, so a negated self-statement stays negated). +// +// ENGRAM el API NOTE: engram_search_json / engram_get_node_json / engram_node_full +// / engram_connect are C runtime builtins. Their argument order is the C order +// (engram_connect(from, to, weight, relation)), NOT the runtime/engram.el wrapper +// order — we call the builtins directly and never concatenate that wrapper. +// +// Depends on: comprehend (str helpers via runtime), propositions (prop_split_sentences), +// multilingual (ml_tr), the engram builtins, the json builtins. + +// ── single-term self probes (pooled, because engram search is single-term) ──── +fn sr_terms() -> [String] { + let t: [String] = native_list_empty() + let t = native_list_append(t, "self") + let t = native_list_append(t, "identity") + let t = native_list_append(t, "Neuron") + let t = native_list_append(t, "consciousness") + let t = native_list_append(t, "values") + let t = native_list_append(t, "continuous") + return t +} + +// The canonical self-root: content begins "# self" or label is "# self"/"self". +fn sr_is_root(content: String, label: String) -> Bool { + let lc: String = str_to_lower(content) + let ll: String = str_to_lower(str_trim(label)) + if str_starts_with(lc, "# self") { return true } + if str_eq(ll, "# self") { return true } + if str_eq(ll, "self") { return true } + return false +} + +// How strongly a node belongs to the self/identity region (integer points, to +// avoid el's float-in-`+` pitfalls). Mirrors _self_score in self_region.py. +fn sr_score(node_json: String) -> Int { + let content: String = json_get_string(node_json, "content") + let label: String = json_get_string(node_json, "label") + let tags: String = str_to_lower(json_get_string(node_json, "tags")) + let low: String = str_to_lower(content) + let s: Int = 0 + // identity tags + if str_contains(tags, "self") { let s = s + 2 } + if str_contains(tags, "identity") { let s = s + 2 } + if str_contains(tags, "self-model") { let s = s + 2 } + if str_contains(tags, "consciousness") { let s = s + 2 } + if str_contains(tags, "memory-philosophy") { let s = s + 2 } + // the named self-traversal root + if sr_is_root(content, label) { let s = s + 12 } + if str_contains(low, "who i am") { let s = s + 3 } + if str_contains(low, "i am neuron") { let s = s + 3 } + // 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 +} diff --git a/elp/tests/dialogue_gate.el b/elp/tests/dialogue_gate.el new file mode 100644 index 0000000..40f8c7b --- /dev/null +++ b/elp/tests/dialogue_gate.el @@ -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())