Ranking-dependent DeFi protocols create participation barriers for small-volume stakeholders due to competitive disadvantages based on fund size rankings. Our analysis reveals that brokers with higher fund balances achieve better revenue performance, disadvantaging smaller investors. We introduce LiquidityPool, a fund aggregation protocol that enables collective participation in ranking-dependent DeFi environments by pooling resources from multiple stakeholders. Through game-theoretic analysis of multi-leader multi-follower Stackelberg competition, we establish theoretical results that differ from traditional competition assumptions. Our analysis reveals knife-edge equilibrium properties where symmetric multi-party competition exists only under restrictive initial conditions. When initial asymmetry exceeds a critical threshold, protocols converge to a monopolistic market structure regardless of the starting configuration. We show that monopolistic concentration performs better than fragmented competition in terms of social welfare, contrary to the predictions of classical Bertrand and Cournot competition. This occurs because convex revenue functions create economies of scale that favor fund aggregation over competitive fragmentation. Experimental validation using real Ethereum blockchain data shows monopolization across different market structures. While welfare improvements are modest due to concentrated wealth distribution, LiquidityPool enables 98% of small-volume stakeholders to participate in protocols that were previously inaccessible. These findings suggest that traditional antitrust policies promoting market fragmentation may not be effective in ranking-dependent DeFi networks, where concentration enables rather than restricts efficient resource allocation and stakeholder accessibility.
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High dialogue engagement is a crucial indicator of an effective conversation. A reliable measure of engagement could help benchmark large language models, enhance the effectiveness of human-computer interactions, or improve personal communication skills. However, quantifying engagement is challenging, since it is subjective and lacks a ''gold standard''. This paper proposes PMIScore, an efficient unsupervised approach to quantify dialogue engagement. It uses pointwise mutual information (PMI), which is the probability of generating a response conditioning on the conversation history. Thus, PMIScore offers a clear interpretation of engagement. As directly computing PMI is intractable due to the complexity of dialogues, PMIScore learned it through a dual form of divergence. The algorithm includes generating positive and negative dialogue pairs, extracting embeddings by large language models (LLMs), and training a small neural network using a mutual information loss function. We validated PMIScore on both synthetic and real-world datasets. Our results demonstrate the effectiveness of PMIScore in PMI estimation and the reasonableness of the PMI metric itself.
We study the problem of auction design for advertising platforms that face strategic advertisers who are bidding across platforms. Each advertiser's goal is to maximize their total value or conversions while satisfying some constraint(s) across all the platforms they participates in. In this paper, we focus on advertisers with return-over-investment (henceforth, ROI) constraints, i.e. each advertiser is trying to maximize value while making sure that their ROI across all platforms is no less than some target value. An advertiser interacts with the platforms through autobidders -- for each platform, the advertiser strategically chooses a target ROI to report to the platform's autobidder, which in turn uses a uniform bid multiplier to bid on the advertiser's behalf on the queries owned by the given platform. Our main result is an auction comparator theorem that can be used to choose between different auction mechanisms by a revenue-maximizing platform operating in a competitive environment. Analogous to the Linkage Principle for interdependent values, our comparator demonstrates that, for a broad class of advertiser valuations, second-price auctions may sometimes generate higher revenue than first-price auctions. This contrasts sharply with the single-platform setting, where first-price auctions are optimal for both revenue and welfare. Our findings highlight the critical importance of considering inter-platform competition when designing optimal auctions.
The growing scale of ad auctions on online advertising platforms has intensified competition, making manual bidding impractical and necessitating auto-bidding to help advertisers achieve their economic goals. Current auto-bidding methods have evolved to use offline reinforcement learning or generative methods to optimize bidding strategies, but they can sometimes behave counterintuitively due to the black-box training manner and limited mode coverage of datasets, leading to challenges in understanding task status and generalization in dynamic ad environments. Large language models (LLMs) offer a promising solution by leveraging prior human knowledge and reasoning abilities to improve auto-bidding performance. However, directly applying LLMs to auto-bidding faces difficulties due to the need for precise actions in competitive auctions and the lack of specialized auto-bidding knowledge, which can lead to hallucinations and suboptimal decisions. To address these challenges, we propose a hierarchical Large auto-Bidding Model (LBM) to leverage the reasoning capabilities of LLMs for developing a superior auto-bidding strategy. This includes a high-level LBM-Think model for reasoning and a low-level LBM-Act model for action generation. Specifically, we propose a dual embedding mechanism to efficiently fuse two modalities, including language and numerical inputs, for language-guided training of the LBM-Act; then, we propose an offline reinforcement fine-tuning technique termed GQPO for mitigating the LLM-Think's hallucinations and enhancing decision-making performance without simulation or real-world rollout like previous multi-turn LLM-based methods. Experiments demonstrate the superiority of a generative backbone based on our LBM, especially in an efficient training manner and generalization ability.
Web3 technologies and decentralised finance (DeFi) have not only revolutionised finance, but they have also given rise to completely different concepts in terms of how we handle our wealth and how we utilise the Internet, which is still an unreliable medium for the exchange of information. To overcome trust issues, cryptographic tools were introduced and these days we even put our digital assets on the blockchain. These new technologies enabled us to make most financial solutions decentralised. A widely popular financial product that is implemented on blockchains is a constant product market maker (CPMM). A CPMM can be used to swap digital assets between mutually untrusting parties by relying on prices calculated from its bonding curve. A blockchain-based automated market maker (AMM) quotes prices that usually lag behind external market prices, and this price difference can be exploited by automated bots, aka. arbitrageurs. The question we investigate in this work focuses on this issue. More specifically, we experiment with different approaches from financial mathematics to account for stochastic price changes, perform extensive measurements to quantify the characteristics of the DeFi market, focusing on mainnet Ethereum and develop stochastic grid optimisers to fit model parameters to actual AMM market data that we extract from the logs of the Ethereum virtual machine. We report on model performance that is more aligned with reality compared to previous works that have relied on naive two-parameter Black-Scholes (BS) models. Our results reveal that hidden Markov models can give better estimates for both the number of arbitrage trades and the arbitrage profits when combined with a jump diffusion model and a two-parameter BS model.
High-quality data labeling is essential for training robust machine learning models; however, existing methods often ignore fraud or assume non-negative worker utility, failing to penalize harmful contributions without discouraging participation. To address this, we propose the Negativity-Aware Incentive (NAI) mechanism which introduces two novel components. First, the Ability-Result Characteristic Function (AR-CF) adapts and extends Shapley value theory through signed valuation to explicitly capture both positive and negative contributions, by combining workers' abilities with real-time task results to define contribution values. Second, a dynamic stake pool mechanism employs pre-commitment economics with adaptive dual-control parameters to balance fairness and operational efficiency. Through extensive experiments on multimodal datasets (images, text, audio, video), NAI outperforms state-of-the-art baselines: it improves video labeling accuracy by 16.6%, and reduces fraudulent behaviors by 33.9%. Furthermore, our deployment on the NetEase Youling crowdsourcing platform, serving 430,000 registered workers with 80,000 monthly active workers, validates NAI's real-world viability. Real-time A/B testing shows a 59.6% improvement in labeling quality for beginner tasks and a consistent reduction in fraud rates (14.8%-33.9%) across difficulty levels. This work establishes a paradigm shift in crowdsourcing system design, demonstrating that explicit negative modeling can enhance data quality, optimize costs, and foster participation at scale.
Optimizing the advertiser's cumulative value of winning impressions under budget constraints poses a complex challenge in online advertising, under the paradigm of AI-Generated Bidding (AIGB). Advertisers often have personalized objectives but limited historical interaction data, resulting in few-shot scenarios where traditional reinforcement learning (RL) methods struggle to perform effectively. Large Language Models (LLMs) offer a promising alternative for AIGB by leveraging their in-context learning capabilities to generalize from limited data. However, they lack the numerical precision required for fine-grained optimization. To address this limitation, we introduce GRPO-Adaptive, an efficient LLM post-training strategy that enhances both reasoning and numerical precision by dynamically updating the reference policy during training. Built upon this foundation, we further propose DARA, a novel dual-phase framework that decomposes the decision-making process into two stages: a few-shot reasoner that generates initial plans via in-context prompting, and a fine-grained optimizer that refines these plans using feedback-driven reasoning. This separation allows DARA to combine LLMs' in-context learning strengths with precise adaptability required by AIGB tasks. Extensive experiments on both real-world and synthetic data environments demonstrate that our approach consistently outperforms existing baselines in terms of cumulative advertiser value under budget constraints.
Transaction Fee Mechanisms (TFMs) study auction design in the Blockchain context, and emphasize robustness against miner and user collusion, moreso than traditional auction theory.[6] introduce the notion of a mechanism being c-Side-Contract-Proof (c-SCP), i.e., robust to a collusion of the miner and c users. Later work [5,14] shows a gap between the 1-SCP and 2-SCP classes. We show that the class of 2-SCP mechanisms equals that of any c-SCP with c\geq 2, under a relatively minor assumption of consistent tie-breaking. In essence, this implies that any mechanism vulnerable to collusion, is also vulnerable to a small collusion.
Auto-bidding systems are widely used in advertising to automatically determine bid values under constraints such as total budget and Return-on-Spend (RoS) targets. Existing works often assume that the value of an ad impression, such as the conversion rate, is known. This paper considers the more realistic scenario where the true value is unknown. We propose a novel method that uses conformal prediction to quantify the uncertainty of these values based on machine learning methods trained on historical bidding data with contextual features, without assuming the data are i.i.d. This approach is compatible with current industry systems that use machine learning to predict values. Building on prediction intervals, we introduce an adjusted value estimator derived from machine learning predictions, and show that it provides performance guarantees without requiring knowledge of the true value. We apply this method to enhance existing auto-bidding algorithms with budget and RoS constraints, and establish theoretical guarantees for achieving high reward while keeping RoS violations low. Empirical results on both simulated and real-world industrial datasets demonstrate that our approach improves performance while maintaining computational efficiency.
Opinion dynamics models how the publicly expressed opinions of users in a social network coevolve according to their neighbors as well as their own intrinsic opinion. Motivated by the real-world manipulation of social networks during the 2016 US elections and the 2019 Hong Kong protests, a growing body of work models the effects of a strategic actor who interferes with the network to induce disagreement or polarization. We lift the assumption of a single strategic actor by introducing a model in which any subset of network users can manipulate network outcomes. They do so by acting according to a fictitious intrinsic opinion. Strategic actors can have conflicting goals, and push competing narratives. We characterize the Nash Equilibrium of the resulting meta-game played by the strategic actors. Experiments on real-world social network datasets from Twitter, Reddit, and Political Blogs show that strategic agents can significantly increase polarization and disagreement, as well as increase the ''cost'' of the equilibrium. To this end, we give worst-case upper bounds on the Price of Misreporting (analogous to the Price of Anarchy). Finally, we give efficient learning algorithms for the platform to (i) detect whether strategic manipulation has occurred, and (ii) learn who the strategic actors are. Our algorithms are accurate on the same real-world datasets, suggesting how platforms can take steps to mitigate the effects of strategic behavior.
AI has reached a turning point. Systems can now perceive, generate, and act in language and image across digital platforms at unprecedented scale. Yet as AI moves from tools to collaborators—embedded in decision-making, institutions, and everyday life—a new requirement becomes unavoidable: AI must understand the world the way humans inhabit it. This talk introduces Cognitive World Modeling as the next phase of AI development. It unifies physical world modeling—time, space, causality, action—with mental world modeling—goals, beliefs, intentions, emotions, and social norms—into a single, persistent representation of reality as experienced by humans. Together, these models allow AI systems not only to predict outcomes, but to reason about meaning, context, and consequence. Cognitive World Modeling moves AI beyond reactive toward systems that can plan, explain, adapt, and collaborate over time. Alignment and trust emerge not as post hoc constraints, but as properties of systems that maintain accurate, evolving models of both the external world and the humans within it.
Measurement is a fundamental human activity. It is through measurement that we identify opportunity, and via opportunity that we seek improvement. In information retrieval and web search we measure how good search engines are at ordering answers to user queries, how close two ranked lists are to each other, how good LLMs are at re-ranking sets of candidate documents, and how close generated answer sentences are to the ideal output. This talk begins by motivating the well-known ''top-weighted'' measurements that are used when assessing the quality of a ranked list of documents against known annotations (qrels). In particular, Moffat and Zobel [2] suggest use of a parameterized geometric distribution to assign weights to ranks, arguing that a distribution with a bounded infinite sum is preferable to ones with unbounded sums; and also motivating the geometric sequence via a proposed user browsing model, the first time this had been done. Their rankbiased precision mechanism has the further advantage of allowing the extent of the uncertainty to be quantified, where uncertainty arises whenever the qrels are not comprehensive.
We often treat data as an infinitely reusable resource: a single dataset can support many analyses, train multiple models, and be shared widely without apparent cost. This talk argues that in important ways, data is not endlessly reusable. Instead, in certain contexts, data behaves like a consumable resource that degrades with use. The clearest example arises in the presence of privacy concerns. Fundamental results show that any informative public analysis of personal data inevitably leaks some information about the underlying individuals, and that these privacy losses accumulate across repeated uses of the same or overlapping datasets [1]. If some level of privacy is to be preserved, this imposes intrinsic limits on how many times data can be used. In joint work under submission, we connect this perspective to the mosaic effect from legal scholarship, arguing that privacy risks arise not only from combining data pieces, but also from combining seemingly innocuous data uses [6]. This view suggests regulatory and technical approaches that treat data use itself as a rival good [4]. Data can also degrade with use even when privacy is not at stake. A line of work on adaptive data analysis shows that repeatedly querying the same dataset can lead to overfitting: results that appear valid on the dataset but fail to generalize to the underlying distribution, even when the dataset is very large [2, 3, 5]. In both privacy and generalization, each interaction with a dataset consumes part of a limited resource, constraining future computations. Recognizing data degradation opens a range of research directions, including systems for tracking and budgeting data use, algorithmic techniques to mitigate degradation, the role of synthetic data and data curators, and new models of non-worst-case adaptive computation. Together, these directions work towards a data ecosystem that explicitly accounts for data degradation.
AI is no longer just a component of Web systems; it is increasingly shaping the experiences users have online. From search and recommendation to conversational and generative interfaces, AI is redefining how people interact with content at Web scale. In this keynote, I reflect on how recent advances in AI, including deep learning and generative models, are reshaping the design space of Web technologies and the systems that support them. Drawing on insights from developing AI-driven systems at Spotify, I discuss how search and recommendation are evolving into interactive, intent-aware, and user-controllable experiences that support exploration and discovery. The talk highlights emerging system paradigms, deployment challenges, and open research questions around building such experiences at scale, and reflects on the implications for the design of future Web systems and interactions.