# v0.2.16 # { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" } import datetime import json import typing from dataclasses import dataclass from genlayer import * # ============================================================================ # Constants — statuses, categories, limits # ============================================================================ # Bounty lifecycle statuses. Stored as small ints for cheap storage; # translated to strings at the view boundary (see STATUS_NAMES). STATUS_OPEN_FOR_SUBMISSIONS: int = 0 STATUS_EVALUATING: int = 1 STATUS_SETTLED: int = 2 STATUS_CANCELLED: int = 3 STATUS_TIMED_OUT_RECOVERED: int = 4 STATUS_NAMES: dict[int, str] = { STATUS_OPEN_FOR_SUBMISSIONS: "OPEN_FOR_SUBMISSIONS", STATUS_EVALUATING: "EVALUATING", STATUS_SETTLED: "SETTLED", STATUS_CANCELLED: "CANCELLED", STATUS_TIMED_OUT_RECOVERED: "TIMED_OUT_RECOVERED", } # Sub-state guarding evaluate_bounty against re-entrant / duplicate calls. # A bounty is "EVALUATING" the instant close_submissions() runs, but we also # track whether an evaluation PASS is currently mid-flight so a second call # arriving before the first one finishes (e.g. retried transaction) cannot # double-settle. GenVM executes contract calls to completion one at a time, # but this flag is cheap insurance and documents intent explicitly. EVAL_LOCK_IDLE: int = 0 EVAL_LOCK_RUNNING: int = 1 EVAL_LOCK_DONE: int = 2 # Hard limits — sanity rails, generous enough not to constrain real use. MAX_TITLE_LEN: int = 200 MAX_DESCRIPTION_LEN: int = 3000 MAX_CATEGORY_LEN: int = 40 MAX_CRITERIA_LEN: int = 400 MAX_URL_LEN: int = 500 MAX_SUMMARY_LEN: int = 1200 MAX_SUBMISSIONS_PER_BOUNTY: int = 40 MAX_EVIDENCE_EXCERPT: int = 1500 # chars of rendered page fed to the LLM per submission MAX_REASONING_STORED: int = 6000 # chars of Contribution Graph JSON persisted per bounty MAX_CATEGORY_TAG_LEN: int = 40 BPS_DENOMINATOR: int = 10000 # Comparative-equivalence tolerance band for the Contribution Graph — two # validator results are treated as equivalent if every contributor's bps # allocation is within this many bps of the leader's. Mirrors the # EventWeaver confidence-band tolerance idea applied to reward splits. ALLOCATION_TOLERANCE_BPS: int = 500 # Grace period after a bounty's submission deadline before ANYONE may # trigger the timeout/recovery fallback if the sponsor never evaluates. DEFAULT_TIMEOUT_GRACE_SECONDS: int = 7 * 24 * 60 * 60 # 7 days MIN_TIMEOUT_GRACE_SECONDS: int = 60 * 60 # 1 hour floor (tests / demos) MAX_TIMEOUT_GRACE_SECONDS: int = 90 * 24 * 60 * 60 # 90 days ceiling DEFAULT_CATEGORIES: list[str] = [ "security", "ux", "performance", "recovery", "documentation", ] # Error prefixes — deterministic, machine-parseable failure classes. ERR_EXPECTED = "EXPECTED: " # caller mistake (bad input, wrong state) ERR_EXTERNAL = "EXTERNAL: " # upstream/web failure ERR_TRANSIENT = "TRANSIENT: " # retryable condition ERR_LLM = "LLM_ERROR: " # model output unusable after sanitation # ============================================================================ # Storage dataclasses # ============================================================================ @allow_storage @dataclass class Bounty: """A single bounty: funded task, evaluation criteria, and lifecycle.""" id: u64 sponsor: Address title: str description: str category: str reward_pool: u256 # agreed/advertised reward amount reward_deposited: u256 # actual escrowed GEN balance (separate ledger) status: u8 created_ts: u64 submission_deadline_ts: u64 evaluation_criteria: str # comma-separated categories, e.g. "security,ux,performance,recovery,documentation" min_bond: u256 # anti-spam: reserved for future per-submission bonding settlement_ts: u64 contribution_graph_json: str # persisted reasoning trace / result summary (truncated) eval_lock: u8 # EVAL_LOCK_IDLE / RUNNING / DONE — double-evaluation guard submission_count: u32 @allow_storage @dataclass class Submission: """One contributor's entry to a bounty.""" id: u32 bounty_id: u64 contributor: Address evidence_url: str # real web-fetchable evidence (repo / demo) summary: str # submitter's own pitch (capped, never trusted alone) submitted_ts: u64 extracted_category: str # dominant category the LLM attributed this submission to; "" until evaluated influence_weight_bps: u32 # 0..10000 share of the reward pool; 0 until evaluated reward_owed: u256 # computed payout; 0 until evaluated paid: bool # true once _send_gen has fired for this submission @allow_storage @dataclass class BountyActivity: """Append-only activity log entry for a bounty (lifecycle / audit trail).""" kind: str # "CREATE" | "SUBMIT" | "CLOSE" | "EVALUATE" | "SETTLE" | "CANCEL" | "TIMEOUT" actor: Address amount: u256 ts: u64 note: str # ============================================================================ # Pure helpers (deterministic — safe anywhere) # ============================================================================ def _require(cond: bool, message: str) -> None: """Deterministic guard that raises a user-facing EXPECTED error.""" if not cond: raise gl.vm.UserError(ERR_EXPECTED + message) def _clamp_int(value: int, low: int, high: int) -> int: """Clamp an int into [low, high].""" if value < low: return low if value > high: return high return value def _truncate(text: str, limit: int) -> str: """Trim text to a storage-safe length without exploding mid-codepoint.""" if len(text) <= limit: return text return text[: limit - 1] + "…" def _normalize_url(url: str) -> str: """Light URL validation/normalization. Rejects obviously invalid input.""" u = url.strip() _require(0 < len(u) <= MAX_URL_LEN, f"evidence URL must be 1..{MAX_URL_LEN} chars") _require( u.startswith("https://") or u.startswith("http://"), f"evidence URL must start with http(s):// — got '{u[:40]}'", ) return u def _normalize_criteria(criteria: str) -> list[str]: """Parse a comma-separated evaluation-criteria string into a clean, de-duplicated, lower-cased list of category tags.""" _require(0 < len(criteria.strip()) <= MAX_CRITERIA_LEN, "evaluation_criteria required") raw_parts = [p.strip().lower() for p in criteria.split(",")] parts = [p for p in raw_parts if p] _require(len(parts) > 0, "evaluation_criteria must contain at least one category") seen: list[str] = [] for p in parts: _require(len(p) <= MAX_CATEGORY_TAG_LEN, f"category tag too long: '{p}'") if p not in seen: seen.append(p) return seen def _first_present(payload: dict, keys: list[str]) -> typing.Any: """Return the first present key from a list of aliases, else None.""" for key in keys: if key in payload: return payload[key] return None def _sanitize_json_text(text: str) -> str: """Strip markdown fences and leading chatter around a JSON object/array.""" stripped = text.strip() if stripped.startswith("```"): first_newline = stripped.find("\n") if first_newline != -1: stripped = stripped[first_newline + 1 :] if stripped.rstrip().endswith("```"): stripped = stripped.rstrip()[:-3] # fall back to the outermost object braces if there is chatter around it start = stripped.find("{") end = stripped.rfind("}") if start != -1 and end != -1 and end > start: stripped = stripped[start : end + 1] return stripped.strip() def _coerce_bps(raw: typing.Any) -> int: """Coerce arbitrary LLM allocation output into an int 0..10000 bps. Accepts ints, floats, percentage strings ("30%"), and fractions (0.3).""" try: if isinstance(raw, bool): value = 0.0 elif isinstance(raw, (int, float)): value = float(raw) elif isinstance(raw, str): cleaned = raw.strip().rstrip("%").strip() value = float(cleaned) else: value = 0.0 except (ValueError, TypeError): value = 0.0 if 0.0 < value <= 1.0: value *= 10000.0 elif 1.0 < value <= 100.0: # Ambiguous band: LLM may report either "30" meaning 30% or already # bps-scale. Treat anything <=100 as a percentage — bps values from # a well-behaved prompt are expected to be >100 for any nontrivial # allocation, and the tolerance-band comparative principle absorbs # any resulting rounding drift. value *= 100.0 value = max(0.0, min(float(BPS_DENOMINATOR), value)) return int(round(value)) def _parse_contribution_graph_payload(raw: typing.Any, submission_ids: list[int]) -> dict: """Normalize an LLM Contribution Graph verdict into a canonical dict: { "allocations": {submission_id_str: bps_int, ...}, "categories": {submission_id_str: category_str, ...}, "complementary_notes": str, "reasoning": str, } Tolerant of alias keys and stringly-typed values. Any submission id missing from the model's allocation map defaults to 0 bps rather than raising — an incomplete verdict degrades gracefully instead of aborting settlement. """ payload: typing.Any = raw if isinstance(payload, str): try: payload = json.loads(_sanitize_json_text(payload)) except (json.JSONDecodeError, ValueError): raise gl.vm.UserError(ERR_LLM + "contribution graph verdict was not parseable JSON") if not isinstance(payload, dict): raise gl.vm.UserError(ERR_LLM + "contribution graph verdict JSON was not an object") alloc_raw = _first_present( payload, ["allocations", "allocation", "rewards", "bps_allocations", "distribution"] ) allocations: dict[str, int] = {} if isinstance(alloc_raw, dict): for k, v in alloc_raw.items(): allocations[str(k)] = _coerce_bps(v) elif isinstance(alloc_raw, list): # tolerate [{"submission_id": .., "bps": ..}, ...] shape for item in alloc_raw: if isinstance(item, dict): sid = _first_present(item, ["submission_id", "id", "sid"]) bps = _first_present(item, ["bps", "allocation_bps", "weight", "percent", "pct"]) if sid is not None: allocations[str(sid)] = _coerce_bps(bps) # Ensure every known submission id has an entry (default 0). for sid in submission_ids: allocations.setdefault(str(sid), 0) cat_raw = _first_present(payload, ["categories", "extracted_categories", "category_map", "top_category"]) categories: dict[str, str] = {} if isinstance(cat_raw, dict): for k, v in cat_raw.items(): categories[str(k)] = _truncate(str(v).strip().lower(), MAX_CATEGORY_TAG_LEN) notes_raw = _first_present( payload, ["complementary_notes", "complementary", "synergies", "notes"] ) complementary_notes = str(notes_raw) if notes_raw is not None else "" reasoning_raw = _first_present(payload, ["reasoning", "rationale", "explanation", "analysis"]) reasoning = str(reasoning_raw) if reasoning_raw is not None else "" return { "allocations": allocations, "categories": categories, "complementary_notes": complementary_notes, "reasoning": reasoning, } def _normalize_allocations(allocations: dict[str, int], submission_ids: list[int]) -> dict[str, int]: """Clamp each allocation into [0, 10000], cap the sum at 10000 bps, and deterministically route any rounding remainder/shortfall so totals are exact. Redistribution order is by ascending submission id, so every validator computes an identical result regardless of dict iteration order returned by the LLM.""" ordered_ids = sorted(submission_ids) cleaned: dict[str, int] = {} total = 0 for sid in ordered_ids: key = str(sid) val = _clamp_int(int(allocations.get(key, 0)), 0, BPS_DENOMINATOR) cleaned[key] = val total += val if total > BPS_DENOMINATOR: # Scale down proportionally, then fix residual rounding by trimming # from the largest allocations first (deterministic: ties broken by # ascending submission id, already our iteration order). overflow = total - BPS_DENOMINATOR # Trim from the end (highest ids) first is arbitrary but # deterministic; instead trim proportionally to each share size for # fairness, largest first. order_by_size = sorted(ordered_ids, key=lambda sid: cleaned[str(sid)], reverse=True) idx = 0 while overflow > 0 and order_by_size: key = str(order_by_size[idx % len(order_by_size)]) if cleaned[key] > 0: cleaned[key] -= 1 overflow -= 1 idx += 1 if idx > BPS_DENOMINATOR * 2: break # safety valve, should never trigger total = BPS_DENOMINATOR elif total < BPS_DENOMINATOR and ordered_ids: # Route the unallocated remainder deterministically to the last # (highest-id) submission — matches "rounding-safe last recipient" # per the spec, rather than leaving GEN un-distributed. remainder = BPS_DENOMINATOR - total last_key = str(ordered_ids[-1]) cleaned[last_key] = min(BPS_DENOMINATOR, cleaned[last_key] + remainder) return cleaned # ============================================================================ # Native transfer target — payouts go to wallets (EOAs), not other # Intelligent Contracts. The EOA/EVM path requires this contract-interface # stub; gl.get_contract_at(...).emit_transfer(...) is the IC-to-IC path and # would not settle a real balance for an externally-owned account. # ============================================================================ @gl.evm.contract_interface class _Recipient: class View: pass class Write: pass def _send_gen(to_address: Address, amount: int) -> None: """Single emission choke point for every native-token payout. Zero the ledger field and persist state BEFORE calling this — never after — so a reentrant call always finds the balance already zeroed.""" if amount <= 0: return _Recipient(to_address).emit_transfer(value=u256(int(amount))) # ============================================================================ # The Contract # ============================================================================ class VertexBountyFusion(gl.Contract): """A bounty marketplace where every submission is evaluated together and rewarded proportionally via an LLM-built Contribution Graph, with real native GEN escrow and payout.""" # ---- ownership / config ------------------------------------------------- owner: Address paused: bool min_bond_default: u256 timeout_grace_seconds: u64 accrued_protocol_fees: u256 # ---- bounty storage ------------------------------------------------- bounty_count: u64 bounties: TreeMap[u32, Bounty] bounty_submissions: TreeMap[u32, DynArray[Submission]] # per-sponsor list of bounty ids they created sponsor_bounties: TreeMap[Address, DynArray[u64]] # per-contributor list of (bounty_id) they submitted to — for quick lookup contributor_bounties: TreeMap[Address, DynArray[u64]] # per-bounty activity log activity: TreeMap[u32, DynArray[BountyActivity]] # internal withdrawable native balances (rounding remainders, accrued # protocol fees, or any credit path); withdraw() turns credits into real # transfers. Direct bounty settlement pays contributors immediately via # _send_gen rather than crediting this ledger, per the locked "pay in # the same call" decision — this ledger exists for the residual cases. balances: TreeMap[Address, u256] # ---- platform metrics ----------------------------------------------- total_volume: u256 total_bounties_settled: u64 total_payouts: u256 # ------------------------------------------------------------------------ # Construction # ------------------------------------------------------------------------ def __init__( self, min_bond_default: int = 0, timeout_grace_seconds: int = DEFAULT_TIMEOUT_GRACE_SECONDS, ): """Deploy the platform. Args: min_bond_default: default anti-spam bond (native units) applied to bounties that don't override it. 0 disables. timeout_grace_seconds: seconds past a bounty's submission deadline before anyone may trigger claim_sponsor_timeout(). """ self.owner = gl.message.sender_address self.paused = False self.min_bond_default = u256(max(0, min_bond_default)) self.timeout_grace_seconds = u64( _clamp_int(int(timeout_grace_seconds), MIN_TIMEOUT_GRACE_SECONDS, MAX_TIMEOUT_GRACE_SECONDS) ) self.accrued_protocol_fees = u256(0) self.bounty_count = u64(0) self.total_volume = u256(0) self.total_bounties_settled = u64(0) self.total_payouts = u256(0) # ------------------------------------------------------------------------ # Internal utilities # ------------------------------------------------------------------------ def _not_paused(self) -> None: if self.paused: raise gl.vm.UserError(ERR_EXPECTED + "platform is paused") def _only_owner(self) -> None: if gl.message.sender_address != self.owner: raise gl.vm.UserError(ERR_EXPECTED + "only the owner may call this") def _now_ts(self) -> int: """Authenticated, consensus-agreed clock. GenVM patches datetime.now() to the network's block time, which every validator computes identically — it is never read from caller-supplied arguments or calldata, so it cannot be spoofed by a transaction sender to fabricate a future or past time.""" return int(datetime.datetime.now(datetime.timezone.utc).timestamp()) def _get_bounty(self, bounty_id: int) -> Bounty: bid = u32(bounty_id) bounty = self.bounties.get(bid) if bounty is None: raise gl.vm.UserError(ERR_EXPECTED + f"bounty {bounty_id} does not exist") return bounty def _get_submissions(self, bounty_id: int) -> list: arr = self.bounty_submissions.get(u32(bounty_id)) return list(arr) if arr is not None else [] def _only_sponsor_or_owner(self, bounty: Bounty) -> None: sender = gl.message.sender_address _require( sender == bounty.sponsor or sender == self.owner, "only the bounty sponsor or the platform owner may call this", ) def _credit_balance(self, addr: Address, amount: int) -> None: """Credit an internal withdrawable balance (value stays in-contract until withdraw() emits the real native transfer).""" if amount <= 0: return current = self.balances.get(addr) base = int(current) if current is not None else 0 self.balances[addr] = u256(base + int(amount)) def _log(self, bounty_id: int, kind: str, actor: Address, amount: int, ts: int, note: str) -> None: """Append to the bounty's activity log.""" bid = u32(bounty_id) if self.activity.get(bid) is None: self.activity[bid] = [] self.activity[bid].append( BountyActivity( kind=kind, actor=actor, amount=u256(max(0, amount)), ts=u64(max(0, ts)), note=_truncate(note, 200), ) ) def _record_sponsor_bounty(self, addr: Address, bounty_id: int) -> None: if self.sponsor_bounties.get(addr) is None: self.sponsor_bounties[addr] = [] self.sponsor_bounties[addr].append(u64(bounty_id)) def _record_contributor_bounty(self, addr: Address, bounty_id: int) -> None: if self.contributor_bounties.get(addr) is None: self.contributor_bounties[addr] = [] arr = self.contributor_bounties[addr] bid = u64(bounty_id) for existing in arr: if existing == bid: return arr.append(bid) def _send_native(self, recipient: Address, amount: int) -> None: """Emit an actual native-token transfer from this contract to `recipient` (a wallet/EOA). Delegates to the module-level `_send_gen` choke point — callers must zero and persist the ledger field(s) BEFORE invoking this.""" _send_gen(recipient, amount) # ------------------------------------------------------------------------ # Serialization for views (schema-safe primitives only) # ------------------------------------------------------------------------ def _submission_dict(self, sub: Submission) -> dict: return { "id": int(sub.id), "bounty_id": int(sub.bounty_id), "contributor": sub.contributor.as_hex, "evidence_url": sub.evidence_url, "summary": sub.summary, "submitted_ts": int(sub.submitted_ts), "extracted_category": sub.extracted_category, "influence_weight_bps": int(sub.influence_weight_bps), "reward_owed": int(sub.reward_owed), "paid": bool(sub.paid), } def _bounty_dict(self, bounty: Bounty) -> dict: return { "id": int(bounty.id), "sponsor": bounty.sponsor.as_hex, "title": bounty.title, "description": bounty.description, "category": bounty.category, "reward_pool": int(bounty.reward_pool), "reward_deposited": int(bounty.reward_deposited), "status": STATUS_NAMES.get(int(bounty.status), "OPEN_FOR_SUBMISSIONS"), "created_ts": int(bounty.created_ts), "submission_deadline_ts": int(bounty.submission_deadline_ts), "evaluation_criteria": bounty.evaluation_criteria, "min_bond": int(bounty.min_bond), "settlement_ts": int(bounty.settlement_ts), "submission_count": int(bounty.submission_count), } # ======================================================================== # PUBLIC WRITES — bounty lifecycle # ======================================================================== @gl.public.write.payable def create_bounty( self, title: str, description: str, category: str, evaluation_criteria: str, submission_deadline_ts: int, min_bond: int = 0, ) -> int: """Create and fund a bounty. Attach native GEN via message value — this becomes both `reward_pool` (the agreed amount) and `reward_deposited` (the actual escrow ledger). Args: title / description / category: bounty metadata. evaluation_criteria: comma-separated categories the sponsor cares about, e.g. "security,ux,performance,recovery,documentation". Not hardcoded — sponsors may define their own category set. submission_deadline_ts: unix time after which new submissions are rejected (sponsor may still close early). min_bond: reserved anti-spam knob for future per-submission bonding; defaults to the platform default when 0. Returns: the new bounty id. """ self._not_paused() sender = gl.message.sender_address deposited = int(gl.message.value) now_ts = self._now_ts() _require(deposited > 0, "must fund the bounty with a positive GEN amount") _require(0 < len(title.strip()) <= MAX_TITLE_LEN, f"title must be 1..{MAX_TITLE_LEN} chars") _require(len(description) <= MAX_DESCRIPTION_LEN, f"description exceeds {MAX_DESCRIPTION_LEN} chars") _require(0 < len(category.strip()) <= MAX_CATEGORY_LEN, "category required") _require(submission_deadline_ts > now_ts, "submission_deadline_ts must be in the future") criteria_list = _normalize_criteria(evaluation_criteria) bond = int(min_bond) if int(min_bond) > 0 else int(self.min_bond_default) _require(bond >= 0, "min_bond must be non-negative") bounty_id = int(self.bounty_count) self.bounty_count = u64(bounty_id + 1) bid = u32(bounty_id) self.bounties[bid] = Bounty( id=u64(bounty_id), sponsor=sender, title=title.strip(), description=description.strip(), category=category.strip(), reward_pool=u256(deposited), reward_deposited=u256(deposited), status=u8(STATUS_OPEN_FOR_SUBMISSIONS), created_ts=u64(now_ts), submission_deadline_ts=u64(submission_deadline_ts), evaluation_criteria=",".join(criteria_list), min_bond=u256(bond), settlement_ts=u64(0), contribution_graph_json="", eval_lock=u8(EVAL_LOCK_IDLE), submission_count=u32(0), ) self.bounty_submissions[bid] = [] self._record_sponsor_bounty(sender, bounty_id) self.total_volume = u256(int(self.total_volume) + deposited) self._log(bounty_id, "CREATE", sender, deposited, now_ts, title.strip()[:100]) return bounty_id @gl.public.write def submit_solution(self, bounty_id: int, evidence_url: str, summary: str) -> int: """Submit a solution to an open bounty. No payment required from the contributor. `evidence_url` must be a real http(s) link (GitHub repo, deployed demo, etc.) — the contract fetches this at evaluation time rather than trusting `summary` alone. Returns: the new submission id (scoped to this bounty). """ self._not_paused() now_ts = self._now_ts() bounty = self._get_bounty(bounty_id) _require( int(bounty.status) == STATUS_OPEN_FOR_SUBMISSIONS, "bounty is not open for submissions", ) _require(now_ts <= int(bounty.submission_deadline_ts), "submission deadline has passed") url = _normalize_url(evidence_url) clean_summary = _truncate(summary.strip(), MAX_SUMMARY_LEN) _require(len(clean_summary) > 0, "summary must not be empty") existing = self._get_submissions(bounty_id) _require(len(existing) < MAX_SUBMISSIONS_PER_BOUNTY, "bounty has reached its submission cap") sender = gl.message.sender_address sub_id = len(existing) bid = u32(bounty_id) self.bounty_submissions[bid].append( Submission( id=u32(sub_id), bounty_id=u64(bounty_id), contributor=sender, evidence_url=url, summary=clean_summary, submitted_ts=u64(now_ts), extracted_category="", influence_weight_bps=u32(0), reward_owed=u256(0), paid=False, ) ) bounty.submission_count = u32(int(bounty.submission_count) + 1) self._record_contributor_bounty(sender, bounty_id) self._log(bounty_id, "SUBMIT", sender, 0, now_ts, url[:100]) return sub_id @gl.public.write def close_submissions(self, bounty_id: int) -> None: """Move a bounty from OPEN_FOR_SUBMISSIONS to EVALUATING. Callable by the sponsor (or owner) at any time — an explicit human action that triggers the non-deterministic evaluation phase — OR by anyone once the submission deadline has passed.""" self._not_paused() now_ts = self._now_ts() bounty = self._get_bounty(bounty_id) _require( int(bounty.status) == STATUS_OPEN_FOR_SUBMISSIONS, "bounty is not open for submissions", ) deadline_passed = now_ts > int(bounty.submission_deadline_ts) if not deadline_passed: self._only_sponsor_or_owner(bounty) bounty.status = u8(STATUS_EVALUATING) self._log(bounty_id, "CLOSE", gl.message.sender_address, 0, now_ts, "") @gl.public.write def cancel_bounty(self, bounty_id: int) -> None: """Sponsor (or owner) cancels a bounty BEFORE any submissions exist, refunding the full escrow. Zero-then-transfer ordering.""" now_ts = self._now_ts() bounty = self._get_bounty(bounty_id) self._only_sponsor_or_owner(bounty) _require( int(bounty.status) == STATUS_OPEN_FOR_SUBMISSIONS, "only an open bounty can be cancelled", ) _require(int(bounty.submission_count) == 0, "cannot cancel once submissions exist") refund = int(bounty.reward_deposited) bounty.reward_deposited = u256(0) bounty.status = u8(STATUS_CANCELLED) bounty.settlement_ts = u64(now_ts) bounty.contribution_graph_json = "Cancelled before any submissions were received; full refund issued." self._log(bounty_id, "CANCEL", gl.message.sender_address, refund, now_ts, "") # zero + persist happened above; transfer happens last self._send_native(bounty.sponsor, refund) # ======================================================================== # PUBLIC WRITES — non-deterministic evaluation core # ======================================================================== def _fetch_submission_evidence(self, submissions: list) -> list[dict]: """Fetch each submission's evidence URL defensively inside the nondet block. Runs INSIDE a leader/validator function — never call from deterministic code. A dead or slow source degrades to an error record instead of aborting the whole evaluation.""" evidence: list[dict] = [] for sub in submissions: try: text = gl.nondet.web.render(str(sub.evidence_url), mode="text") excerpt = str(text)[:MAX_EVIDENCE_EXCERPT] evidence.append({"id": int(sub.id), "url": str(sub.evidence_url), "ok": True, "excerpt": excerpt}) except Exception as exc: # noqa: BLE001 — degrade per-source, never abort evidence.append( { "id": int(sub.id), "url": str(sub.evidence_url), "ok": False, "excerpt": f"[fetch failed: {str(exc)[:150]}]", } ) return evidence def _build_contribution_graph_prompt( self, bounty_title: str, bounty_description: str, criteria: list[str], submissions: list, evidence: list[dict], ) -> str: """Compose the joint reasoning prompt that asks the LLM to reason ACROSS ALL submissions together — never one-in-isolation — and produce a Contribution Graph: strongest submission per criterion, complementary relationships, and a percentage allocation across contributors summing to 100%.""" evidence_by_id = {item["id"]: item for item in evidence} blocks = [] for sub in submissions: item = evidence_by_id.get(int(sub.id), {"ok": False, "excerpt": "(no evidence fetched)"}) status = "OK" if item.get("ok") else "FETCH_FAILED" blocks.append( f"--- SUBMISSION #{int(sub.id)} by {sub.contributor.as_hex} " f"(evidence {status}: {sub.evidence_url})\n" f"Submitter summary: {sub.summary}\n" f"Fetched evidence excerpt:\n{item.get('excerpt', '')}" ) submissions_text = "\n\n".join(blocks) if blocks else "(no submissions)" criteria_text = ", ".join(criteria) submission_ids = ", ".join(str(int(s.id)) for s in submissions) return f"""You are a neutral technical judge evaluating ALL submissions to a single \ bounty together, as one merged solution — you are NOT picking one winner. BOUNTY: "{bounty_title}" DESCRIPTION: {bounty_description} EVALUATION CRITERIA (categories the sponsor cares about): {criteria_text} You will be shown every submission's own pitch AND real evidence fetched \ from each submission's public URL. Weigh the fetched evidence more heavily \ than the submitter's own claims — submitters may oversell their work. For EACH criterion, decide which submission(s) contribute the strongest \ work. Then reason about how the submissions COMPLEMENT each other (e.g. one \ has the strongest security, another the best UX) as if they were being \ merged into one final solution. Finally compute a reward allocation across \ ALL submission ids ({submission_ids}) expressed in basis points (bps, out \ of 10000 total) reflecting each contributor's influence on the merged \ solution. The allocations must sum to 10000 (or less, if you intentionally \ withhold some as unallocated — but prefer summing to 10000). SUBMISSIONS: {submissions_text} Respond with ONLY a JSON object, no markdown, with exactly these keys: {{ "allocations": {{"": , ...}} for \ EVERY submission id listed above, "categories": {{"": "", ...}}, "complementary_notes": "one short paragraph on how the submissions \ complement each other", "reasoning": "one short paragraph explaining the overall allocation \ rationale" }} Rules: - Base "strongest category" judgments on the fetched evidence, not just the \ submitter's summary. - If a submission's evidence fetch failed, allocate it a low but non-zero \ weight only if the summary is independently credible; otherwise allocate 0. - Keep reasoning and complementary_notes each under 150 words. - Allocations must be non-negative integers summing to at most 10000.""" def _evaluate_contribution_graph_nondet( self, bounty: Bounty, submissions: list, criteria: list[str], ) -> dict: """Run the full web-fetch + LLM joint-reasoning pass under a comparative equivalence principle. The principle is deliberately outcome-focused and tolerant: the leader's result stands as long as validators agree, PER CONTRIBUTOR, on the bps allocation within ALLOCATION_TOLERANCE_BPS, and on the same top category per contributor — regardless of exact wording, JSON key order, or minor percentage rounding. This mirrors the EventWeaver step-verdict comparative pattern and is what prevents leader-rotation / Undetermined status on inherently fuzzy, cross-submission qualitative reasoning. """ submission_ids = [int(s.id) for s in submissions] def leader() -> str: evidence = self._fetch_submission_evidence(submissions) prompt = self._build_contribution_graph_prompt( bounty.title, bounty.description, criteria, submissions, evidence ) raw = gl.nondet.exec_prompt(prompt, response_format="json") parsed = _parse_contribution_graph_payload(raw, submission_ids) normalized_allocations = _normalize_allocations(parsed["allocations"], submission_ids) # Canonical sorted-key JSON — comparative equivalence then # compares MEANING (per-contributor bps within tolerance, same # top category), never exact bytes. return json.dumps( { "allocations": {k: normalized_allocations[k] for k in sorted(normalized_allocations)}, "categories": {k: parsed["categories"].get(k, "") for k in sorted(parsed["categories"])}, "complementary_notes": _truncate(parsed["complementary_notes"], 1000), "reasoning": _truncate(parsed["reasoning"], 1500), }, sort_keys=True, ) principle = ( "Both results are JSON Contribution Graph verdicts distributing a " "reward pool across the SAME set of submission ids for one bounty. " "Treat them as equivalent if, for EVERY submission id, the " f"'allocations' bps value differs by no more than {ALLOCATION_TOLERANCE_BPS} " "basis points, AND the 'categories' entry for that submission id names " "the same or a clearly synonymous strongest category (e.g. 'ux' and " "'user experience' are the same). Differences in 'reasoning' or " "'complementary_notes' wording, formatting, key order, or minor " "percentage rounding within the tolerance band are NOT grounds for " "disagreement — validators must not require exact-byte or exact-wording " "matches, only agreement on the substantive allocation and category " "conclusions." ) raw_result = gl.eq_principle.prompt_comparative(leader, principle) parsed = _parse_contribution_graph_payload(raw_result, submission_ids) parsed["allocations"] = _normalize_allocations(parsed["allocations"], submission_ids) return parsed def _apply_contribution_graph( self, bounty: Bounty, submissions: list, graph: dict, now_ts: int ) -> None: """Fold a Contribution Graph verdict into submission state and pay out. Zero-then-transfer ordering: reward_deposited is zeroed and persisted BEFORE any _send_gen call below.""" allocations = graph["allocations"] categories = graph["categories"] pool = int(bounty.reward_deposited) # zero the ledger + persist BEFORE any transfer bounty.reward_deposited = u256(0) payouts: list[tuple[Address, int]] = [] for sub in submissions: key = str(int(sub.id)) bps = int(allocations.get(key, 0)) owed = (pool * bps) // BPS_DENOMINATOR sub.influence_weight_bps = u32(bps) sub.reward_owed = u256(owed) sub.extracted_category = _truncate(categories.get(key, ""), MAX_CATEGORY_TAG_LEN) if owed > 0: payouts.append((sub.contributor, owed)) summary = { "bounty_id": int(bounty.id), "reward_pool_distributed": pool, "allocations_bps": allocations, "categories": categories, "complementary_notes": graph.get("complementary_notes", ""), "reasoning": graph.get("reasoning", ""), } bounty.contribution_graph_json = _truncate(json.dumps(summary, sort_keys=True), MAX_REASONING_STORED) bounty.settlement_ts = u64(now_ts) # transfers happen last, after every ledger field above is zeroed # and the mutated dataclasses are the live storage objects for recipient, amount in payouts: self._send_native(recipient, amount) for sub in submissions: if int(sub.reward_owed) > 0: sub.paid = True self.total_payouts = u256(int(self.total_payouts) + pool) @gl.public.write def evaluate_bounty(self, bounty_id: int) -> dict: """The core non-deterministic function: fetch real evidence for every submission, reason across ALL of them jointly to build the Contribution Graph, compute proportional GEN payouts, and settle. Guards against double-evaluation/double-payout by re-deriving status from storage on every call — a bounty already EVALUATING (a prior pass never got here) can be evaluated once; SETTLED, CANCELLED, or TIMED_OUT_RECOVERED bounties reject outright. Returns the persisted Contribution Graph summary dict. """ self._not_paused() now_ts = self._now_ts() bounty = self._get_bounty(bounty_id) status = int(bounty.status) _require(status == STATUS_EVALUATING, "bounty must be EVALUATING (call close_submissions first)") _require(int(bounty.eval_lock) != EVAL_LOCK_DONE, "bounty has already been evaluated") _require(int(bounty.eval_lock) != EVAL_LOCK_RUNNING, "an evaluation pass is already in progress") submissions = self._get_submissions(bounty_id) _require(len(submissions) > 0, "bounty has no submissions to evaluate") bounty.eval_lock = u8(EVAL_LOCK_RUNNING) criteria = [c for c in bounty.evaluation_criteria.split(",") if c] graph = self._evaluate_contribution_graph_nondet(bounty, submissions, criteria) self._apply_contribution_graph(bounty, submissions, graph, now_ts) bounty.status = u8(STATUS_SETTLED) bounty.eval_lock = u8(EVAL_LOCK_DONE) self.total_bounties_settled = u64(int(self.total_bounties_settled) + 1) self._log(bounty_id, "SETTLE", gl.message.sender_address, 0, now_ts, "evaluated + paid") return { "bounty_id": bounty_id, "status": STATUS_NAMES[int(bounty.status)], "contribution_graph_json": bounty.contribution_graph_json, } @gl.public.write def claim_sponsor_timeout(self, bounty_id: int) -> dict: """Recovery/exit path: if a bounty's submissions closed (or its deadline passed) but the sponsor never triggered evaluate_bounty within `timeout_grace_seconds` of the deadline, ANY address may call this. Zero submissions -> full refund to sponsor. One or more submissions -> automatic even-split evaluation across all submissions (equal influence_weight_bps), paid identically to the success path. Zero-then-transfer ordering throughout. """ self._not_paused() now_ts = self._now_ts() bounty = self._get_bounty(bounty_id) status = int(bounty.status) _require( status in (STATUS_OPEN_FOR_SUBMISSIONS, STATUS_EVALUATING), "bounty is not in a timeout-eligible state", ) grace_deadline = int(bounty.submission_deadline_ts) + int(self.timeout_grace_seconds) _require(now_ts > grace_deadline, "timeout grace period has not elapsed yet") _require(int(bounty.eval_lock) != EVAL_LOCK_DONE, "bounty has already been evaluated") submissions = self._get_submissions(bounty_id) if len(submissions) == 0: refund = int(bounty.reward_deposited) bounty.reward_deposited = u256(0) bounty.status = u8(STATUS_TIMED_OUT_RECOVERED) bounty.settlement_ts = u64(now_ts) bounty.contribution_graph_json = "Sponsor timeout with zero submissions; full refund issued." bounty.eval_lock = u8(EVAL_LOCK_DONE) self._log(bounty_id, "TIMEOUT", gl.message.sender_address, refund, now_ts, "refund, no submissions") self._send_native(bounty.sponsor, refund) return { "bounty_id": bounty_id, "status": STATUS_NAMES[int(bounty.status)], "refunded": refund, } # Automatic even-split fallback — deterministic, no LLM call needed. even_bps = BPS_DENOMINATOR // len(submissions) allocations = {str(int(s.id)): even_bps for s in submissions} allocations = _normalize_allocations(allocations, [int(s.id) for s in submissions]) graph = { "allocations": allocations, "categories": {str(int(s.id)): "unevaluated_timeout_split" for s in submissions}, "complementary_notes": "", "reasoning": ( "Sponsor did not trigger evaluation within the timeout grace " "period; reward was split evenly across all submissions as a " "deterministic fallback." ), } bounty.eval_lock = u8(EVAL_LOCK_RUNNING) self._apply_contribution_graph(bounty, submissions, graph, now_ts) bounty.status = u8(STATUS_TIMED_OUT_RECOVERED) bounty.eval_lock = u8(EVAL_LOCK_DONE) self._log(bounty_id, "TIMEOUT", gl.message.sender_address, 0, now_ts, "even-split fallback") return { "bounty_id": bounty_id, "status": STATUS_NAMES[int(bounty.status)], "contribution_graph_json": bounty.contribution_graph_json, } # ======================================================================== # PUBLIC WRITES — internal balance / administration # ======================================================================== @gl.public.write def withdraw(self, amount: int) -> None: """Withdraw internal balance as a REAL native-token transfer from the contract to the caller. Used for rounding remainders / accrued protocol fees credited via _credit_balance.""" sender = gl.message.sender_address try: amount = int(amount) # tolerate stringly-typed calldata amounts except (ValueError, TypeError): raise gl.vm.UserError(ERR_EXPECTED + "amount must be an integer") current = self.balances.get(sender) available = int(current) if current is not None else 0 _require(amount > 0, "withdraw amount must be positive") _require(amount <= available, f"insufficient balance: have {available}") self.balances[sender] = u256(available - amount) self.total_payouts = u256(int(self.total_payouts) + amount) self._send_native(sender, amount) @gl.public.write def pause(self) -> None: """Owner: halt bounty creation, submission, and evaluation.""" self._only_owner() self.paused = True @gl.public.write def unpause(self) -> None: """Owner: resume operations.""" self._only_owner() self.paused = False @gl.public.write def set_min_bond_default(self, min_bond_default: int) -> None: """Owner: update the platform-wide default anti-spam bond.""" self._only_owner() _require(int(min_bond_default) >= 0, "min_bond_default must be non-negative") self.min_bond_default = u256(int(min_bond_default)) @gl.public.write def set_timeout_grace_seconds(self, timeout_grace_seconds: int) -> None: """Owner: update the grace period after which claim_sponsor_timeout becomes callable by anyone.""" self._only_owner() clamped = _clamp_int(int(timeout_grace_seconds), MIN_TIMEOUT_GRACE_SECONDS, MAX_TIMEOUT_GRACE_SECONDS) self.timeout_grace_seconds = u64(clamped) @gl.public.write def set_owner(self, new_owner: str) -> None: """Owner: transfer ownership to a new address (hex string).""" self._only_owner() self.owner = Address(new_owner) @gl.public.write def sweep_protocol_fees(self) -> int: """Owner: move accrued protocol fees into the owner's withdrawable balance. Returns the swept amount. (No protocol fee is currently assessed on settlements — this exists for a future fee schedule and is kept at 0 unless credited elsewhere.)""" self._only_owner() amount = int(self.accrued_protocol_fees) _require(amount > 0, "no fees accrued") self.accrued_protocol_fees = u256(0) self._credit_balance(self.owner, amount) return amount # ======================================================================== # PUBLIC VIEWS # ======================================================================== @gl.public.view def get_bounty(self, bounty_id: int) -> dict: """Full bounty summary (without submissions).""" return self._bounty_dict(self._get_bounty(bounty_id)) @gl.public.view def get_bounties( self, offset: int = 0, limit: int = 20, status: str = "", category: str = "", ) -> list[dict]: """Paginated bounty summaries, newest first, optionally filtered by status name (e.g. "OPEN_FOR_SUBMISSIONS") and/or category (case-insensitive exact match).""" count = int(self.bounty_count) capped_limit = _clamp_int(int(limit), 1, 50) wanted_status = status.strip().upper() wanted_category = category.strip().lower() result: list[dict] = [] idx = count - 1 - max(0, int(offset)) scanned = 0 while idx >= 0 and len(result) < capped_limit and scanned < count: bounty = self.bounties.get(u32(idx)) scanned += 1 idx -= 1 if bounty is None: continue if wanted_status and STATUS_NAMES.get(int(bounty.status)) != wanted_status: continue if wanted_category and bounty.category.strip().lower() != wanted_category: continue result.append(self._bounty_dict(bounty)) return result @gl.public.view def get_bounty_count(self) -> int: return int(self.bounty_count) @gl.public.view def get_submissions(self, bounty_id: int) -> list[dict]: """All submissions to a bounty, in submission order.""" self._get_bounty(bounty_id) return [self._submission_dict(s) for s in self._get_submissions(bounty_id)] @gl.public.view def get_submission(self, bounty_id: int, submission_id: int) -> dict: self._get_bounty(bounty_id) subs = self._get_submissions(bounty_id) _require(0 <= submission_id < len(subs), "submission index out of range") return self._submission_dict(subs[submission_id]) @gl.public.view def get_contribution_graph(self, bounty_id: int) -> dict: """Full reasoning trace + allocations for a bounty's evaluation, if it has been evaluated (empty string until then).""" bounty = self._get_bounty(bounty_id) return { "bounty_id": int(bounty.id), "status": STATUS_NAMES.get(int(bounty.status), "OPEN_FOR_SUBMISSIONS"), "settlement_ts": int(bounty.settlement_ts), "contribution_graph_json": bounty.contribution_graph_json, "submissions": [self._submission_dict(s) for s in self._get_submissions(bounty_id)], } @gl.public.view def get_sponsor_bounty_ids(self, address: str) -> list[int]: arr = self.sponsor_bounties.get(Address(address)) if arr is None: return [] return [int(x) for x in arr] @gl.public.view def get_contributor_bounty_ids(self, address: str) -> list[int]: arr = self.contributor_bounties.get(Address(address)) if arr is None: return [] return [int(x) for x in arr] @gl.public.view def get_balance_of(self, address: str) -> int: """Internal withdrawable native balance of an address.""" current = self.balances.get(Address(address)) return int(current) if current is not None else 0 @gl.public.view def get_activity(self, bounty_id: int, offset: int = 0, limit: int = 25) -> list[dict]: """Recent activity log entries for a bounty, newest first.""" self._get_bounty(bounty_id) log = self.activity.get(u32(bounty_id)) if log is None: return [] total = len(log) capped = _clamp_int(int(limit), 1, 100) start = total - 1 - max(0, int(offset)) result: list[dict] = [] idx = start while idx >= 0 and len(result) < capped: evt = log[idx] result.append( { "kind": evt.kind, "actor": evt.actor.as_hex, "amount": int(evt.amount), "ts": int(evt.ts), "note": evt.note, } ) idx -= 1 return result @gl.public.view def get_platform_stats(self) -> dict: return { "bounty_count": int(self.bounty_count), "total_volume": int(self.total_volume), "total_bounties_settled": int(self.total_bounties_settled), "total_payouts": int(self.total_payouts), "accrued_protocol_fees": int(self.accrued_protocol_fees), "paused": bool(self.paused), } @gl.public.view def get_config(self) -> dict: return { "owner": self.owner.as_hex, "paused": bool(self.paused), "min_bond_default": int(self.min_bond_default), "timeout_grace_seconds": int(self.timeout_grace_seconds), "allocation_tolerance_bps": ALLOCATION_TOLERANCE_BPS, "max_submissions_per_bounty": MAX_SUBMISSIONS_PER_BOUNTY, } @gl.public.view def get_categories(self) -> list[str]: """Example/default evaluation categories. Sponsors are not limited to these — evaluation_criteria is a free-form comma-separated field per bounty.""" return list(DEFAULT_CATEGORIES)