arXiv:2512.15534v2 Announce Type: replace Abstract: Discrete dynamical models underpin systems biology, but we still lack substrate-agnostic diagnostics for when such models can sustain genuinely open-ended evolution (OEE): the continual production of novel phenotypes rather than eventual settling. We introduce a simple, model-independent metric, Omega, that quantifies OEE as the residence-time-weighted average of attractor cycle lengths […]
Generating Key Postures of Bharatanatyam Adavus with Pose Estimation
arXiv:2603.29570v1 Announce Type: cross Abstract: Preserving intangible cultural dances rooted in centuries of tradition and governed by strict structural and symbolic rules presents unique challenges in the digital era. Among these, Bharatanatyam, a classical Indian dance form, stands out for its emphasis on codified adavus and precise key postures. Accurately generating these postures is crucial […]
Early Exiting Predictive Coding Neural Networks for Edge AI
arXiv:2309.02022v2 Announce Type: replace-cross Abstract: The Internet of Things is transforming various fields, with sensors increasingly embedded in wearables, smart buildings, and connected equipment. While deep learning enables valuable insights from IoT data, conventional models are too computationally demanding for resource-limited edge devices. Moreover, privacy concerns and real-time processing needs make local computation a necessity […]
Robust Safety Monitoring of Language Models via Activation Watermarking
arXiv:2603.23171v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can be misused to reveal sensitive information, such as weapon-making instructions or writing malware. LLM providers rely on $emphmonitoring$ to detect and flag unsafe behavior during inference. An open security challenge is $emphadaptive$ adversaries who craft attacks that simultaneously (i) evade detection while (ii) eliciting unsafe […]
Streaming 4D Visual Geometry Transformer
arXiv:2507.11539v2 Announce Type: replace-cross Abstract: Perceiving and reconstructing 3D geometry from videos is a fundamental yet challenging computer vision task. To facilitate interactive and low-latency applications, we propose a streaming visual geometry transformer that shares a similar philosophy with autoregressive large language models. We explore a simple and efficient design and employ a causal transformer […]
Bringing Up a Bilingual BabyLM: Investigating Multilingual Language Acquisition Using Small-Scale Models
arXiv:2603.29552v1 Announce Type: cross Abstract: Multilingualism is incredibly common around the world, leading to many important theoretical and practical questions about how children learn multiple languages at once. For example, does multilingual acquisition lead to delays in learning? Are there better and worse ways to structure multilingual input? Many correlational studies address these questions, but […]
Past, Present, and Future of Bug Tracking in the Generative AI Era
arXiv:2510.08005v3 Announce Type: replace-cross Abstract: Traditional bug-tracking systems rely heavily on manual reporting, reproduction, classification, and resolution, involving multiple stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires substantial coordination and human effort, widens the communication gap between non-technical users and developers, and significantly slows the process from bug […]
LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface
arXiv:2603.22519v2 Announce Type: replace-cross Abstract: Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the information conveyed to an LLM often has a richer structure and semantics, which is not conveyed in a string. For example, most prompts contain both instructions […]
InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
arXiv:2512.08829v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly tasked with ultra-long multimodal understanding. While linear architectures offer constant computation and memory footprints, they often struggle with high-frequency visual perception compared to standard Transformers. To bridge this gap, we introduce textbfInfiniteVL. We first develop a hybrid base model called textbfInfiniteVL-Base that interleaves a small […]
Reducing Complexity for Quantum Approaches in Train Load Optimization
arXiv:2603.29543v1 Announce Type: cross Abstract: Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary source of this complexity arises from the need to model and reduce rehandle operations-unproductive crane moves required to access blocked containers. Conventional mathematical formulations address this by introducing […]
Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
arXiv:2602.15772v2 Announce Type: replace-cross Abstract: Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade-off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To […]
LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction
arXiv:2603.21045v3 Announce Type: replace-cross Abstract: Diffusion-based image super-resolution (SR), which aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations, faces a fundamental trade-off between inference efficiency and reconstruction quality. The state-of-the-art residual-shifting diffusion framework achieves efficient 4-step inference, yet suffers from severe performance degradation in compact sampling trajectories. This is mainly attributed to […]