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// where it came from (observation, rule, etc.)\n note?: string;\n}\n\n// A contradiction snapshot: what we tried to add, and what broke.\nexport interface Contradiction {\n constraint: Constraint;\n previousDomain: Set;\n}\n\n// Core CAL state: domains + constraints + contradictions.\nexport class CALRuntime {\n private domains = new Map>();\n private constraints: Constraint[] = [];\n private contradictions: Contradiction[] = [];\n\n // Initialize a variable with an initial domain (if not present).\n initDomain(variable: string, initial: Iterable) {\n if (!this.domains.has(variable)) {\n this.domains.set(variable, new Set(initial));\n }\n }\n\n // Add a single constraint: intersect domain with allowed values.\n addConstraint(c: Constraint) {\n this.constraints.push(c);\n\n const prev = this.domains.get(c.variable);\n if (!prev) {\n // No prior domain: just adopt allowed set as domain.\n this.domains.set(c.variable, new Set(c.allowed));\n return;\n }\n\n const before = new Set(prev);\n const next = new Set();\n for (const v of prev) if (c.allowed.has(v)) next.add(v);\n\n if (next.size === 0) {\n // Contradiction: record, but do not destroy previous domain.\n this.contradictions.push({\n constraint: c,\n previousDomain: before,\n });\n return;\n }\n\n this.domains.set(c.variable, next);\n }\n\n // Add multiple constraints at once.\n addConstraints(cs: Constraint[]) {\n for (const c of cs) this.addConstraint(c);\n }\n\n // Observation → constraints (user-defined extractor).\n // Example: { animal: \"cat\", legs: \"4\" } → constraints on \"animal\", \"legs\".\n addObservation(\n obs: Record,\n source = \"observation\",\n note?: string,\n ) {\n const cs: Constraint[] = [];\n for (const [k, v] of Object.entries(obs)) {\n cs.push({\n id: `${source}:${k}:${v}:${Date.now()}:${Math.random()}`,\n variable: k,\n allowed: new Set([v]),\n source,\n note,\n });\n }\n this.addConstraints(cs);\n }\n\n // Query current possible values for a variable, plus supporting constraints.\n query(variable: string) {\n const domain = this.domains.get(variable);\n if (!domain) {\n return { variable, possible: [], supporting: [] as Constraint[] };\n }\n\n const supporting = this.constraints.filter((c) => c.variable === variable);\n return {\n variable,\n possible: [...domain],\n supporting,\n };\n }\n\n // Get all current domains.\n getDomains() {\n const out: Record = {};\n for (const [k, v] of this.domains.entries()) {\n out[k] = [...v];\n }\n return out;\n }\n\n // Get all constraints.\n getConstraints() {\n return [...this.constraints];\n }\n\n // Get all contradictions.\n getContradictions() {\n return [...this.contradictions];\n }\n}\n\n// ---------------- CAL LANGUAGE LAYER ----------------\n\ntype POS = \"N\" | \"V\" | \"DET\" | \"PRON\" | \"OTHER\";\n\ninterface LexEntry {\n lemma: string;\n pos: POS;\n}\n\n// Tiny lexicon; in a real system you’d hydrate this from HF vocab / tags.\nconst LEXICON: Record = {\n the: { lemma: \"the\", pos: \"DET\" },\n a: { lemma: \"a\", pos: \"DET\" },\n an: { lemma: \"an\", pos: \"DET\" },\n\n dog: { lemma: \"dog\", pos: \"N\" },\n dogs: { lemma: \"dog\", pos: \"N\" },\n cat: { lemma: \"cat\", pos: \"N\" },\n cats: { lemma: \"cat\", pos: \"N\" },\n ball: { lemma: \"ball\", pos: \"N\" },\n balls: { lemma: \"ball\", pos: \"N\" },\n man: { lemma: \"man\", pos: \"N\" },\n men: { lemma: \"man\", pos: \"N\" },\n woman: { lemma: \"woman\", pos: \"N\" },\n women: { lemma: \"woman\", pos: \"N\" },\n\n chased: { lemma: \"chase\", pos: \"V\" },\n chase: { lemma: \"chase\", pos: \"V\" },\n chases: { lemma: \"chase\", pos: \"V\" },\n\n kicked: { lemma: \"kick\", pos: \"V\" },\n kick: { lemma: \"kick\", pos: \"V\" },\n kicks: { lemma: \"kick\", pos: \"V\" },\n\n saw: { lemma: \"see\", pos: \"V\" },\n see: { lemma: \"see\", pos: \"V\" },\n sees: { lemma: \"see\", pos: \"V\" },\n\n he: { lemma: \"he\", pos: \"PRON\" },\n she: { lemma: \"she\", pos: \"PRON\" },\n they: { lemma: \"they\", pos: \"PRON\" },\n};\n\ninterface Token {\n surface: string;\n lemma: string;\n pos: POS;\n}\n\ninterface ParsedEvent {\n id: string; // e.g. \"e1\"\n verb: string; // lemma\n agent?: string; // lemma\n patient?: string; // lemma\n}\n\n// Very small tokenizer + tagger using the lexicon.\nfunction tokenize(sentence: string): Token[] {\n return sentence\n .toLowerCase()\n .replace(/[^a-z\\s]/g, \"\")\n .split(/\\s+/)\n .filter(Boolean)\n .map((surface) => {\n const entry = LEXICON[surface];\n if (!entry) return { surface, lemma: surface, pos: \"OTHER\" as POS };\n return { surface, lemma: entry.lemma, pos: entry.pos };\n });\n}\n\n// Extremely minimal SVO parser: DET? N / PRON → V → DET? N\nfunction parseSentenceToEvent(\n sentence: string,\n id: string,\n): ParsedEvent | null {\n const tokens = tokenize(sentence);\n const verbIndex = tokens.findIndex((t) => t.pos === \"V\");\n if (verbIndex === -1) return null;\n\n const verb = tokens[verbIndex].lemma;\n\n const before = tokens.slice(0, verbIndex);\n const after = tokens.slice(verbIndex + 1);\n\n const agent = before.find((t) => t.pos === \"N\" || t.pos === \"PRON\")?.lemma;\n const patient = after.find((t) => t.pos === \"N\" || t.pos === \"PRON\")?.lemma;\n\n return { id, verb, agent, patient };\n}\n\n// Map a ParsedEvent → CAL constraints\nfunction eventToConstraints(ev: ParsedEvent, source = \"lang\"): Constraint[] {\n const cs: Constraint[] = [];\n const now = Date.now();\n\n // Event verb\n cs.push({\n id: `${source}:${ev.id}:verb:${ev.verb}:${now}`,\n variable: `event:${ev.id}:verb`,\n allowed: new Set([ev.verb]),\n source,\n note: \"event verb\",\n });\n\n if (ev.agent) {\n cs.push({\n id: `${source}:${ev.id}:agent:${ev.agent}:${now}`,\n variable: `event:${ev.id}:agent`,\n allowed: new Set([ev.agent]),\n source,\n note: \"event agent\",\n });\n\n // Agent role knowledge\n cs.push({\n id: `${source}:role:agent:${ev.agent}:${now}`,\n variable: `lemma:${ev.agent}:role`,\n allowed: new Set([\"agent\"]),\n source,\n note: \"lemma can be an agent\",\n });\n }\n\n if (ev.patient) {\n cs.push({\n id: `${source}:${ev.id}:patient:${ev.patient}:${now}`,\n variable: `event:${ev.id}:patient`,\n allowed: new Set([ev.patient]),\n source,\n note: \"event patient\",\n });\n\n cs.push({\n id: `${source}:role:patient:${ev.patient}:${now}`,\n variable: `lemma:${ev.patient}:role`,\n allowed: new Set([\"patient\"]),\n source,\n note: \"lemma can be a patient\",\n });\n }\n\n // Verb frame knowledge: verb takes agent + patient\n cs.push({\n id: `${source}:frame:${ev.verb}:${now}`,\n variable: `verb:${ev.verb}:frame`,\n allowed: new Set([\"agent-patient\"]),\n source,\n note: \"verb frame\",\n });\n\n return cs;\n}\n\n// High-level: learn a sentence into CAL\nfunction learnSentence(cal: CALRuntime, sentence: string, id: string) {\n const ev = parseSentenceToEvent(sentence, id);\n if (!ev) return;\n const cs = eventToConstraints(ev, \"lang\");\n cal.addConstraints(cs);\n}\n\n// ---------------- VAL HANDLER: \"TRAIN\" ON A CORPUS ----------------\n\nexport default function handler() {\n const cal = new CALRuntime();\n const logger = new HtmlLogger();\n\n // In a real system, this corpus would be streamed from a Hugging Face dataset.\n const corpus = [\n \"The dog chased the ball.\",\n \"The cat chased the ball.\",\n \"The man kicked the ball.\",\n \"The woman saw the dog.\",\n \"He chased the cat.\",\n \"They saw the ball.\",\n ];\n\n corpus.forEach((sentence, idx) => {\n learnSentence(cal, sentence, `e${idx + 1}`);\n });\n\n // Inspect what CAL has learned about verbs, roles, and events.\n const domains = cal.getDomains();\n\n logger.log({ corpus });\n logger.log({ domains 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