March 2026

arXiv:2603.25926v1 Announce Type: new Abstract: Soft context compression reduces the computational workload of processing long contexts in LLMs by encoding long context into a smaller number of latent tokens. However, existing frameworks apply uniform compression ratios, failing to account for the extreme variance in natural language information density. While adopting a density-aware dynamic compression ratio seems intuitive, empirical investigations reveal that models struggle intrinsically with operations parameterized by input dependent, continuous structural hyperparameters. To resolve this pitfall, we […]

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arXiv:2603.25925v1 Announce Type: new Abstract: Game-based learning (GBL) is widely adopted in mathematics education. It enhances learners’ engagement and critical thinking throughout the mathematics learning process. However, enabling players to learn intrinsically through mathematical games still presents challenges. In particular, effective GBL systems require dozens of high-quality game levels and mechanisms to deliver them to appropriate players in a way that matches their learning abilities. To address this challenge, we propose a framework, guided by adaptive learning theory, […]

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arXiv:2603.25924v1 Announce Type: new Abstract: Multimodal AI systems are evaluated by downstream task accuracy, but high accuracy does not mean the underlying data is coherent. A model can score well on Visual Question Answering (VQA) while its inputs contradict each other. We introduce the Multimodal Coherence Score (MCS), a metric that evaluates fusion quality independent of any downstream model. MCS decomposes coherence into four dimensions, identity, spatial, semantic, and decision, with weights learned via Nelder-Mead optimization. We evaluate […]

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arXiv:2603.25923v1 Announce Type: new Abstract: Deep learning models have shown promise in EEG-based outcome prediction for comatose patients after cardiac arrest, but their reliability is often compromised by subtle forms of data leakage. In particular, when long EEG recordings are segmented into short windows and reused across multiple training stages, models may implicitly encode and propagate label information, leading to overly optimistic validation performance and poor generalization. In this study, we identify a previously overlooked form of data […]

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arXiv:2603.25914v1 Announce Type: new Abstract: We resolve the long-standing open problem of Boolean dynamic data structure hardness, proving an unconditional lower bound of $Omega((log n / loglog n)^2)$ for the Multiphase Problem of Patrascu [STOC 2010] (instantiated with Inner Product over $mathbb{F}_2$). This matches the celebrated barrier for weighted problems established by Larsen [STOC 2012] and closes the gap left by the $Omega(log^{1.5} n)$ Boolean bound of Larsen, Weinstein, and Yu [STOC 2018]. The previous barrier was methodological: […]

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arXiv:2603.25906v1 Announce Type: new Abstract: Estimating human pose, classifying actions, and predicting movement progress are essential for human-robot interaction. While vision-based methods suffer from occlusion and privacy concerns in realistic environments, tactile sensing avoids these issues. However, prior tactile-based approaches handle each task separately, leading to suboptimal performance. In this study, we propose a Shared COnvolutional Transformer for Tactile Inference (SCOTTI) that learns a shared representation to simultaneously address three separate prediction tasks: 3D human pose estimation, action […]

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arXiv:2603.25905v1 Announce Type: new Abstract: Context and motivation: With the rapid advancement of AI technologies, there is an increasing need to understand how AI can be effectively integrated into RE processes. In recent years, several studies have explored the potential and challenges of applying GenAI to support or even automate RE-related activities. Question/problem: Despite the existing body of knowledge on AI’s potential for supporting RE activities, there is limited evidence on its practical applicability and limitations from an […]

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arXiv:2603.25904v1 Announce Type: new Abstract: Semiconductor intellectual property (IP) theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques. Traditional “cryptic” IC camouflaging methods typically focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis. This paper explores “mimetic deception,” where a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A). We provide a comprehensive evaluation of three deceptive methodologies: IP Camouflage, […]

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arXiv:2603.25903v1 Announce Type: new Abstract: Scaling robot learning to long-horizon tasks remains a formidable challenge. While end-to-end policies often lack the structural priors needed for effective long-term reasoning, traditional neuro-symbolic methods rely heavily on hand-crafted symbolic priors. To address the issue, we introduce ENAP (Emergent Neural Automaton Policy), a framework that allows a bi-level neuro-symbolic policy adaptively emerge from visuomotor demonstrations. Specifically, we first employ adaptive clustering and an extension of the L* algorithm to infer a Mealy […]

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arXiv:2603.25902v1 Announce Type: new Abstract: Humanoid robots have the promise of locomoting like humans, including fast and dynamic running. Recently, reinforcement learning (RL) controllers that can mimic human motions have become popular as they can generate very dynamic behaviors, but they are often restricted to single motion play-back which hinders their deployment in long duration and autonomous locomotion. In this paper, we present a pipeline to dynamically retarget human motions through an optimization routine with hard constraints to […]

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