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Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection
arXiv:2608.06434v1 Announce Type: new Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness. Recent dual-system Vision-Language-Action (VLA) architectures combine fast reactive control with slow deliberative reasoning to balance inference speed and task success rate. However, existing dual-process VLAs tightly couple the fast module to intermediate representations of the slow module, necessitating end-to-end joint training and limiting modularity, extensibility and flexible system switching. In this paper, we propose Environment-aware Model Selection (EMS), an adaptive VLA inference framework that switches between two fully decoupled systems of different scales through environment-aware model selection. The large-scale deliberative system provides globally consistent trajectory planning to ensure task success, while a lightweight reactive system enables high-frequency closed-loop control. A reinforcement-learning-based switching po
EntropyMoE: Entropy-Aware Sparse Expert Routing for Tokenizer-Free LLMs
arXiv:2608.06398v1 Announce Type: new Abstract: Recent byte-level large language models (LLMs) have made tokenizer-free modeling increasingly competitive by grouping bytes into dynamically sized patches. However, existing byte-patch architectures still apply the same dense feed-forward computation to every patch. This uniform computation cannot adapt model capacity to variations in patch semantics and granularity. We address this limitation with EntropyMoE, a Mixture-of-Experts (MoE) architecture designed for dynamic byte patches. EntropyMoE replaces the dense feed-forward modules in the global patch Transformer with Top-K expert layers. Each dynamic patch serves as the basic unit of expert routing, and its byte coverage determines its contribution to workload accounting. The router selects experts directly from patch entropy, using the same granularity signal that underlies dynamic patch construction to organize sparse computation. Patch entropy and length jointly define the feature s
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
arXiv:2608.06394v1 Announce Type: new Abstract: Multi-label node classification is an important yet challenging task in graph learning, where nodes exhibit multiple semantics simultaneously. Existing methods for multi-label node classification can effectively model multiple labels, while only considering in-domain scenarios where the model needs to be trained and tested within the same graph domain, resulting in limited cross-domain generalization. Recently, Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable graph representations across diverse graph domains and downstream tasks. However, existing GFMs are built upon single-label assumption, where all nodes are arbitrarily regarded as containing only one class of semantic and embedded into a single representation. For multi-label nodes, such a representation essentially approximates multiple semantics with a single point in the representation space, inevitably leading to semantic entanglement
I Thought Loading Data Was the Finish Line. It Was the Starting Point.
Building my first dbt models and learning what “analysis-ready” data actually means The post I Thought Loading Data Was the Finish Line. It Was the Starting Point. appeared first on Towards Data Science.
Top 5 Claude Skills for Marketing
Claude can write an ad or email from a prompt. This is usually done manually. Useful, but hardly a coherent system. The work still needs research, positioning, channel planning, quality checks, and reporting. Claude s marketing skills add to those missing processes. However, search results mix dedicated marketing repositories with huge general-purpose libraries. For a fair [ ] The post Top 5 Claude Skills for Marketing appeared first on Analytics Vidhya.
How to Implement Structured Output with Local LLMs
Why use it? How to implement it? What can we do when it fails? The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.
Before Q, K, and V: Reconstructing the Transformer
Many Transformer explainers start with the finished architecture. We ask why it looks the way it does. The post Before Q, K, and V: Reconstructing the Transformer appeared first on Towards Data Science.
Building Trustworthy Snowflake AI Agents with Semantic Governance
This year, many data teams have added AI agents to their roadmaps. The excitement is real: an agent that turns a two-day analysis into a two-minute conversation can change how analysts and business teams work together. But agents are only as reliable as the data foundation beneath them. Point them at raw tables or outdated [ ] The post Building Trustworthy Snowflake AI Agents with Semantic Governance appeared first on Analytics Vidhya.
Building a Streamlit UI for My LangGraph AI Agent
Building a production-ready web interface for a stateful LangGraph agent The post Building a Streamlit UI for My LangGraph AI Agent appeared first on Towards Data Science.
Matplotlib vs Plotly: Which Python Chart Tool Should You Choose?
From Static Plots to Interactive Data Exploration The post Matplotlib vs Plotly: Which Python Chart Tool Should You Choose? appeared first on Towards Data Science.
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science.
The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.
Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head. The post The Problem with pandas Isn’t Performance. It’s Cognitive Overhead. appeared first on Towards Data Science.