ASSAY is a general agent harness built so that an LLM agent reasons its way through an unfamiliar world, learns that world from interaction at test time, and carries what it learns into later runs. A world is attached through one small adapter and a registry of permitted actions, and the agent is never told what its actions do: it discovers each by predicting its effect and paying for the answer. The single governing rule is that there is no action without a prediction, and every prediction is graded in code against the world's own response. That discipline is what forces the agent to build a world model under scarcity, which is where the reasoning and the test-time learning come from. The agent internalizes what it learns through self-declared instruments over its observations and through knowledge that carries across runs, demoted to hypothesis until re-earned, a tested capability whose measurement is the next phase. A by-product of the same discipline, not its aim, is a fully auditable record: every event lands on a hash-chained journal that a standalone public tool re-verifies from the artifacts alone, and a single ungated action invalidates a run. We evaluate the same harness on three worlds. On the ARC-AGI-3 public set it scores RHAE 96.54, confirmed by the benchmark server on a public scorecard, winning 24 of 25 games under hard action caps at a measured 8.0% exploration overhead and approximately zero capability cost over completed games. On the Factorio Learning Environment the same design fail-closes the sanctioned-interface bypass a published agent fell to. On the OOLONG long-context benchmark an early sweep places it in the same band as mature general harnesses across a 128K to 4M token ladder. The ARC-AGI-3 campaign also produced an epistemic finding: on six occasions the agent proved a level impossible, each proof consistent with every recorded transition and wrong exactly where none had gone, and a three-step audit of each proof's unexercised rules converted five into wins within existing budgets.
[Depreciated and replaced by V3] This pre-V3 paper is replaced by the corresponding V3 clean-room reconstruction: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work.A comprehensive, highly rigorous consolidated manuscript dismantling black-box AI through the deterministic Smithian Fold Theory. We present exact zero-parameter derivations of the fine-structure constant (137.03599917718), Levinthal's paradox, structural genetics, and SOTA empirical competitive parity in Chess, Symmetric Go, and Natural Language Processing. Unison AI operates at 57 million times the computational efficiency of modern Transformers, tracing physical geometry without gradient descent.
AuraOS Second Prior Art Disclosure (N9–N13): Holographic Headers, Gas‑Free Fractal Ledger, Swarm Mesh, Decoupled VR Rendering, and Interactive Narrative FST. This paper extends the AuraOS sovereign cognitive substrate with five new claims. N9 embeds a 1.2 KB hyperdimensional snapshot of the entire codebase into every file header, enabling O(1) integrity verification. N10 replaces blockchain gas fees with RAM‑staking and Proof‑of‑Presence derived from device entropy. N11 describes a swarm mesh for collective learning, elastic distributed compute, and zero‑trust routing. N12 introduces VSA‑addressed decoupled rendering, where a smartphone controls photorealistic VR/AR worlds by sending only hyperdimensional addresses (not assets). N13 presents an FST‑constrained interactive movie/game engine where NPCs use generative dialogue within narrative bounds, and player actions (including free speech) change the story. All claims are published under AGPLv3 §13 to prevent corporate capture.
Jack McGarrigle, Jessica Smith, J. Gwyn Griffiths, Jamie Torrance · 6 authors
Background and aims: Dark patterns are online platform design features that influence consumer behaviour to the advantage of the interface designer. In online gambling, such designs may exacerbate gambling-related harms, particularly among vulnerable consumers. This study aims to provide the first scoping review of dark patterns in online gambling. Methods: Following established scoping review frameworks, we systematically searched databases and grey literature using terms related to dark patterns and online gambling. The review protocol was preregistered. Results: Included articles (n = 16) addressed a variety of gambling-related dark patterns: hidden gambling management tools, inducements with complex conditions, minimum balances required to withdraw funds, unnecessary frictions involved in closing an account, high defaults in stake, deposit, reality check and deposit limit settings, and urgency-based gambling prompts. To address inconsistent terminology across studies, we synthesised existing literature by mapping identified dark patterns to a transdisciplinary framework, providing greater conceptual clarity and direction for future research. Discussions and conclusions: The potential for harm from dark patterns is evident, yet evidence on behavioural impacts is limited, hindered by restricted access to proprietary gambling operator data. Research in this area is sparse and fragmented, often using inconsistent terminology. Future studies should empirically investigate the influence of dark patterns on consumer behaviour, especially among vulnerable populations, and evaluate safer design alternatives. We recommend mandating gambling operators to collaborate with researchers to assess platform safety, and shifting the burden of proof onto operators to demonstrate that their platforms prioritise consumer safety and foster responsible gambling environments.
[Depreciated and replaced by V3] This pre-V3 paper is replaced by the corresponding V3 clean-room reconstruction: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. Opaque predictive reliability is valuable evidence of performance; it is not by itself a derivation, causal explanation or proof. This paper establishes the Smithian Fold Theory standard: one machine-checked self-proven theorem, zero axioms, zero fitted parameters, exact trace to the One, independent certificates, public evidence and a halt when forcing breaks. The synchronized corpus executes 326 suites and 2,002 exact checks with zero failures, with all 326 generated-C certificates identical to source. Its computational proofs carry the same method into sealed blind protein structure, exact and competitive Chess, exact and competitive Go, native zero-trained-parameter UnisonAI and measurement of fold law inside trained weights. The paper protects authorship and empirical method: agents do not declare Maria Smith's findings, convert their auxiliary failures into her results or impose incumbent theoretical walls. Benchmark victories remain explicit objectives; development evidence directs construction; every positive result is investigated and retained. Scientific author and publication authority: Maria Smith, Ernos Labs. Open source: Smithian Fold Theory of Everything.
As blockchain technology advances, Ethereum based gambling decentralized applications (DApps) represent a new paradigm in online gambling. This paper examines the concepts, principles, implementation, and prospects of Ethereum based gambling DApps. First, we outline the concept and operational principles of gambling DApps. These DApps are blockchain based online lottery platforms. They utilize smart contracts to manage the entire lottery process, including issuance, betting, drawing, and prize distribution. Being decentralized, lottery DApps operate without central oversight, unlike traditional lotteries. This ensures fairness and eliminates control by any single entity. Automated smart contract execution further reduces management costs, increases profitability, and enhances game transparency and credibility. Next, we analyze an existing Ethereum based gambling DApp, detailing its technical principles, implementation, operational status, vulnerabilities, and potential solutions. We then elaborate on the implementation of lottery DApps. Smart contracts automate the entire lottery process including betting, drawing, and prize distribution. Although developing lottery DApps requires technical expertise, the expanding Ethereum ecosystem provides growing tools and frameworks, lowering development barriers. Finally, we discuss current limitations and prospects of lottery DApps. As blockchain technology and smart contracts evolve, lottery DApps are positioned to significantly transform the online lottery industry. Advantages like decentralization, automation, and transparency will likely drive broader future adoption.
This study introduces GenePixKolor (GPK) fusion, an innovative approach to non-fungible token (NFT) generation and rarity ranking tailored for the gaming industry and tokenomics ecosystems. GPK Fusion leverages genetic algorithms, image processing and machine learning to create a comprehensive, four-stage system that optimizes both the trait generation and visual appeal of NFTs, while providing an advanced rarity ranking mechanism. The GPK Fusion’s rarity ranking method uniquely combines trait-based and pixel-based evaluations. Pixel rarity was assessed using color distribution for overall aesthetic appeal, and trait rarity was assessed using trait combinations. Fusing pixel rarity with trait rarity provides a more holistic assessment of an NFT’s uniqueness, balancing functional value with visual attractiveness. This approach addresses the limitations of existing rarity calculation methods, offering a more nuanced and comprehensive evaluation of the NFT. Empirical comparisons demonstrate that GPK Fusion consistently produces NFTs with superior trait combinations and enhanced visual appeal compared to traditional methods. Its rarity ranking shows a strong correlation with practical valuation strategies in real-time trading environments and enhances the NFT marketplaces. This research contributes to the evolving field of NFT design and valuation by providing game developers and tokenomics strategists with a powerful tool for creating, evaluating and ranking digital assets. GPK Fusion’s methodology opens new avenues for creating more engaging, visually striking and balanced NFTs. GPK potentially revolutionizes asset creation and valuation in the rapidly growing intersection of gaming and blockchain technologies.
Existing game tournaments have faced problems where large game companies or a small organizing committee monopolize decision-making on rules and revenue distribution, thereby hindering fairness and creativity.To overcome these limitations, this paper proposes a DAO (Decentralized Autonomous Organization)-based, broadcaster-centric operational model.In this model, all stakeholders in the tournament participate in transparent decision-making and revenue distribution through smart contracts, thereby enhancing fairness and trust.Furthermore, it manages copyright negotiations with game companies and oversees revenue models within the DAO, ultimately promoting ecosystem diversity and sustainability.Through conceptual simulations and case analyses, this study verifies the feasibility of the proposed model.Compared to traditional centralized structures, the findings confirm the model's potential for democratizing the decision-making process, establishing fair compensation frameworks, and proactively resolving copyright issues.
As an emerging video game genre in recent years, blockchain games have attracted substantial attention due to the strengths of blockchain technology and the popularity of Web3 ideals. However, blockchain games have encountered a dilemma due to the inability to attract traditional video game players. In this paper, we compare data gathered from Twitter, webpages, and research literature through the grounded theory (Glaserian approach) to uncover that the key to transcending the current state lies in the innovation of native and engaging game mechanics. We propose a design framework for blockchain game mechanics derived from the concept of emergent games, which integrates the basic elements of emergent games with the unique features of blockchain technology, discussing its potential design directions. Finally, we implement a high-fidelity prototype to serve as a case, instantiating a novel mechanic based on this framework and providing early proof of feasibility.
Abhimanyu Nag, Samrat Gupta, Sudipan Sinha, Arka Datta
Decentralized Finance (DeFi) governance models have become increasingly complex due to the involvement of numerous independent agents, each with their own incentives and strategies. To effectively analyze these systems, we propose using Multi Agent Influence Diagrams (MAIDs) as a powerful tool for modeling and studying the strategic interactions within DeFi governance. MAIDs allow for a comprehensive representation of the decision-making processes of various agents, capturing the influence of their actions on one another and on the overall governance outcomes. In this paper, we study a simple governance game that approximates real governance protocols and compute the Nash equilibria using MAIDs. We further outline the structure of a MAID in MakerDAO.
In this paper, we conduct a portfolio analysis based on the lottery-like characteristics of cryptocurrencies to examine return predictability. Our results show that cryptocurrencies with higher lottery-like characteristics exhibit lower one-month ahead returns. This phenomenon, known as the lottery-like effect, suggests that investors overvalue cryptocurrencies with stronger lottery-like traits, leading to lower future returns. Moreover, the effect persists over longer horizons, and the results remain robust after controlling for other crypto-asset characteristics.
This research paper presents a theoretical framework for the development and deployment of 'Smoky,' an innovative artificial intelligence system designed to confront systemic racism. Grounded in Africancentered scholarship, 'Smoky' represents a pioneering endeavor in leveraging technology for social equity. The paper explores the conceptualization, development, and potential applications of 'Smoky' within the decentralized autonomous organization (DAO) framework of Planetary Chess. Control theory and automation principles are integral to the design and operation of 'Smoky,' ensuring its effectiveness in addressing racial injustice. The paper highlights the role of control theory in system design, automation of monitoring and response, feedback mechanisms for learning and adaptation, integration with blockchain technology, decentralized control, and continuous evaluation and improvement. By integrating control theory and automation into the 'Smoky' system, the paper contributes to the advancement of technologydriven solutions for social justice. This paper serves as a call to philanthropists and potential collaborators to join in the realization of this vision, contributing to the advancement of technology-driven solutions for social justice.
Shikaku is a pencil puzzle consisting of a rectangular grid, with some cells containing a number. The player has to partition the grid into rectangles such that each rectangle contains exactly one number equal to the area of that rectangle. In this paper, we propose two physical zero-knowledge proof protocols for Shikaku using a deck of playing cards, which allow a prover to physically show that he/she knows a solution of the puzzle without revealing it. Most importantly, in our second protocol we develop a general technique to physically verify a rectangle-shaped area with a certain size in a rectangular grid, which can be used to verify other problems with similar constraints.
Oliver James Scholten, Nathan Hughes, Sebastian Deterding, Anders Drachen · 6 authors
Ethereum crypto-games are a booming and relatively unexplored area of the games industry. While there is no consensus definition yet, 'crypto-games' commonly denotes games that store tokens, e.g. in-game items, on a distributed ledger atop a cryptocurrency network. This enables the trading of game items for cryptocurrency, which can then be exchanged for regular currency. Together with their chance-based mechanics, this makes crypto-games part of the recent convergence of digital gaming and gambling. In a first effort to scope the field, this paper surveys popular crypto-games, which use the Ethereum cryptocurrency, to tease out characteristic technical properties and gameplay. It then compares the games' features with criteria found in current legal and psychological definitions of gambling. We find that the popular crypto-games selected meet a combined legal and psychological definition of gambling, and conclude with ramifications for future research.
In multi-prover interactive proofs (MIPs), the verifier is usually non-adaptive. This stems from an implicit problem which we call ``contamination'' by the verifier. We make explicit the verifier contamination problem, and identify a solution by constructing a generalization of the MIP model. This new model quantifies non-locality as a new dimension in the characterization of MIPs. A new property of zero-knowledge emerges naturally as a result by also quantifying the non-locality of the simulator.
The class TFNP is the search analog of NP with the additional guarantee that any instance has a solution. TFNP has attracted extensive attention due to its natural syntactic subclasses that capture the computational complexity of important search problems from algorithmic game theory, combinatorial optimization and computational topology. Thus, one of the main research objectives in the context of TFNP is to search for efficient algorithms for its subclasses, and at the same time proving hardness results where efficient algorithms cannot exist. Currently, no problem in TFNP is known to be hard under assumptions such as NP hardness, the existence of one-way functions, or even public-key cryptography. The only known hardness results are based on less general assumptions such as the existence of collision-resistant hash functions, one-way permutations less established cryptographic primitives (e.g. program obfuscation or functional encryption). Several works explained this status by showing various barriers to proving hardness of TFNP. In particular, it has been shown that hardness of TFNP hardness cannot be based on worst-case NP hardness, unless NP=coNP. Therefore, we ask the following question: What is the weakest assumption sufficient for showing hardness in TFNP? In this work, we answer this question and show that hard-on-average TFNP problems can be based on the weak assumption that there exists a hard-on-average language in NP. In particular, this includes the assumption of the existence of one-way functions. In terms of techniques, we show an interesting interplay between problems in TFNP, derandomization techniques, and zero-knowledge proofs.
Akari, Takuzu, Kakuro and KenKen are logic games similar to Sudoku. In Akari, a labyrinth on a grid has to be lit by placing lanterns, respecting various constraints. In Takuzu a grid has to be filled with 0's and 1's, while respecting certain constraints. In Kakuro a grid has to be filled with numbers such that the sums per row and column match given values; similarly in KenKen a grid has to be filled with numbers such that in given areas the product, sum, difference or quotient equals a given value. We give physical algorithms to realize zero-knowledge proofs for these games which allow a player to show that he knows a solution without revealing it. These interactive proofs can be realized with simple office material as they only rely on cards and envelopes. Moreover, we formalize our algorithms and prove their security.