搜索结果：Matchmakers

This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of traditional Indian weddings. IWSO models the matchmaking process driven by collaboration among families, candidates, and matchmakers as a guided, selective search framework for solving complex optimization problems. The algorithm introduces two key innovations: (i) a matchmaker-guided influence strategy, where elite solutions direct the evolution of weaker candidates, enhancing convergence without external parameters; and (ii) an adaptive elimination and reinitialization mechanism that maintains diversity and prevents premature convergence by replacing underperforming individuals. IWSO employs a weighted multi-objective fitness function and analytically derived time and space complexity, benchmarked against existing optimization approaches such as Genetic Algorithm (GA), Partical Swarm Optimization (PSO), Differential Evolution (DE), Cuckoo Search (CS), etc. Extensive experiments on benchmark high-dimensional and multimodal test functions demonstrate superior performance of IWSO in terms of convergence speed, solution quality, and robustn

Fine-tuning Large Language Models (LLMs) for specialized domains is constrained by a fundamental challenge: the need for diverse, cross-organizational data conflicts with the principles of data privacy and sovereignty. While Federated Learning (FL) provides a framework for collaboration without raw data exchange, its classic centralized form introduces a single point of failure and remains vulnerable to model inversion attacks. Decentralized FL (DFL) mitigates this risk by removing the central aggregator but typically relies on inefficient, random peer-to-peer (P2P) pairings, forming a collaboration graph that is blind to agent heterogeneity and risks negative transfer. This paper introduces KNEXA-FL, a novel framework for orchestrated decentralization that resolves this trade-off. KNEXA-FL employs a non-aggregating Central Profiler/Matchmaker (CPM) that formulates P2P collaboration as a contextual bandit problem, using a LinUCB algorithm on abstract agent profiles to learn an optimal matchmaking policy. It orchestrates direct knowledge exchange between heterogeneous, PEFT-based LLM agents via secure distillation, without ever accessing the models themselves. Our comprehensive expe

Matchmaking has become a prevalent part in contemporary applications, being used in dating apps, social media, online games, contact tracing and in various other use-cases. However, most implementations of matchmaking require the collection of sensitive/personal data for proper functionality. As such, with this work we aim to reduce the privacy leakage inherent in matchmaking applications. We propose H-Elo, a Fully Homomorphic Encryption (FHE)-based, private rating system, which allows for secure matchmaking through the use of traditional rating systems. In this work, we provide the construction of H-Elo, analyse the security of it against a capable adversary as well as benchmark our construction in a chess-based rating update scenario. Through our experiments we show that H-Elo can achieve similar accuracy to a plaintext implementation, while keeping rating values private and secure. Additionally, we compare our work to other private matchmaking solutions as well as cover some future directions in the field of private matchmaking. To the best of our knowledge we provide one of the first private and secure rating system-based matchmaking protocols.

The internet folklore of the Cat Distribution System (CDS) humorously suggests that cats are "assigned" to people rather than intentionally sought. Beyond its playful origins, CDS reflects a culturally resonant way people perceive and engage in adoption, and this user context can guide the redesign and improvement of adoption systems. In the Philippines, where an estimated 13.11 million stray cats and dogs place the country sixth worldwide in overpopulation, this framing offers a novel way to rethink adoption platforms. We developed a prototype application inspired by CDS principles, focusing on features such as algorithmic matchmaking, community reporting, and proximity-based discovery. An initial evaluation with potential users (n=35) indicated that the system was positively received for its ease of use and its alignment with users' intuitive expectations, though participants highlighted areas for improvement in transparency of matchmaking and owner-adopter communication. The findings suggest that culturally embedded metaphors like CDS can shape mental models, making adoption processes feel more serendipitous and less transactional.

We present a family of Kaczmarz-based preference learning algorithms for real-time personalized matchmaking in reciprocal recommender systems. Post-step L2 normalization, common in Kaczmarz-inspired online learners, induces exponential recency bias: the influence of the t-th interaction decays as eta^(n - t), reaching approximately 1e-6 after just 20 swipes at eta = 0.5. We resolve this by replacing the normalization step with a Tikhonov-regularized projection denominator that bounds step size analytically without erasing interaction history. When candidate tag vectors are not pre-normalized, as in realistic deployments where candidates vary in tag density, the Tikhonov denominator ||a||^2 + alpha produces genuinely per-candidate adaptive step sizes, making it structurally distinct from online gradient descent with any fixed learning rate. We further derive a block variant that processes full swipe sessions as a single Gram matrix solve. Population-scale simulation over 6,400 swipes reveals that Block Normalized Kaczmarz (BlockNK), which combines the batch Gram solve with post-session L2 normalization, achieves the highest preference alignment (Align@20 = 0.698), the strongest inte

A formal mechanism is presented in which a willing regulator-matchmaker fosters cooperation on resources among participants in the AGI race, collects a Pigouvian tax based on the speed-up it induces, and invests the proceeds into alignment research. The construction is derived in the continuous-time options framework of Tan (2025) in which cooperation is treated as a jump in the underlying asset value of participating players, the Pigouvian component is matched to the marginal effect of increasing expected loss, and the total collected fund endogenizes the rate of learning on safety. It is shown how the framework allows for determining participation and optimal activity levels. Conditions under which it is optimal to enter the market are derived, and it is proven that if the orthogonality condition holds between the supported portfolio and the abilities component, the Suicide Region collapses at finite time, and the upper bound for this time is derived as sum of a deterministic and random term. Finally, if orthogonality is violated, it is proven that enhancing matchmaker capacity does not recover the market's superiority. The construction links research areas including two-sided ma

Social and personal decisions in relational domains such as matchmaking are deeply entwined with cultural norms and historical hierarchies, and can potentially be shaped by algorithmic and AI-mediated assessments of compatibility, acceptance, and stability. In South Asian contexts, caste remains a central aspect of marital decision-making, yet little is known about how contemporary large language models (LLMs) reproduce or disrupt caste-based stratification in such settings. In this work, we conduct a controlled audit of caste bias in LLM-mediated matchmaking evaluations using real-world matrimonial profiles. We vary caste identity across Brahmin, Kshatriya, Vaishya, Shudra, and Dalit, and income across five buckets, and evaluate five LLM families (GPT, Gemini, Llama, Qwen, and BharatGPT). Models are prompted to assess profiles along dimensions of social acceptance, marital stability, and cultural compatibility. Our analysis reveals consistent hierarchical patterns across models: same-caste matches are rated most favorably, with average ratings up to 25% higher (on a 10-point scale) than inter-caste matches, which are further ordered according to traditional caste hierarchy. These

In the context of blockchain, MEV refers to the maximum value that can be extracted from block production through the inclusion, exclusion, or reordering of transactions. Searchers often participate in order flow auctions (OFAs) to obtain exclusive rights to private transactions, available through entities called matchmakers, also known as order flow providers (OFPs). Most often, redistributing the revenue generated through such auctions among transaction creators is desirable. In this work, we formally introduce the matchmaking problem in MEV, its desirable properties, and associated challenges. Using cooperative game theory, we formalize the notion of fair revenue redistribution in matchmaking and present its potential possibilities and impossibilities. Precisely, we define a characteristic form game, referred to as RST-Game, for the transaction creators. We propose to redistribute the revenue using the Shapley value of RST-Game. We show that the corresponding problem could be SUBEXP (i.e. $2^{o(n)}$, where $n$ is the number of transactions); therefore, approximating the Shapley value is necessary. Further, we propose a randomized algorithm for computing the Shapley value in RST-

Service discovery is a fundamental process in wireless networks, enabling devices to find and communicate with services dynamically, and is critical for the seamless operation of modern systems like 5G and IoT. This paper introduces PriSrv+, an advanced privacy and usability-enhanced service discovery protocol for modern wireless networks and resource-constrained environments. PriSrv+ builds upon PriSrv (NDSS'24), by addressing critical limitations in expressiveness, privacy, scalability, and efficiency, while maintaining compatibility with widely-used wireless protocols such as mDNS, BLE, and Wi-Fi. A key innovation in PriSrv+ is the development of Fast and Expressive Matchmaking Encryption (FEME), the first matchmaking encryption scheme capable of supporting expressive access control policies with an unbounded attribute universe, allowing any arbitrary string to be used as an attribute. FEME significantly enhances the flexibility of service discovery while ensuring robust message and attribute privacy. Compared to PriSrv, PriSrv+ optimizes cryptographic operations, achieving 7.62* faster for encryption and 6.23* faster for decryption, and dramatically reduces ciphertext sizes by

A fair and fast matchmaking system is an important component of modern multiplayer online games, directly impacting player retention and satisfaction. However, creating fair matches between lobbies (pre-made teams) of heterogeneous skill levels presents a significant challenge. Matching based simply on average team skill metrics, such as mean or median rating or rank, often results in unbalanced and one-sided games, particularly when skill distributions are wide or skewed. This paper introduces Cinder, a two-stage matchmaking system designed to provide fast and fair matches. Cinder first employs a rapid preliminary filter by comparing the "non-outlier" skill range of lobbies using the Ruzicka similarity index. Lobbies that pass this initial check are then evaluated using a more precise fairness metric. This second stage involves mapping player ranks to a non-linear set of skill buckets, generated from an inverted normal distribution, to provide higher granularity at average skill levels. The fairness of a potential match is then quantified using the Kantorovich distance on the lobbies' sorted bucket indices, producing a "Sanction Score." We demonstrate the system's viability by ana

Large-scale competitive platforms are interacting multi-agent systems in which latent skills drift over time and pairwise interactions are shaped by matchmaking. We study a controlled rating dynamics in the mean-field limit and derive a kinetic description for the joint evolution of skills and ratings. In the Gaussian regime, we prove an exact moment closure and obtain a low-dimensional deterministic state dynamics for rating accuracy. This yields three main insights. First, skill drift imposes an intrinsic ceiling on long-run accuracy (the ``Red Queen'' effect). Second, with period-by-period scale control, the information content of interactions satisfies an invariance principle: under signal-matched scaling, the one-step accuracy transition is independent of matchmaking intensity. Third, the optimal platform policy separates: filtering is implemented by a greedy choice of the gain and rating scale, while matchmaking reduces to a static trade-off between match utility and sorting costs.

Real-time music alignment, also known as score following, is a fundamental MIR task with a long history and is essential for many interactive applications. Despite its importance, there has not been a unified open framework for comparing models, largely due to the inherent complexity of real-time processing and the language- or system-dependent implementations. In addition, low compatibility with the existing MIR environment has made it difficult to develop benchmarks using large datasets available in recent years. While new studies based on established methods (e.g., dynamic programming, probabilistic models) have emerged, most evaluations compare models only within the same family or on small sets of test data. This paper introduces Matchmaker, an open-source Python library for real-time music alignment that is easy to use and compatible with modern MIR libraries. Using this, we systematically compare methods along two dimensions: music representations and alignment methods. We evaluated our approach on a large test set of solo piano music from the (n)ASAP, Batik, and Vienna4x22 datasets with a comprehensive set of metrics to ensure robust assessment. Our work aims to establish a

Social belonging is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI (Social Agent Mediated Interactions) offers one solution by facilitating student connections, but its effectiveness may be constrained by an incomplete Theory of Mind, limiting its ability to create an effective 'mental model' of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. To explore this gap, we examine the viability of automated personality inference by proposing a personality detection model utilizing GPT's zeroshot capability to infer Big-Five personality traits from forum introduction posts, often encouraged in online courses. We benchmark its performance against established models, finding that while GPT models show promising results on this specific dataset, performance varies significantly across traits. We identify potential biases toward optimistic trait inference, particularly for traits with skewed distributions. We demonstrate a proof-of-concept integration of personality detection into SAMI's entity-based matchmaking system, focusing on three trai

The Ultimate Fighting Championship (UFC) has grown from a niche combat sport promotion into a globally recognized competitive enterprise. This study applies complex network analysis to explore the structural evolution of UFC matchmaking and its impact on competitive dynamics, fighter prominence, and audience engagement. By constructing directed and undirected networks where fighters represent nodes and bouts define edges, we examine key metrics such as degree distribution, clustering, betweenness centrality, and eigenvector centrality. Our findings reveal how the UFC's matchmaking strategies transitioned from tightly clustered, repetitive matchups in its early years to a more decentralized and strategically curated fight network. We identify distinct structural properties between winners and losers, showing that successful fighters maintain centrality while frequently losing fighters exhibit surprising degrees of sustained connectivity. Correlations with Pay-Per-View sales and Google search trends suggest that network dispersion and novelty in matchups drive greater audience interest, while excessive clustering and density reduce engagement. Furthermore, comparisons with official r

Robotic assembly remains a significant challenge due to complexities in visual perception, functional grasping, contact-rich manipulation, and performing high-precision tasks. Simulation-based learning and sim-to-real transfer have led to recent success in solving assembly tasks in the presence of object pose variation, perception noise, and control error; however, the development of a generalist (i.e., multi-task) agent for a broad range of assembly tasks has been limited by the need to manually curate assembly assets, which greatly constrains the number and diversity of assembly problems that can be used for policy learning. Inspired by recent success of using generative AI to scale up robot learning, we propose MatchMaker, a pipeline to automatically generate diverse, simulation-compatible assembly asset pairs to facilitate learning assembly skills. Specifically, MatchMaker can 1) take a simulation-incompatible, interpenetrating asset pair as input, and automatically convert it into a simulation-compatible, interpenetration-free pair, 2) take an arbitrary single asset as input, and generate a geometrically-mating asset to create an asset pair, 3) automatically erode contact surf

Large language models (LLMs), like ChatGPT, are capable of computing affectionately nuanced text that therefore can shape online interactions, including dating. This study explores how individuals experience closeness and romantic interest in dating profiles, depending on whether they believe the profiles are human- or AI-generated. In a matchmaking scenario, 307 participants rated 10 responses to the Interpersonal Closeness Generating Task, unaware that all were LLM-generated. Surprisingly, perceived source (human or AI) had no significant impact on closeness or romantic interest. Instead, perceived quality and human-likeness of responses shaped reactions. The results challenge current theoretical frameworks for human-machine communication and raise critical questions about the importance of authenticity in affective online communication.

Yang et al. proposed a lightweight certificateless multiuser matchmaking encryption (LC-MUME) scheme for mobile devices, published in IEEE Transactions on Information Forensics and Security (TIFS) (DOI: 10.1109/TIFS.2023.3321961). Their construction aims to reduce computational and communication overhead within a one-to-many certificateless cryptographic framework. The authors claim that their scheme satisfies existential unforgeability under chosen-message attacks (EUF-CMA) in the random oracle model. However, our cryptanalytic study demonstrates that the scheme fails to meet this critical security requirement. In particular, we show that a Type-I adversary can successfully forge a valid ciphertext without possessing the complete private key of the sender. Both theoretical analysis and practical implementation confirm that this attack can be mounted with minimal computational cost. To address these weaknesses, we propose a modification strategy to strengthen the security of matchmaking encryption schemes in mobile computing environments.

Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable machine learning (ML)-ready data. Addressing this fundamental data-centric problem has wide implications, especially in domains like healthcare, finance and e-commerce -- but also has the potential to benefit ML models more generally, by increasing the data available for ML model training. However, schema matching is a challenging ML task due to structural/hierarchical and semantic heterogeneity between different schemas. Previous ML approaches to automate schema matching have either required significant labeled data for model training, which is often unrealistic or suffer from poor zero-shot performance. To this end, we propose Matchmaker - a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker also self-improves in a zero-shot manner without the need for labeled demonstrations via a novel optimization approach, which constructs synthetic in-context demonstrations to guide the language model's reasoning process. Empirically