arXiv:2601.09742v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents, they face a critical scalability bottleneck known as the “Generalization-Specialization Dilemma.” Monolithic agents equipped with extensive toolkits suffer from context pollution and attention decay, leading to hallucinations. Conversely, static multi-agent swarms introduce significant latency and resource overhead. This paper introduces a Self-Evolving Concierge System, a novel architecture utilizing a Dynamic Mixture of Experts (DMoE) approach. Unlike recent self-improving agents that rewrite their […]

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arXiv:2601.17131v1 Announce Type: new Abstract: Subgame solving is a technique for scaling algorithms to large games by locally refining a precomputed blueprint strategy during gameplay. While straightforward in perfect-information games where search starts from the current state, subgame solving in imperfect-information games must account for hidden states and uncertainty about the opponent’s past strategy. Gadget games were developed to ensure that the improved subgame strategy is robust against any possible opponent’s strategy in a zero-sum game. Gadget games […]

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Vascular anomalies (VAs), including hemangiomas and vascular malformations, present a significant diagnostic challenge due to their high prevalence, complex classification (nearly 100 subtypes), and visual mimicry. Current Multimodal Large Language Models (MLLMs) struggle in this specialized domain, often failing to capture fine-grained visual features or lacking evidence-based reasoning. To address these limitations, we introduce HevaDx, an agentic diagnostic system that explicitly decouples visual perception from clinical reasoning. Leveraging a newly constructed large-scale dataset of VA patients, HevaDx employs […]

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arXiv:2601.10804v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain fundamentally constrained by the severe imbalance in global language resources. While over 7,000 languages are spoken worldwide, only a small subset (fewer than 100) has sufficient digital presence to meaningfully influence modern LLM training. This disparity leads to systematic underperformance, cultural misalignment, and limited accessibility for speakers of low-resource and extreme-low-resource languages. To address this gap, we introduce Bring Your Own Language (BYOL), […]

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I’ve been bashing away at this on and off for a year now, and I just seem to be chasing my tail. I am using TensorFlow to try to determine sea state from webcam stills, but I don’t seem to be getting any closer to a useful model. Training accuracy for a few models is around 97% and I have tried to prevent overtraining – but to be honest, whatever I try doesn’t make much difference. My predicted […]

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arXiv:2601.04193v1 Announce Type: cross Abstract: We propose a discrete transport equation on graphs which connects distributions on both vertices and edges. We then derive a discrete analogue of the Benamou-Brenier formulation for Wasserstein-$1$ distance on a graph and as a result classify all $W_1$ geodesics on graphs.

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Was working on a deepfake research paper and was trying to get access to DFDC dataset but for some reason the dfdc official website ain’t working, is it because I didnt acquire access to it ??? Is there any other way I can get hands on the dataset??? submitted by /u/Ok_Concert6723 [link] [comments]

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Successful comic book adaptations have been a thing since forever, but we can all agree that it’s only been in the last 15 years or so that comic book movies have really become a juggernaut at the box office. And the numbers back this up; multiple comic book movies have crossed the billion-dollar mark in the last decade and a half. This got me thinking, however. Out of all the comic book movies that have been released, which […]

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arXiv:2505.08683v3 Announce Type: replace Abstract: Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortized Bayesian Inference (ABI) can become computationally challenging. While ABI enables fast inference after training, generating sufficient training data still requires thousands of model simulations, which is infeasible for expensive models. Surrogate models offer a solution by providing approximate simulations at a lower computational cost, allowing the generation of […]

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