January 2026

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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arXiv:2601.17130v1 Announce Type: new Abstract: Graph neural networks (GNNs) have become the standard tool for encoding data and their complex relationships into continuous representations, improving prediction accuracy in several machine learning tasks like node classification and link prediction. However, their use in sensitive applications has raised concerns about the potential leakage of training data. Research on privacy leakage in GNNs has largely been shaped by findings from non-graph domains, such as images and tabular data. We emphasize the […]

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arXiv:2601.17124v1 Announce Type: new Abstract: The field of image generation is currently bifurcated into autoregressive (AR) models operating on discrete tokens and diffusion models utilizing continuous latents. This divide, rooted in the distinction between VQ-VAEs and VAEs, hinders unified modeling and fair benchmarking. Finite Scalar Quantization (FSQ) offers a theoretical bridge, yet vanilla FSQ suffers from a critical flaw: its equal-interval quantization can cause activation collapse. This mismatch forces a trade-off between reconstruction fidelity and information efficiency. In […]

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arXiv:2601.17123v1 Announce Type: new Abstract: We introduce and explore a new multimodal input representation for vision-language models: acoustic field video. Unlike conventional video (RGB with stereo/mono audio), our video stream provides a spatially grounded visualization of sound intensity across a scene, offering a new and powerful dimension of perceptual understanding. Our real-time pipeline uses low-cost beamforming microphone arrays that are already common in smart speakers and increasingly present in robotics and XR headsets, yet this sensing capability remains […]

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arXiv:2601.17112v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language tasks but suffer from extremely large memory footprints and computational costs. In this paper, we introduce a tensor compression framework based on the cproduct for computing low rank approximation In the first part of our approach, we leverage the algebraic structure of the cproduct to represent weight tensors such as those in embedding layers, attention projections, and feed forward networks in […]

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arXiv:2601.17111v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models are typically pre-trained with explicit load-balancing constraints to ensure statistically balanced expert routing. Despite this, we observe that even well-trained MoE models exhibit significantly imbalanced routing. This behavior is arguably natural-and even desirable – as imbalanced routing allows models to concentrate domain-specific knowledge within a subset of experts. Expert parallelism (EP) is designed to scale MoE models by distributing experts across multiple devices, but with a less-discussed assumption of balanced […]

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arXiv:2601.17110v1 Announce Type: new Abstract: Accurate short-term energy consumption forecasting is essential for efficient power grid management, resource allocation, and market stability. Traditional time-series models often fail to capture the complex, non-linear dependencies and external factors affecting energy demand. In this study, we propose a forecasting approach based on Recurrent Neural Networks (RNNs) and their advanced variant, Long Short-Term Memory (LSTM) networks. Our methodology integrates historical energy consumption data with external variables, including temperature, humidity, and time-based features. […]

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arXiv:2601.17109v1 Announce Type: new Abstract: Health information seeking has fundamentally changed since the onset of Large Language Models (LLM), with nearly one third of ChatGPT’s 800 million users asking health questions weekly. Understanding the sources of those AI generated responses is vital, as health organizations and providers are also investing in digital strategies to organically improve their ranking, reach and visibility in LLM systems like ChatGPT. As AI search optimization strategies are gaining maturity, this study introduces an […]

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arXiv:2601.17108v1 Announce Type: new Abstract: This paper proposes a Mamba-assisted neural network framework incorporating self-attention mechanism to achieve improved channel estimation with low complexity for orthogonal frequency-division multiplexing (OFDM) waveforms, particularly for configurations with a large number of subcarriers. With the integration of customized Mamba architecture, the proposed framework handles large-scale subcarrier channel estimation efficiently while capturing long-distance dependencies among these subcarriers effectively. Unlike conventional Mamba structure, this paper implements a bidirectional selective scan to improve channel estimation […]

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arXiv:2601.17107v1 Announce Type: new Abstract: Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This challenge is further compounded by privacy and ethical concerns associated with sensitive patient information. As a result, well-trained medical segmentation models on private datasets constitute valuable intellectual property requiring robust protection mechanisms. Existing model protection techniques primarily focus on classification and generative tasks, while segmentation models-crucial to medical image analysis-remain […]

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