arXiv:2512.10411v5 Announce Type: replace-cross Abstract: The quadratic complexity of self attention in Transformer based LLMs renders long context inference prohibitively expensive. While Sliding Window Attention (SWA), the simplest sparse attention pattern, offers a linear complexity alternative, it suffers from catastrophic long context performance collapse, which stems from two fundamental factors: the training inference mismatch when […]
Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein
arXiv:2603.23361v2 Announce Type: replace-cross Abstract: Biological AI models increasingly predict complex cellular responses, yet their learned representations remain disconnected from the molecular processes they aim to capture. We present CDT-III, which extends mechanism-oriented AI across the full central dogma: DNA, RNA, and protein. Its two-stage Virtual Cell Embedder architecture mirrors the spatial compartmentalization of the […]
FEAST: Fully Connected Expressive Attention for Spatial Transcriptomics
arXiv:2603.25247v1 Announce Type: cross Abstract: Spatial Transcriptomics (ST) provides spatially-resolved gene expression, offering crucial insights into tissue architecture and complex diseases. However, its prohibitive cost limits widespread adoption, leading to significant attention on inferring spatial gene expression from readily available whole slide images. While graph neural networks have been proposed to model interactions between tissue […]
Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models
arXiv:2603.25250v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from ID classes and detecting OOD based on their distance to these labels. However, such labels may present poor activation on OOD samples, failing to capture the OOD […]
Fusion Learning from Dynamic Functional Connectivity: Combining the Amplitude and Phase of fMRI Signals to Identify Brain Disorders
arXiv:2603.24603v1 Announce Type: new Abstract: Dynamic functional connectivity (dFC) derived from resting-state functional magnetic resonance imaging (fMRI) has been extensively utilized in brain science research. The sliding window correlation (SWC) method is a widely used approach for constructing dFC by computing correlation coefficients between amplitude time series of signals from pairs of brain regions. In […]
Fast Iteration of Spaced k-mers
arXiv:2603.25417v1 Announce Type: new Abstract: We present efficient approaches for extracting spaced k-mers from nucleotide sequences. They are based on bit manipulation instructions at CPU level, making them both simpler to implement and up to an order of magnitude faster than existing methods. We further evaluate common pitfalls in k-mer processing, which can cause major […]
FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA
arXiv:2603.25243v1 Announce Type: cross Abstract: Large language models and autonomous agents are increasingly explored for EDA automation, but many existing integrations still rely on script-level or request-level interactions, which makes it difficult to preserve tool state and support iterative optimization in real production-oriented environments. In this work, we present FluxEDA, a unified and stateful infrastructure […]
EcoThink: A Green Adaptive Inference Framework for Sustainable and Accessible Agents
arXiv:2603.25498v1 Announce Type: new Abstract: As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint of Large Language Models (LLMs) presents a critical sustainability challenge. Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking, a redundancy that amplifies carbon emissions and operational barriers. […]
mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT
arXiv:2603.21606v4 Announce Type: replace-cross Abstract: Current language model training commonly applies multi-task Supervised Fine-Tuning (SFT) using a homogeneous compute budget across all sub-datasets. This approach is fundamentally sub-optimal: heterogeneous learning dynamics cause faster-learning tasks to overfit early while slower ones remain under-fitted. To address this, we introduce mSFT, an iterative, overfitting-aware search algorithm for multi-task […]
System-Anchored Knee Estimation for Low-Cost Context Window Selection in PDE Forecasting
arXiv:2603.25025v1 Announce Type: new Abstract: Autoregressive neural PDE simulators predict the evolution of physical fields one step at a time from a finite history, but low-cost context-window selection for such simulators remains an unformalized problem. Existing approaches to context-window selection in time-series forecasting include exhaustive validation, direct low-cost search, and system-theoretic memory estimation, but they […]
WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing
arXiv:2603.25226v1 Announce Type: cross Abstract: The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to “vibe coding”, where users can build complete projects and even control computers using natural language instructions. This paradigm has driven automated webpage development, but it introduces a new requirement about how to automatically […]
Sparse Visual Thought Circuits in Vision-Language Models
arXiv:2603.25075v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) improve interpretability in multimodal models, but it remains unclear whether SAE features form modular, composable units for reasoning-an assumption underlying many intervention-based steering methods. We test this modularity hypothesis and find it often fails: intervening on a task-selective feature set can modestly improve reasoning accuracy, while intervening […]