Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance and struggle with modality/task discrepancies, as well as model heterogeneity. To address these challenges, we propose FedAFD, a unified MFL framework that enhances client and server learning. On the client side, we introduce a bi-level adversarial alignment strategy to align local and global […]

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arXiv:2604.14256v1 Announce Type: new Abstract: Modern information access ecosystems consist of mixtures of systems, such as retrieval systems and large language models, and increasingly rely on marketplaces to mediate access to models, tools, and data, making competition between systems inherent to deployment. In such settings, outcomes are shaped not only by benchmark quality but also by competitive pressure, including user switching, routing decisions, and operational constraints. Yet evaluation is still largely conducted on static benchmarks with accuracy-focused measures […]

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arXiv:2601.14455v1 Announce Type: new Abstract: GitHub Actions is a widely used platform that allows developers to automate the build and deployment of their projects through configurable workflows. As the platform’s popularity continues to grow, it has become a target of choice for recent software supply chain attacks. These attacks exploit excessive permissions, ambiguous versions, or the absence of artifact integrity checks to compromise workflows. In response to these attacks, several security scanners have emerged to help developers harden […]

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How smart contracts can encode fiscal discipline, aid conditionality, and trust Financial aid and monetary interventions in emerging markets are constrained less by intent than by execution. Capital is typically distributed through multilayered institutional pipelines involving governments, non-governmental organisations, correspondent banks, and local intermediaries. Each layer introduces delay, opacity, and opportunity for misallocation. By the time funds reach their intended recipients, their effectiveness is often diminished. These systems exhibit familiar failure modes. Disbursement delays undermine urgency. Leakage through […]

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arXiv:2509.00923v2 Announce Type: replace-cross Abstract: Monte Carlo Counterfactual Regret Minimization (MCCFR) has emerged as a cornerstone algorithm for solving extensive-form games, but its integration with deep neural networks introduces scale-dependent challenges that manifest differently across game complexities. This paper presents a comprehensive analysis of how neural MCCFR component effectiveness varies with game scale and proposes an adaptive framework for selective component deployment. We identify that theoretical risks such as nonstationary target distribution shifts, action support collapse, variance explosion, […]

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Every decentralized protocol built so far has shipped the same architectural mistake. Here’s what we borrowed from 150,000 years of human behavior to fix it. There’s a pattern in how anthropologists describe information flow in pre-literate communities. News doesn’t broadcast — it cascades. A person hears something from someone they trust, judges the claim against what they know of that source, and decides how far to pass it along. The further information travels from its origin, the more […]

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General aviation fault diagnosis and efficient maintenance are critical to flight safety; however, deploying deep learning models on resource-constrained edge devices poses dual challenges in computational capacity and interpretability. This paper proposes LiteInception–a lightweight interpretable fault diagnosis framework designed for edge deployment. The framework adopts a two-stage cascaded architecture aligned with standard maintenance workflows: Stage 1 performs high-recall fault detection, and Stage 2 conducts fine-grained fault classification on anomalous samples, thereby decoupling optimization objectives and enabling on-demand allocation […]

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Real-world decision-making systems operate in environments where state transitions depend not only on the agent’s actions, but also on textbf{exogenous factors outside its control}–competing agents, environmental disturbances, or strategic adversaries–formally, $s_{h+1} = f(s_h, a_h, bar{a}_h)+ω_h$ where $bar{a}_h$ is the adversary/external action, $a_h$ is the agent’s action, and $ω_h$ is an additive noise. Ignoring such factors can yield policies that are optimal in isolation but textbf{fail catastrophically in deployment}, particularly when safety constraints must be satisfied. Standard Constrained MDP […]

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arXiv:2601.09805v1 Announce Type: new Abstract: Modern logical reasoning with LLMs primarily relies on employing complex interactive frameworks that decompose the reasoning process into subtasks solved through carefully designed prompts or requiring external resources (e.g., symbolic solvers) to exploit their strong logical structures. While interactive approaches introduce additional overhead, hybrid approaches depend on external components, which limit their scalability. A non-interactive, end-to-end framework enables reasoning to emerge within the model itself — improving generalization while preserving analyzability without any […]

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We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be. Robots are not yet walking the streets, nor are most of us talking to AI all day. People still die of disease, we still can’t easily go to space, and there is a lot about the universe we don’t understand. And yet, we have recently […]

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