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This paper addresses the challenge of systematically discovering cybercriminal communities on Telegram, a popular encrypted messaging platform increasingly used for illicit activities
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This paper studies a novel security vulnerability in stateful agentic systems that extend large language models (LLMs) beyond session-bounded assistants into systems persisting context across multi…
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AAD-1 addresses key limitations in one-step autoregressive image-to-video generation, where prior adversarial distillation approaches struggled with motion collapse and unstable training that produ…
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This paper addresses the challenge of denoising diffusion-weighted imaging (DWI) scans corrupted by signal-dependent Rician noise, which arises from magnitude reconstruction and complicates restora…
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This paper addresses the challenge of generating attosecond pulse trains from solid-state materials, specifically single-layer graphene (SLG), by identifying macroscopic phase-matching conditions i…
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This work addresses a fundamental challenge in high-temperature cuprate superconductors — isolating intrinsic CuO2 plane physics by eliminating charge-reservoir layer complexities
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This paper addresses the challenge of training high-quality visual editing models without paired source-target data, which is especially difficult for video editing where paired data is extremely c…
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CLI-Anything addresses a fundamental misalignment in current AI-based computer-use agents that control software primarily through brittle GUI interactions
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Privately Inferred Credentials (𝜋Creds) address the challenge of creating decentralized, privacy-preserving verifiable credentials over unstructured and semantically rich data, which existing zero-…
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Demo2Tutorial addresses the challenge of automatically converting raw human computer interaction recordings into structured, multimodal software tutorials
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This paper addresses the fundamental challenge of extending the Forward-Forward (FF) algorithm—a biologically plausible and local alternative to backpropagation (BP)—from classification to real-wor…
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This paper addresses the problem of forecasting political news engagement on Twitter over long time horizons, which is crucial for understanding dynamics such as hyperpartisanship, filter bubbles, …
