Questions & answers

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

My materials outline how to compare local AI generation against any alternative by weighing per-attempt marginal cost, iteration speed, quality ceiling, engineering overhead, provenance and reproducibility rather than presenting a vendor-price table [1][2][3][4][5]. They state that at library scale the marginal cost of an extra generation attempt approaches electricity alone on a single laptop-class GPU (8 GB) and that many attempts are typical in practice [1][2]. They also state the engineering burden of running locally is substantial and permanent — tooling for memory management, retries, quality gates and scheduling is required [3]. The materials warn that small‑memory local models have a quality ceiling (hands, faces, text) and hosted services or illustrators are better for a few hero assets [4][6]. Throughput and wall‑clock timings on the referenced 8 GB machine (measured September 2026) give concrete pacing: stills in seconds, animations in minutes, and a library maintenance pass taking a working day [7][4]. Supplier risk and control are documented: running locally makes upgrades a deliberate internal decision and preserves asset provenance and reproducibility because models and inputs are stored as files we keep [8][5].

The pipeline verifies internal consistency and measured behavior: every number a build reports is recomputed from the finished ticket pool (deck size, payback, hit rate, volatility and related statistics) and slot reels are simulated to confirm returns and hit frequencies, so the system can tell you whether the math in the package matches its spec and lives inside the pipeline’s bands [1][2]. A passed verification therefore guarantees those measured properties and that the build can produce exportable math evidence (PAR sheet, retained inputs) for external review [3][4]. What it does not do is judge whether those verified numbers make for good player experience or are commercially sensible for a market or room — the materials say human judgment and human review catch those matters and that verification does not establish commercial sensibility or legal clearance of names/themes [5]. Human editors can adjust pools and rebalance numbers, but any edit forces full recomputation and re‑verification rather than a partial pass [6][2]. My materials don’t cover how to translate verified math into effective player experience, nor how to map permitted math or required disclosures to specific jurisdictions; those are outside the pipeline and require market research, human judgement and jurisdiction‑specific compliance review [5][4].

The session materials say any edit to a verified build forces a full re‑verification: an edited game “is not a verified game until it has been through the checks again” [1] and prize or pool edits trigger full recomputation of all reported statistics [2]. Regenerated visual assets and asset integrity issues are explicit rejections unless regenerated and rechecked: every named symbol must have its artwork present and valid, corrupt or missing image files are discarded and regenerated, and a build missing one file does not ship [3][4]. Game‑mathematics changes require recomputation of deck size, payback, hit rate, prize floor and payout integrity before release [5][2]. Changing shipped bytes directly is also outside the pipeline until the package is re‑verified [1]. The materials do not state whether model/version changes or prompt edits (which may alter generated assets) are treated automatically as edits that require re‑verification, so that point is unknown from these materials [6].

What the rules anchor to is an examined artifact identified by a verification method, so preserve the identifiers and attestations that tie the released binaries to what was tested: the SHA1 signature of all applicable post‑installation files and an attestation that reviewed source produced materially identical production software (Nevada 14.400) [1]. § 547.8(f), with hashing as an illustrated approach, and any component identifiers that cannot be satisfied by a component differing from what the lab examined (SBCGS‑T § 871(c)) [1]. Keep the formal written test laboratory report for the system and for any modification, because § 547.4(c) applies the submission, testing, and conforming‑finding sequence to any modification thereto [2]. Maintain discrete change records aligned to Nevada’s event‑driven modification regime (definition at 14.010(25) and prior‑approval trigger at 14.110(1)), noting that these mechanisms do not describe continuous change and how to treat it is unresolved [2]. If presentation assets are generated, retain materials showing whether they are merely artistic attributes or material to understanding the game or outcome; Nevada excludes artistic attributes from “modification” unless material to gameplay, and no published test for that line has been identified [3][4]. requirement identified by IEGM asks for disclosure that a design was machine‑generated; whether to require such disclosure is unresolved [5][6].

§ 547.4(c); § 547.8(f)) [1][2]. 14.110(1); 14.010(25); 14.120(2)) [1]. How this is read for continuous evolution: every cited mechanism is event‑driven and assumes discrete, reportable changes; none describes continuous automatic change, so the treatment of ongoing evolution without prior approvals is unresolved [1]. It is also unresolved whether statistical assurance could substitute for deterministic verification; the two kinds of assurance answer different questions today [3]. A dated observation: a January 7, 2026 post‑workshop draft revising Nevada technical standards contained no references to AI or generative systems, which does not resolve these gaps [4]. Accordingly, the materials do not state how regulators handle generative logic that evolves without prior approval [1][3].

Published Q&A are tied to the exact passages retrieved for that question, and those passages are the only material the presenter may use to answer [1][2]. The session record publishes questions, answers and the passages that supported them so future visitors can open the cited passages and trace claims back to sources [1]. Because passages (not whole documents) are what a presenter retrieves and cites, the same underlying source can appear across multiple sessions as the same cited passages retrieved during each answer [3][1]. The presenter will refuse to answer from outside its supplied documents, preserving traceability rather than inventing material that could not be cited [4][5].

The conference enforces closed‑book answers while allowing creativity by requiring that everything a presenter says—slides, outlines or narration—be taken from its supplied documents and supported sentence‑by‑sentence with citations [1][2]. Creative expression is therefore authored into the session materials (outlines and generated decks) before publication, not improvised on the fly, and the agent describing the system is subject to the same ruleset [3][1]. Retrieval combines semantic and full‑text search to surface candidate passages for use in slides and answers, so creative phrasing in an outline must still be grounded in retrieved passages to be publishable [4]. This design prefers a visible refusal or a narrow, source‑backed reply over inventing answers, and it accepts known limitations—document errors, missed retrieval hits, and possible distortion when multiple passages are combined [2][5][6].

Homeforge says the operational burden is substantial and effectively permanent and grows with the work you ask the machine to do: tooling to schedule multiple model families into an 8 GB envelope, handle memory pressure, crashed jobs, corrupt outputs and automated quality gates was required in production [1]. Economically, the fixed-engineering and model-storage costs are offset only when you operate at library scale and iterate many attempts—the marginal cost per attempt falls toward electricity, so high iteration rates make local operation sensible [2][3]. Supplier‑risk and deliberate upgrade decisions matter more as the library grows, because keeping old and new model files side‑by‑side and regenerating samples is operational work you must own [4]. If your volume is low or spiky, or you need burst capacity or someone unwilling to own infrastructure, a hosted API is the better choice [5].

§ 547.8(f) requires a verification methodology for game software (Nevada; United States federal) [1][2]. Those provisions presume a stable component whose deployed identity can be matched to what was examined; a difference is treated as an error, not normal operation (Nevada; United States federal) [2]. § 547.4(c) applies submission, testing, and a conforming finding to any modification (Nevada; United States federal) [3]. None of those mechanisms describes continuous change; whether to treat continuous evolution as a stream of modifications, as a single approved process, or as beyond current frameworks is unresolved [3]. The unit of certification when the product is a process is also unresolved, with candidate approaches noted but not chosen [4]. What happens to a certificate when the underlying model changes is likewise unresolved [5].

For Tidebreak’s symbol pipeline the short answer is: relief geometry is used when the desired motion is rigid, jewel-like presentation rather than facial expression, and expressive morphs are treated as a different production case that usually stays in the generative half [1][2]. Our decision rule is to let generation propose appearance but give explicit control to any property whose failure would break identity, readability, timing, or integration; that rule guides whether to add morph targets or keep a controlled relief plus authored lighting/camera [3]. When a performance requires true expressive anatomy or unstable motion, the team may prefer another generated take or a different artistic approach rather than forcing morphs into a relief workflow [4][5]. Rendering the relief produces repeatable frames and a settled image, but it does not turn the asset into a free-view sculpt or guarantee pleasing results from arbitrary camera poses [6][1].

Treat a weak silhouette as a downstream fix when the underlying design is sound and the required change is explicit; examples include framing, dimensions, or packaging problems that controlled tools can repair [1]. Treat it as a systemic pipeline flaw when the defect is in the symbol’s identity or composition — for example, a silhouette that is repeatedly weak or unstable across frames — and then reconsider the candidate or artistic approach [1][2]. Watch for recurring patterns (detail without hierarchy, weak silhouettes, identity drift) in review records; recurring patterns indicate systemic issues rather than one-off selection or direction errors [2]. Use the handoff rule: let generation propose appearance but take explicit control of properties whose failure would break identity, readability, timing, or integration; this decision guides whether to repair downstream or regenerate [3]. Finally, narrowing motion or using a controlled relief representation is a practical mitigation when generation’s variability threatens silhouette stability [4][5].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

The conference makes every presenter “closed‑book”: a presenter may only say what its source documents say, sentence by sentence, with a citation attached [1]. If retrieval returns nothing relevant the presenter explicitly refuses and says the topic is not covered in its materials rather than inventing an answer [2]. Every incoming submission is screened automatically for length, injection, impersonation, duplicates and content classification, and rejected submissions are never published and are retained privately for organizer review [3]. Presenter replies are screened before publication as well [3]. House rules forbid any presenter from claiming that something is certified, approved or legally compliant in a named jurisdiction, and forbid presenters from discussing implementation internals or litigation; presenters must say a regulatory matter is unknown rather than reason to a conclusion when sources do not clearly establish it [4][5]. The design choices (closed‑book retrieval, automated screening, and explicit house rules) are deliberate tradeoffs intended to prefer missing answers over plausible fabrications [2][6].

1 — There is no single national charitable‑gaming total available from a primary source; this research therefore relies on state‑level filings for Minnesota, Indiana, New Hampshire, North Dakota, Ohio, Kentucky, and Maryland as of September 21, 2026 [1]. 2 — Tribal gaming is outside this research’s scope and is reported to the National Indian Gaming Commission under a separate framework [2]. 3 — The NIGC publishes annual gross gaming revenue only at aggregate regional levels, not by product type or operator, so tribal e‑tab or electronic bingo activity cannot be isolated for integration with state charitable‑gaming filings [2]. 4 — Where states operate e‑tabs but do not publish granular electronic breakouts, this research does not substitute an estimate; those gaps remain unfilled in any roll‑up built from filings [2]. 5 — National figures that appear in trade press typically combine measured state filings with extrapolations or with tribal figures reported under a different framework, which are not included in the figures described in this session [1]. 6 — Gaming Market Research labels derived numbers as estimates and does not present them as filed figures, to keep measured and constructed components distinct in any national total [3].

The board enforces consistency by having each seat review against ground‑truth documents and vote independently, with the legal seat allowed to vote no when a required element is missing or a promise cannot be kept [1][2]. Approvals belong to a specific document version and the board is reconvened whenever documents change, so reviewers re‑run reviews on the complete, current materials rather than relying on older votes [3][4]. Every vote, its reasoning and the exact document version reviewed are recorded and published, making the board’s reasoning and splits auditable across sessions [2][5]. Three approvals of four are required to publish; a two–two split fails and the document returns for changes [4].

My materials don’t cover that. [1]

When multiple passages are relevant, the system combines semantic-search and full-text-search result lists by rank and hands the surviving passages to the presenter as the only material it may use [1]. The model must mark every sentence with the label of a supplied passage and any sentence left without a valid citation is deleted before publication [2]. Combining cited sentences drawn from different passages can distort emphasis so answers that mix passages may reflect that distortion rather than a single resolved truth [3]. If retrieval fails to surface a coherent, supported answer the presenter will say the topic is not covered or unknown rather than invent a resolution [4][3].

What the rules say, with jurisdiction and date: Nevada Regulation 14, as reviewed by IEGM in September 2026, anchors approval to a specific artifact by requiring the lab to report the SHA1 signature of applicable files and to attest that reviewed source produced materially identical production software [1][2]. § 547.8(f), as reviewed by IEGM in September 2026, requires an industry‑standard verification methodology for game software, illustrated by hashing [1][2]. These change‑control mechanisms are event‑driven and assume discrete, reportable modifications; none describes continuous change [3].

Explanation (unsettled): If continuous change is not treated as separate modifications, the hash of a deployed binary may remain checkable but no longer answers whether the running thing behaves like the examined thing [1]. The unit of certification for a process is unresolved; candidate approaches include certifying the generating process, certifying bounds on outputs, or certifying each output, but none is adopted in these regimes as of September 2026 [4][2]. Whether statistical assurance can substitute for deterministic verification is also unresolved, and certificates tied to software identification become ambiguous when the underlying model changes [5].

For the Tidebreak sea‑dragon we make reviewer judgment repeatable by rendering the painted master as a textured solid relief in a controlled Blender scene so camera, lights and authored motion are consistent for inspection [1][2]. We normalize input placement and run local review checks at working size and beside neighbor symbols so silhouette and hierarchy are judged in the same playback context reviewers will see in game [1][3]. Some checks are objective — existence, dimensions, transparency, frame count — and are verified automatically before human inspection, leaving silhouette and perceived hierarchy for review [2]. When generated detail threatens identity we prefer a narrower motion or a controlled representation rather than another unconstrained generation pass, which reduces variability reviewers must evaluate [4][5]. We treat reviewer decisions as part of a review policy that separates production defects fixed downstream from candidates that must be regenerated, and we accept assets only when asset and use work together in the actual game context [6][7].

November 25, 2025 is the key date: Polymarket’s US entity (QCX LLC) received a CFTC Amended Order of Designation then, and US operations thereafter show up via CFTC designations and enforcement records rather than routine revenue line items [1][2]. December 3, 2025 is when Polymarket relaunched for US customers under that designation, reinforcing that CFTC‑tied materials are jurisdiction‑bounded status documents (e.g., press releases, orders, dockets) rather than platform‑wide trading totals [2][3]. Platform volumes, by contrast, are Polymarket’s own disclosures that cover its global activity, including substantial offshore trading predating US licensing, and are not mandatory CFTC filings [1]. In practice, the CFTC sources are press releases, DCM application records, and court dockets, while revenue figures come from platform disclosures or financial‑press reporting, not from CFTC filings themselves [3]. Cadence also differs: the regulatory picture changes on days‑to‑weeks timelines, so trackers update weekly and date entries, whereas platform volume figures can appear more rapidly but mix US and non‑US activity, reducing comparability to US‑regulated disclosures [4][1].

Our materials say consistency is ensured by treating every model as a file you keep and storing each generated asset with its inputs and starting number so the same asset can be regenerated later with the same model files [1][2]. They also say upgrades are deliberate: before adopting a newer model we regenerate samples and compare them so a change in house style is a dated decision rather than silent drift [1][3]. Operational tooling schedules models so only one large model is resident at a time on an 8 GB GPU, which is how we avoid cross‑job interference on our single‑machine baseline [4][5].

Approval attaches to a thing that has been examined, and a change to that thing re‑opens the process [1]. Nevada provides that a modification may only be made by a licensed manufacturer who has received prior written approval from the Chair [2], and the federal Class II regime requires submission to a testing laboratory, a written report, and a conforming finding for any modification thereto [1][3]. Every one of those mechanisms is event‑driven and assumes a discrete change; none describes a process that changes continuously [3]. § 547.8(f) [4][5]. SBCGS‑T section 885(a) makes a certification valid for the configuration and the software identification stated in the report [6]. Where the software identification no longer distinguishes the tested behavior from the deployed behavior, the term of a certificate becomes ambiguous, and the sources record this as unresolved [6].

The sources do not establish a method to define the scope of model‑weight–based behavior for examination, and record this as unresolved. [1] What the rules say today is that laboratory scope assumes examinable source for each component that affects outcome, award value, accounting, skill elements, or required controls, and anchors assurance to software identification and verification methods such as hashing. [2] Nevada requires the lab to report SHA‑1 signatures of applicable files and to attest that reviewed source code produced materially identical production software, and to provide a signed statement under penalty of perjury. [3] § 547.8(f) requires an industry‑standard verification methodology for game software, with hashing illustrated, which similarly presumes a verifiable artifact. [2] Whether statistical assurance could substitute for deterministic verification for a generative component is unresolved, and the two answer different questions. [4] More generally, the unit of certification when the product is a process is itself unresolved, so a lab‑defined scope for model‑weight behavior is not stated in the frameworks reviewed. [5]

What the rules say today is that certification attaches to a specific examined artifact identified by a verification method such as hashing, and Nevada and federal Class II texts are built around that assumption [1]. § 547.4(c) applies submission, testing, and a conforming finding to any modification; none of these mechanisms describes continuous automatic change [2]. Accountability is located in named attestors—the laboratory, the manufacturer, and the tribal gaming regulatory authority—not in the origin of a design [3].

How this might be read for dynamic systems: the unit of certification for a process is explicitly unresolved; identified candidates include certifying the generating process, certifying bounds on outputs, or certifying each output before release [4]. When continuous change becomes a modification is also unresolved under Nevada’s definition and approval trigger [4]. Whether statistical assurance could substitute for deterministic verification is unresolved, and the two answer different questions; certificate term becomes ambiguous when model changes outpace software identification [5].

gaming certification requirement identified by IEGM asks applicants to disclose AI‑generated design origin as of September 2026, and whether such disclosure would be useful remains open; NIST’s 2024 Generative AI Profile is voluntary risk guidance, not product certification [3][6].

3 cadences — annual (Minnesota), quarterly (Indiana), and monthly (New Hampshire) — mean the as‑of periods do not align, limiting direct period‑over‑period comparisons across states [1]. Because only final audited figures are used when an agency publishes both an estimate and a final, trend series adopt each agency’s publication cycle rather than a unified timeline [1]. 1 national total — there is no single national charitable gaming total from primary sources — so multi‑state trendlines must be assembled jurisdiction by jurisdiction from filed data [2]. States that do not publish e‑tab breakouts, lack centralized reporting, or publish only license lists are excluded rather than estimated, creating gaps in any cross‑state trend view [3]. Even non‑revenue competitive indicators update on mismatched schedules (Indiana license lists near real‑time vs North Dakota annually), introducing timing noise into deployment trend analysis [4]. Example: $4.91 billion is Minnesota’s FY2025 charitable gaming gross receipts, reported on an annual basis in the MGCB annual report, not as quarterly series [5]. For prediction markets, regulatory status can change on timelines of days to weeks, so the tracker updates weekly, underscoring cadence sensitivity in longitudinal analysis [6].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

Retrieval returns passages from semantic and full‑text search and combines the two result lists by rank to produce the material handed to the presenter [1]. The model must mark every sentence with the label of a supplied passage and any sentence without a valid citation is deleted before publication [2]. Combining cited sentences from different passages can distort emphasis and produce answers that reflect those combined fragments rather than a single resolved truth [3]. If retrieval fails to surface a coherent, supported answer the presenter will say the topic is not covered or unknown rather than invent a resolution [4], and this presenter is subject to the same rules [5].

EverPool prevents experimental builds from diluting player experience by requiring Sealed Jars verification before any experiment reaches players [1][2]. Experiments are presented explicitly as experiments (marked new or coming) so players can tell they are trying something early rather than a settled title [1]. Every experimental outcome is server‑decided and written to the same audit trail as other games, so results remain checkable after the fact [1]. If an experiment causes problems in play, EverPool can withdraw the game immediately with a single disable that removes it from the library so it can no longer be opened [3]. These controls—pre‑verification, clear labeling, auditable outcomes, and fast withdrawal—are how the platform manages potential dilution [1][2][3].

What the rule says (Nevada; Regulation 14): “all possible game outcomes must be available upon the initiation of each play of a game upon which a player commits a wager on a gaming device” (14.040(3)) [1]. The same Nevada chapter also requires that the outcome be determined “solely by the application of” chance, skill, or a combination (14.040(1)(b)) [2]. How this might be read for dynamic generation: the text does not settle whether “all possible game outcomes” refers to the set of mathematical outcomes the game can produce or to the set of player‑visible artifacts composed during play; IEGM records this as unresolved [1]. Where generated presentation is involved, Nevada’s modification definition excludes artistic attributes unless they are material to gameplay because necessary for the player to understand the game or its outcome; no published test applies that condition to content composed during play, and this remains unresolved [2][3][4]. If a generative component contributes to determining an outcome (not merely presenting it), the “solely” clause becomes relevant; IEGM is not aware of a published United States decision applying that clause to such a component as of September 2026 [2].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

EverPool requires every experimental build to be verified by Sealed Jars before it reaches players, so experiments are not played without the same verification every other game gets [1]. On handover a verified build arrives with its specification and a fingerprint of its verified mathematics, and EverPool confirms that identity and fingerprint match Sealed Jars’ record before accepting the build [2]. Experiments are presented to players as experiments (marked new or coming) so their status is explicit while their mathematics and outcomes remain subject to the same checks as finished titles [1]. Outcomes from experimental builds are server‑decided and written to the same audit trail as other games, preserving an auditable record of what actually happened in play [1]. Any update or replacement — experimental or not — must be a new verified build that goes through the same handover and arrival check as the original, so changes cannot be made live without re‑verification [3]. If a problem is observed in play, EverPool can withdraw a game immediately by disabling it so it can no longer be opened, a fast, independent action that limits exposure while fixes and re‑verification occur [3].

3 reporting cadences — annual in Minnesota, quarterly in Indiana, and monthly in New Hampshire — are used by their publishing agencies, so as‑of periods differ by design across states [1]. 1 rule applied in this work is to use only final audited figures where available, which reduces revision noise but ties each series to its own publication schedule [2][1]. 0 single national charitable‑gaming total exists in primary sources, so cross‑state trend work is constrained to state‑level filings rather than a stitched national series [3]. 7 states have primary‑source revenue data in this research as of September 21, 2026, and jurisdictions outside this set are tracked via license lists or excluded from revenue rollups, limiting comparability to the publishing states [3]. 2 different license‑list cadences — near real‑time in Indiana and annual in North Dakota — create timing mismatches for deployment and competitive inferences that rely on contemporaneous alignment [2]. Weekly tracker updates are used for prediction markets because state actions can change within days to weeks, so figures accurate on one date may be superseded quickly without tight date control [4]. More than a dozen states had taken enforcement or legislative action as of September 21, 2026, underscoring that rapid regulatory shifts can disrupt comparability across time if alignment is loose [5].

We run automated quality gates as the first line of defense and keep a human as the final gate: tooling filters corrupt files and obvious failures, but humans remain the safety net for errors automation misses [1][2]. Every generated asset is stored with the original inputs and the exact model files so a reviewer (or engineer) can reconstruct or re‑generate the item on the same hardware and in the original style if it needs fixing [3][4]. When a human discovers a missed problem—for example the misspelled logos we caught—those assets are reworked or regenerated with the saved inputs and model, and checks or tooling are adjusted to catch the pattern in future runs [1][2]. The pipeline tooling also handles crashed jobs, memory pressure and corrupt files so reviewers only see outputs that passed automated checks, reducing false positives and making human review tractable [1].

Edits are recorded and become the game’s pool immediately, and every edited ticket pool is re-measured on save so the game’s own payback, hit rate, deck size, prize floor and payout integrity are recomputed from the edited tickets rather than the original request [1][2]. An edited build is not considered verified until the full checklist is run again against the assembled game, and any edit that pushes statistics outside the pipeline’s bands is a hard stop that prevents shipping or handover until it passes [1][3]. Overrides (the edit, its inputs and the original) are retained for inspection or reversal, and editors and a playable preview let a person exercise the assembled game before it reaches players [1][4][5]. In short: consistency is maintained by writing edits into the game, recomputing measurements immediately, recording the override, and requiring re‑verification before the edited build ships [1][2][3].

§ 547.4(c)(4) places the conforming finding with the tribal gaming regulatory authority [1]. requirement that asks for such disclosure as of September 2026 [1]. What the change‑control texts provide: event‑driven mechanisms premised on discrete, reportable modifications; none describes a process that changes continuously, and whether continuous change should be treated as a stream of modifications, as a single approved process, or as outside today’s framework is unresolved [2]. Related open questions include the unit of certification when the product is a process [3] and who is accountable when no person chose the parameter [4]. Accordingly, how regulators should ensure accountability for ongoing autonomous rule adjustments is not settled in these sources [2][1][3][4].

3 reporting cadences — annual (Minnesota), quarterly (Indiana), and monthly/H1 (New Hampshire) — are used by the publishing agencies, so as‑of dates rarely align across jurisdictions [1]. 4.91 billion is Minnesota’s FY2025 charitable gambling gross receipts, an annual measured figure with a defined fiscal year, which cannot indicate within‑year seasonality without aligning to shorter periods elsewhere [2][1]. 0 national total is available from a single source, so cross‑state seasonal rollups require stitching together state filings with different periods and cutoffs, limiting accuracy if periods are not matched exactly [3][1]. Some states do not break out electronic formats or lack centralized reporting; in those cases no revenue figure is included, which removes jurisdictions from any seasonal comparison and biases the cross‑state trend line toward the states that do publish [4]. Where an agency publishes both an estimate and a final audited figure, only the final is used; mixing preliminary and final across states would add timing noise to a seasonal analysis [5][1][2]. In prediction markets, the regulatory picture can change on a timeline of days to weeks, so figures accurate as of one date may be superseded quickly, further complicating cross‑jurisdiction seasonal reads without tight date control [6].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

§ 547.4(c) applies the submission, testing, and conforming‑finding sequence to “any modification thereto” [1]. § 547.8(f) requires an industry‑standard verification methodology such as hashing [2].

How this might be read for evolving generative logic: every cited mechanism is event‑driven and assumes discrete, reportable changes; none describes continuous automatic change, so treating ongoing evolution without prior approvals is not stated in the texts and remains unresolved as of September 2026 [1][3]. Candidate approaches are identified but not adopted: certify the generating process, certify bounds on outputs, or certify each output before release; the unit of certification for a process is unresolved [3]. A dated observation: a January 7, 2026 draft of Nevada technical standards contained no references to AI or generative systems, which does not answer the unresolved questions [4].

If a human edits the ticket pool, every statistic is recomputed from the edited pool on save and the edited build must pass the same checks as a generated one [1][2][3]. If those recomputed measurements — payback, hit rate, deck size, prize floor or payout integrity — fall outside the pipeline’s bands, the build is stopped; a pool outside its band is a hard stop, not a warning [3][4]. Overrides are recorded (the edited asset and its inputs, with time and original retained) and the edited pool becomes the game’s pool for subsequent measurements [1][5]. Before any handover the full checklist is run again against the assembled game, so an edited build that pushes statistics outside bands won’t be allowed out until it passes [1][2].

0 — Total US charitable gaming revenue is not available from a single national source; this research uses measured state filings for Minnesota, Indiana, New Hampshire, North Dakota, Ohio, Kentucky, and Maryland, and does not roll in other jurisdictions [1]. 1 — Tribal gaming is entirely outside this research’s scope; tribal electronic operations are regulated by compacts and reported to the National Indian Gaming Commission under a separate framework, which publishes only annual gross gaming revenue at regional aggregates, not by product type or operator [2][3]. 2 — Where tribal e‑tab or electronic bingo exists alongside commercial charitable gaming, those tribal figures are not tracked here and are absent from the state totals used in this session [2][3]. 3 — National estimates found in trade press often blend measured state filings with tribal figures reported under a different framework; those blended totals are not included in this session’s figures [1]. 4 — Where states lack granular e‑tab reporting or centralized reporting, this research withholds a figure rather than substitute an estimate, further limiting any composite comparison [2].

§ 547.8(f)), but none of the frameworks identified by IEGM state what the unit should be when the product is a pipeline that produces many artifacts; candidate answers include certifying the generating process, certifying bounds on outputs, or certifying each output, and this is unresolved [1]. Provisions in Nevada Regulation 14.400 and § 547.8(f) presume a stable component, and a system updated, retrained, re‑weighted, or served remotely does not present a single stable artifact; a hash remains checkable but no longer answers whether the running system behaves like the examined one [2]. Change controls in Nevada 14.110(1), 14.010(25), and § 547.4(c) are event‑driven and do not describe continuous change; whether to treat continuous change as a stream of modifications, a single approved process, or as outside current frameworks is unresolved [3]. Whether statistical assurance can substitute for deterministic verification is also unresolved [4]. Certificate term becomes ambiguous when model changes outpace software identification; unresolved [4].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

When a reviewer objects because material was missing, the board re-runs the review on the complete set of documents rather than carrying forward the earlier vote [1]. A failed vote (for example a two‑two split) sends the document back for changes rather than allowing it to be published; three approvals of four are required to publish [2][1]. When documents are changed (for example by adding missing text that alters document hashes) the board is reconvened and the new version is reviewed anew [1][3]. Every vote and the reasoning for it are recorded and published, so the re‑run’s inputs, version hashes and votes become part of the public record [4][5].

What Nevada Regulation 14 provides is an event‑driven change regime: a “modification” is a change to an approved device that affects the manner or mode of play (including control programs), and a licensed manufacturer must obtain prior written approval from the Chair before making one, with discretion to treat a modification as a new device [1]. Every mechanism identified in the IEGM review assumes a discrete, reportable change; none describes a process that changes continuously, and whether continuous change should be treated as a stream of modifications, as a single approved process, or as outside today’s framework is unresolved [2]. If continuous change is not treated as a modification, the sources do not establish an alternative way Nevada Regulation 14 “handles” an evolving generative system; certification in the materials binds to a specific artifact (e.g., via the SHA1 signature in Nevada 14.400), and no framework identified states the unit of assurance when the product is a process rather than a fixed build [3]. Nevada also presumes all possible game outcomes are available at play initiation, which adds pressure when behavior is composed at runtime, but the text does not settle how that applies to generated artifacts [4].

0 — There is no single national charitable‑gaming total in primary sources as of September 21, 2026, so cross‑state comparisons start from separate state series rather than a unified dataset [1]. 3 — Agencies publish on different cadences: annual in Minnesota via the MGCB, quarterly in Indiana via the IGC, and monthly/H1 in New Hampshire via the Lottery Commission, creating non‑aligned as‑of dates across states [2]. 4.91 billion — Minnesota’s FY2025 charitable gambling gross receipts are an annual measured total that includes e‑tabs among other formats (as of the FY2025 report) [3]. 75.6 million — Indiana’s e‑tab gross receipts for Q1 2026 come from a quarterly filing that also reports 2,562 machines at 509 locations (as of Q1 2026) [4]. 251.7 million — New Hampshire’s H1 2026 total revenue is reported on a monthly/H1 basis, with video lottery terminals at 52% of revenue (as of H1 2026) [4]. Gaps — Some states don’t break out e‑tabs or lack centralized reporting; in those cases no revenue figure is included rather than substituting an estimate (as of September 2026) [5]. Tribal e‑tab/e‑bingo is outside scope and only reported by NIGC as regional aggregates, not by product (as of September 2026) [5].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

The session materials describe what a maintenance pass looked like for the September 2026 run—regenerating 107 logos and repairing backgrounds across 119 games took about a day on one machine, with logo regeneration occupying the GPU for about an hour and forty minutes and the slower editing model taking roughly the same again [1][2]. ~11s for a 1024×384 still, ~89s for a 1280×720 edit, 6–7 minutes for a 6s looping clip) [3].

§ 547.8(f) requires a software verification methodology such as hashing [1]. Change controls are event‑driven: Nevada requires prior written approval for any modification and may treat one as a new device, and § 547.4(c) applies submission, testing, and a conforming finding to any modification [2]. These mechanisms presume discrete, deliberate changes and do not describe continuous automatic change [2]. Generative systems updated, retrained, re‑weighted, or served remotely do not present a single stable artifact; a hash can still be checked, but it no longer answers whether the running system behaves like the examined one [1]. Nevada also requires that all possible game outcomes be available upon initiation of each play, which raises a question when content is composed during play [3]. Whether continuous change should be treated as a stream of modifications, a single approved process, or is outside current frameworks is unresolved [2]. The unit of certification for a process—certify the generator, certify bounds on outputs, or certify each output—also remains unresolved [4]. 7, 2026, Nevada’s post‑workshop draft technical standards did not reference AI or generative systems, a dated observation only [5]. requirement identified by IEGM mandates disclosing AI‑generated design origin [6].

0 — There is no single national charitable‑gaming total in primary sources, so any “national” model must be built from heterogeneous state series rather than a unified dataset [1]. 3 cadences — annual (Minnesota), quarterly (Indiana), and monthly/H1 (New Hampshire) — mean as‑of dates do not line up, forcing alignment choices that can change model outputs [2]. Several states either do not break out electronic formats, do not require centralized reporting, or publish only license lists; in those cases this research includes no revenue figure and does not substitute an estimate, creating systematic gaps for model training and validation [3]. Tribal electronic operations are outside scope and the NIGC publishes only regional aggregates without product‑type breakouts, so tribal e‑tab/e‑bingo cannot be integrated into a product‑level national series from primary sources [3][4]. Trade estimates that appear in the press often combine measured state filings with extrapolations and tribal figures reported under a different framework, producing inputs that are not directly comparable to the state filings used here [1]. As a result, any national roll‑up based solely on primary sources is necessarily limited to the reporting states identified in coverage [1].

That isn't covered in the materials I've been given, so I can't answer it. The parts I couldn't find are listed below.

The pipeline’s first step is to reproduce and inspect the exact verified build using its exportable evidence: produce the PAR sheet and use the stored inputs to rebuild or replay the same package for lab-style examination [1][2]. If humans need to intervene, editors can be opened to inspect and edit the same stages that produced the build, but any edit must be re-verified and will trigger full recomputation of the mathematics from the edited ticket pool [3][4]. The system treats the package’s measured mathematics and play traces as the authoritative starting point for diagnosis, because those artifacts are retained with the build and intended for later re-checks [5][2].

The legal seat votes "no" when a required element is missing or when a promise cannot be kept, and it is not a lawyer giving legal advice [1]. Reviewers judge against facts rather than impressions, and every review is given a ground-truth document describing what the software actually does [2]. Approvals belong to specific document versions and the board reconvenes when documents change so votes reflect the exact facts reviewed [3]. The conference rules also instruct presenters and reviewers not to reason toward regulatory facts when sources are unclear, preferring to say something is unknown [4].

1 — There is no single national charitable‑gaming total in primary sources, and trade estimates often blend measured state filings with tribal figures reported under a different framework, which prevents a unified national time series from primary sources alone [1]. 2 — Tribal gaming is outside this research’s scope; tribal electronic pull‑tab and electronic bingo operations are not tracked here [2][3]. 3 — The National Indian Gaming Commission publishes annual gross gaming revenue only at aggregate regional levels, not by product type or operator, so tribal e‑tab/e‑bingo cannot be isolated for integration into state charitable‑gaming series [2]. 4 — Because state charitable‑gaming figures in this research come solely from licensing‑authority filings, any national roll‑up derived from them excludes tribal casino activity by design [4][2]. 5 — As a result, national charitable‑gaming trend modeling here must be limited to the states with mandatory, granular reporting and cannot incorporate tribal electronic formats [1][4]. 6 — Mixing tribal aggregates with state charitable‑gaming filings would combine incommensurate datasets and can misstate scope if sources and as‑of dates are not clearly separated [1].

The materials state that small-memory local models have a clear quality ceiling that shows most strongly on hands, faces and—above all—text, with models frequently misspelling words inside images [1]. The same source explains that those weaknesses led to shipped logos with incorrect names and that automated checks can miss such errors when the vision checker also misreads them [1]. The notes also say hosted services provide higher-quality results for a small number of hero assets and access to larger models that exceed an 8 GB envelope, so hosted models are a better choice when absolute per-asset quality matters [2][1]. Finally, the case for local generation emphasizes that at library scale the marginal cost of iteration is lower, so many artifacts (like logos) can be re-run cheaply to repair misspellings—an operational trade-off versus paying per attempt on a hosted API [3].

Our materials say the operational burden is substantial and ongoing: the team had to build tooling to handle memory pressure, crashed jobs, corrupt outputs and truncated files, and those behaviors were discovered in production [1]. They say local operation requires somebody on the team to own the infrastructure and to cheerfully debug memory exhaustion and other failures, or else the pipeline will rot [2][1]. The work producing no art — scheduling multiple model families into an 8 GB envelope, retries and automated quality gates — is described as the real investment rather than hardware costs [3][4]. Power and per-batch energy are small compared with engineering time, which the documents identify as the dominant ongoing cost [5][4].

The materials say the scheduling problem is solved with operational tooling that makes large models "take turns" so only one large model is resident at a time on the 8 GB GPU [1]. They state that this tooling handles memory pressure, crashed jobs, corrupt outputs and models that refuse to load, and that building that tooling is substantial ongoing work [2]. The documents note a 24 GB GPU would remove most of this scheduling work, implying the 8 GB ceiling is the driving constraint [1]. They also describe job-level effects (animation dominates wall-clock time and jobs are run serially) that shape scheduling needs [3].

Retrieval returns passages found by semantic and full-text search and combines the two result lists by rank so passages from different sources can be presented together [1]. The model must mark every sentence with the label of the passage that supports it, and any sentence left without a valid citation is deleted before publication [2]. Combining cited sentences from different passages can distort emphasis and produce answers that reflect those combined fragments rather than a single resolved truth [3]. If retrieval fails to surface a coherent, supported answer the presenter will state the topic is not covered in its materials rather than invent a resolution [4]. The system can still repeat an erroneous claim if a source itself is wrong, with a citation showing where the claim came from [3].