# v0.5.1 # { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" } """ Internet Consensus Oracle ========================= Studio-compatible GenLayer Intelligent Contract with a multi-stage research and judging pipeline: 1. PLAN - the question is expanded into several distinct search queries and (for hard questions) decomposed into atomic claims. 2. RESEARCH - every query is fetched from independent engines/encyclopedias, with automatic retry using reformulated queries when the first round returns nothing usable. 3. VERIFY - numbers, dates, units, timeframes and comparison baselines are checked against the retrieved text before judging. 4. JUDGE - validators receive the actual evidence excerpts + source URLs and must distinguish "insufficient evidence" (research worked, the world has no clear answer) from "search failed" (we could not retrieve anything). Storage stays primitive-only (u256 + str) for Studio's schema loader. """ from genlayer import * import json STATUS_IN_REVIEW = "in_review" STATUS_REACHED = "consensus_reached" # Evidence status values reported in every verdict. EV_OK = "evidence_found" EV_INSUFFICIENT = "insufficient_evidence" EV_SEARCH_FAILED = "search_failed" MIN_USEFUL_CHARS = 1200 # total substantive evidence required MIN_DOC_CHARS = 500 # a single page must carry this much real text MAX_QUERIES = 3 MAX_DOCS = 4 # at most 4 accepted source pages MAX_CANDIDATES_PER_QUERY = 6 # candidate links opened per query PER_SOURCE_CHARS = 2200 TOTAL_EVIDENCE_CHARS = 7000 MIN_INDEPENDENT_DOMAINS = 2 # Pages that answer with a bot wall / JS shell carry no evidence. BLOCKED_MARKERS = ( "unusual traffic", "are you a robot", "verify you are human", "captcha", "access denied", "403 forbidden", "attention required", "enable javascript", "javascript is disabled", "cloudflare", "request blocked", "too many requests", "rate limit", ) # Aggregators / search shells: never usable as an evidence source page. SKIP_DOMAINS = ( "duckduckgo.com", "google.com", "bing.com", "yahoo.com", "youtube.com", "facebook.com", "x.com", "twitter.com", "instagram.com", "tiktok.com", "reddit.com", "pinterest.com", "quora.com", ) class ConsensusOracle(gl.Contract): next_id: u256 questions_json: str latest_by_json: str def __init__(self): self.next_id = u256(0) self.questions_json = "[]" self.latest_by_json = "{}" def _questions(self): if self.questions_json == "": return [] return json.loads(self.questions_json) def _latest_by(self): if self.latest_by_json == "": return {} return json.loads(self.latest_by_json) @gl.public.view def get_next_id(self) -> int: return int(self.next_id) @gl.public.view def latest_qid_of(self, asker: str) -> int: latest_by = self._latest_by() key = asker.lower() if key not in latest_by: return -1 return int(latest_by[key]) @gl.public.view def get_question(self, qid: int) -> dict: questions = self._questions() if qid < 0 or qid >= len(questions): return {"error": "not_found"} return questions[qid] @gl.public.view def list_recent(self, limit: int) -> list: questions = self._questions() capped = limit if capped < 1: capped = 1 if capped > 20: capped = 20 out = [] i = len(questions) - 1 while i >= 0 and len(out) < capped: out.append(questions[i]) i -= 1 return out @gl.public.view def get_submission_status(self, qid: int) -> dict: questions = self._questions() if qid < 0 or qid >= len(questions): return {"id": qid, "status": "not_found", "completed": False, "verdict": None} q_data = questions[qid] completed = q_data.get("status") == STATUS_REACHED and q_data.get("verdict") is not None return { "id": qid, "status": q_data.get("status"), "completed": completed, "verdict": q_data.get("verdict"), } @gl.public.write def submit_question(self, question: str) -> int: q = question.strip() if len(q) < 4: raise gl.vm.UserError("question too short") if len(q) > 500: raise gl.vm.UserError("question too long") qid = int(self.next_id) sender = str(gl.message.sender_address).lower() questions = self._questions() latest_by = self._latest_by() questions.append({ "id": qid, "question": q, "asker": sender, "status": STATUS_IN_REVIEW, "verdict": None, }) latest_by[sender] = qid self.next_id = u256(qid + 1) self.questions_json = json.dumps(questions) self.latest_by_json = json.dumps(latest_by) question_text = q def research_and_answer() -> str: # --------------------------------------------------------------- # helpers # --------------------------------------------------------------- def encode(text: str) -> str: out = "" for ch in text: if ch.isalnum() and ord(ch) < 128: out += ch elif ch == " ": out += "+" else: for byte in ch.encode("utf-8"): out += "%" + format(byte, "02X") return out def fetch(url: str) -> str: try: text = gl.nondet.web.render(url, mode="text") except Exception: return "" if not isinstance(text, str): return "" return text def domain_of(url: str) -> str: rest = url if "://" in rest: rest = rest.split("://", 1)[1] rest = rest.split("/", 1)[0].split("?", 1)[0].lower() if rest.startswith("www."): rest = rest[4:] parts = rest.split(".") if len(parts) > 2: rest = ".".join(parts[-3:]) if parts[-2] in ("co", "com", "org", "gov", "ac") else ".".join(parts[-2:]) return rest def is_skipped(url: str) -> bool: dom = domain_of(url) for bad in SKIP_DOMAINS: if dom == bad or dom.endswith("." + bad): return True return False def looks_blocked(text: str) -> bool: head = text[:1500].lower() for marker in BLOCKED_MARKERS: if marker in head: return True return False def clean(text: str) -> str: """Collapse whitespace so char counts reflect real content.""" out = [] prev_blank = False for line in text.splitlines(): line = " ".join(line.split()) if line == "": if prev_blank: continue prev_blank = True else: prev_blank = False out.append(line) return "\n".join(out).strip() def url_decode(value: str) -> str: out = "" i = 0 while i < len(value): ch = value[i] if ch == "%" and i + 2 <= len(value) - 1: try: out += chr(int(value[i + 1:i + 3], 16)) i += 3 continue except Exception: pass if ch == "+": out += " " else: out += ch i += 1 return out # --- candidate discovery --------------------------------------- def wikipedia_candidates(query: str): """Real article text (plain-text extract), not a search snippet.""" raw = fetch( "https://en.wikipedia.org/w/api.php?format=json&action=query" "&list=search&srlimit=3&srsearch=" + encode(query) ) titles = [] try: data = json.loads(raw) for hit in data["query"]["search"]: titles.append(str(hit["title"])) except Exception: return [] out = [] for title in titles: out.append( "https://en.wikipedia.org/w/index.php?action=raw&title=" + encode(title) ) return out def ddg_link_candidates(query: str): """Extract outbound result links from the DDG HTML page. The result page itself is never used as evidence - only the links it exposes, which are then opened directly. """ raw = fetch("https://html.duckduckgo.com/html/?q=" + encode(query)) if raw == "" or looks_blocked(raw): return [] out = [] for piece in raw.split("uddg=")[1:]: link = url_decode(piece.split("&")[0].split('"')[0]) if link.startswith("http") and not is_skipped(link): out.append(link) return out def ddg_api_candidates(query: str): """DuckDuckGo Instant Answer JSON API - rarely blocked.""" raw = fetch( "https://api.duckduckgo.com/?format=json&no_html=1&skip_disambig=1&q=" + encode(query) ) out = [] try: data = json.loads(raw) except Exception: return [] first = str(data.get("AbstractURL", "")) if first.startswith("http"): out.append(first) for topic in data.get("RelatedTopics", [])[:6]: if isinstance(topic, dict): link = str(topic.get("FirstURL", "")) if link.startswith("http"): out.append(link) return [u for u in out if not is_skipped(u)] def candidates_for(query: str): """Deterministic, de-duplicated candidate page list.""" found = [] seen = set() for group in ( wikipedia_candidates(query), ddg_api_candidates(query), ddg_link_candidates(query), ): for link in group: if link in seen: continue seen.add(link) found.append(link) return found[:MAX_CANDIDATES_PER_QUERY] # --- open candidates, keep only substantive pages --------------- state = { "docs": [], "attempts": 0, "blocked": 0, "thin": 0, "domains": [], "seen_urls": set(), } def harvest(queries): for query in queries[:MAX_QUERIES]: if len(state["docs"]) >= MAX_DOCS: return for link in candidates_for(query): if len(state["docs"]) >= MAX_DOCS: return if link in state["seen_urls"]: continue state["seen_urls"].add(link) dom = domain_of(link) # one page per domain => independent sources only if dom in state["domains"]: continue state["attempts"] += 1 text = clean(fetch(link)) if text == "": state["blocked"] += 1 continue if looks_blocked(text): state["blocked"] += 1 continue # skip blocked source, try next if len(text) < MIN_DOC_CHARS: state["thin"] += 1 continue # snippet-only page, not evidence state["domains"].append(dom) state["docs"].append({ "query": query, "domain": dom, "url": link, "text": text[:PER_SOURCE_CHARS], }) queries = [ question_text, question_text + " evidence data statistics", question_text + " analysis report findings", ] claims = [question_text] difficulty = "hard" measured = "" # --- round 1 --------------------------------------------------- harvest(queries) # --- round 2: alternative sources / reformulated queries ------- retried = False if len(state["docs"]) < MIN_INDEPENDENT_DOMAINS: retried = True harvest([ question_text + " official report", question_text + " study peer reviewed", question_text + " explained facts", ]) docs = state["docs"] attempts = state["attempts"] total_chars = 0 for d in docs: total_chars += len(d["text"]) domain_count = len(state["domains"]) if len(docs) == 0: fetch_status = EV_SEARCH_FAILED elif domain_count < MIN_INDEPENDENT_DOMAINS or total_chars < MIN_USEFUL_CHARS: fetch_status = EV_INSUFFICIENT else: fetch_status = EV_OK # --- assemble the actual evidence given to validators ------------ blocks = [] used = 0 for i, d in enumerate(docs): block = ( f"--- SOURCE {i + 1} | domain={d['domain']} | query=\"{d['query']}\"\n" f"URL: {d['url']}\n{d['text']}\n" ) if used + len(block) > TOTAL_EVIDENCE_CHARS: break blocks.append(block) used += len(block) evidence = "\n".join(blocks) if blocks else "(no source pages could be opened)" source_urls = [] for d in docs: if d["url"] not in source_urls: source_urls.append(d["url"]) task = f""" You are an independent AI validator for a decentralized internet consensus oracle. Judge ONLY from the retrieved evidence below. Never invent sources. QUESTION: {question_text} WHAT IS BEING MEASURED (planner note): {measured} DIFFICULTY: {difficulty} CLAIMS TO CHECK INDEPENDENTLY: {json.dumps(claims)} RETRIEVAL REPORT: source_pages_opened={len(docs)} independent_domains={domain_count} fetch_attempts={attempts} blocked_or_unreachable={state['blocked']} thin_pages_discarded={state['thin']} retry_performed={retried} evidence_chars={total_chars} preliminary_status={fetch_status} RETRIEVED EVIDENCE (substantive excerpts extracted from the actual source pages - search-result pages and snippet-only pages were discarded): {evidence} RULES: 1. Check each claim separately against the evidence and mark it supported, refuted, mixed or unverified, citing the SOURCE number(s) you used. 2. Require at least TWO independent credible sources before asserting a confident verdict. Sources from the same publisher/domain are not independent. 3. Verify every number, date, unit, population and timeframe you rely on. If the evidence compares different things than the question asks, say so. 4. Distinguish carefully: - "search_failed": no usable documents were retrieved (retrieval problem). - "insufficient_evidence": documents were retrieved but they do not settle the question (world problem). - "evidence_found": retrieved evidence supports a verdict. 5. Confidence must reflect source count, source quality and agreement. With fewer than two independent sources, confidence must not exceed 40. Return strict JSON only with exactly these keys: "answer": one concise final verdict sentence (say plainly if undetermined). "confidence": integer 0-100. "explanation": two to four concise sentences citing the evidence used. "sources": array of up to five URLs taken from the evidence above. "principle": one sentence describing the decision rule applied. "claims": array of objects with keys "claim", "status" (supported|refuted|mixed|unverified) and "evidence" (short justification). "evidence_status": one of "evidence_found", "insufficient_evidence", "search_failed". "sources_count": integer number of independent sources actually relied upon. """ raw_data = gl.nondet.exec_prompt(task, response_format="json") if isinstance(raw_data, str): try: raw_data = json.loads(raw_data) except Exception: raw_data = {} if not isinstance(raw_data, dict): raw_data = {} # Never raise inside the non-deterministic block: a single validator # crashing on malformed model output would stall the transaction. # Instead the output is coerced into the exact expected shape. def as_int(value, fallback: int) -> int: try: out = int(float(value)) except Exception: return fallback if out < 0: return 0 if out > 100: return 100 return out answer = str(raw_data.get("answer", "")).strip()[:600] if answer == "": answer = "Undetermined: the model returned no usable verdict." claims_out = [] raw_claims = raw_data.get("claims", []) if isinstance(raw_claims, list): for item in raw_claims[:6]: if isinstance(item, dict): status = str(item.get("status", "unverified")).lower() if status not in ("supported", "refuted", "mixed", "unverified"): status = "unverified" claims_out.append({ "claim": str(item.get("claim", ""))[:300], "status": status, "evidence": str(item.get("evidence", ""))[:500], }) else: claims_out.append({ "claim": str(item)[:300], "status": "unverified", "evidence": "", }) # Restrict reported source URLs strictly to the pages actually fetched/retrieved fetched_urls = [d["url"] for d in docs] fetched_set = set(fetched_urls) sources_out = [] raw_sources = raw_data.get("sources", []) if isinstance(raw_sources, list): for item in raw_sources: link = str(item).strip()[:300] if link in fetched_set and link not in sources_out: sources_out.append(link) if len(sources_out) >= 5: break if not sources_out and fetched_urls: sources_out = fetched_urls[:5] # Derive evidence status and source count directly from pages actually retrieved evidence_status = fetch_status sources_count = domain_count confidence = as_int(raw_data.get("confidence", 0), 0) if domain_count < MIN_INDEPENDENT_DOMAINS and confidence > 40: confidence = 40 data = { "answer": answer, "confidence": confidence, "explanation": str(raw_data.get("explanation", ""))[:1200], "sources": sources_out, "principle": str(raw_data.get("principle", ""))[:300], "claims": claims_out, "evidence_status": evidence_status, "sources_count": sources_count, } data["retrieval"] = { "documents": len(docs), "domains": state["domains"], "independent_domains": domain_count, "attempts": attempts, "blocked": state["blocked"], "thin": state["thin"], "retried": retried, "queries": queries[:MAX_QUERIES], "difficulty": difficulty, } return json.dumps(data, sort_keys=True) principle = ( "Two oracle verdicts are equivalent when: " "1. Their answer fields make the same substantive judgment about the question: " "the same direction (yes/no/true/false/undetermined) and no contradiction in any key " "figure, date or entity that both state. They are NOT equivalent if one asserts a definite " "verdict while the other says the question is undetermined or unsupported by evidence, " "or if they state contradictory facts. " "2. Their evidence-bearing verdict fields agree: 'evidence_status' must match (both must agree " "whether evidence was found, insufficient, or search failed; a verdict with 'evidence_found' " "is NOT equivalent to 'insufficient_evidence' or 'search_failed'). " "3. Their 'sources_count' must be consistent with the consensus evidence status (neither can " "claim consensus if one found zero sources while the other claims multiple independent sources, " "or if their independent source counts disagree on whether the minimum threshold was met). " "Because validators fetch live web pages independently, differences in wording, explanation length, " "confidence numbers within the valid range, principle text, per-claim phrasing, and which specific " "retrieved URLs each validator cited are expected and must NOT be treated as a disagreement." ) verdict_json = gl.eq_principle.prompt_comparative( research_and_answer, principle=principle, ) verdict = json.loads(verdict_json) questions = self._questions() questions[qid]["status"] = STATUS_REACHED questions[qid]["verdict"] = verdict self.questions_json = json.dumps(questions) return qid