20260518-morning:新增内容

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+---
+source_url: https://arxiv.org/abs/2505.05522
+ingested: 2026-05-15
+sha256: d2d4c033d6f7dd91d6af9eeefa3878c8cee14ac8c1e7acb762b5772c1cf8d004
+---
+
+# Continuous Thought Machines
+
+**Authors:** Luke Darlow, Ciaran Regan, Sebastian Risi, Jeffrey Seely, Llion Jones (Sakana AI, Tokyo; University of Tsukuba; IT University of Copenhagen)
+
+**arXiv:** 2505.05522v4 | **Category:** cs.LG | **Venue:** NeurIPS 2025
+
+## Abstract
+
+Biological brains demonstrate complex neural activity, where neural dynamics are critical to how brains process information. Most artificial neural networks ignore the complexity of individual neurons. We challenge that paradigm. By incorporating neuron-level processing and synchronization, we reintroduce neural timing as a foundational element. We present the Continuous Thought Machine (CTM), a model designed to leverage neural dynamics as its core representation. The CTM has two innovations: (1) neuron-level temporal processing, where each neuron uses unique weight parameters to process incoming histories; and (2) neural synchronization as a latent representation. The CTM aims to strike a balance between neuron abstractions and biological realism.
+
+We demonstrate the CTM's performance across a range of tasks, including solving 2D mazes, ImageNet-1K classification, parity computation, and more. Beyond displaying rich internal representations and offering a natural avenue for interpretation, the CTM can perform tasks that require complex sequential reasoning. The CTM can also leverage adaptive compute, where it can stop earlier for simpler tasks, or keep computing when faced with more challenging instances.

raw/papers/zeng-neurida-2025.md

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+source_url: https://arxiv.org/abs/2512.08483v3
+ingested: 2026-05-15
+sha256: 27f965211912f0ca663f249e72f2a2090294ca4538ada72e5dfc0ccf25f6523d
+---
+
+# NeurIDA: Dynamic Modeling for Effective In-Database Analytics
+
+**Authors:** Lingze Zeng (NUS), Naili Xing (NUS), Shaofeng Cai (NUS), Peng Lu (ZJU), Gang Chen (ZJU), Jian Pei (Duke), Beng Chin Ooi (ZJU)
+
+**arXiv:** 2512.08483v3 | **Category:** cs.DB | **Date:** 2025-12-15
+
+## Abstract
+
+Relational Database Management Systems (RDBMS) manage complex, interrelated data and support a broad spectrum of analytical tasks. With the growing demand for predictive analytics, the deep integration of machine learning (ML) into RDBMS has become critical. However, a fundamental challenge hinders this evolution: conventional ML models are static and task-specific, whereas RDBMS environments are dynamic and must support diverse analytical queries. Each analytical task entails constructing a bespoke pipeline from scratch, which incurs significant development overhead and hence limits wide adoption of ML in analytics.
+
+We present NeurIDA, an autonomous end-to-end system for in-database analytics that dynamically "tweaks" the best available base model to better serve a given analytical task. In particular, we propose a novel paradigm of dynamic in-database modeling to pre-train a composable base model architecture over the relational data. Upon receiving a task, NeurIDA formulates the task and data profile to dynamically select and configure relevant components from the pool of base models and shared model components for prediction. For friendly user experience, NeurIDA supports natural language queries; it interprets user intent to construct structured task profiles, and generates analytical reports with dedicated LLM agents. By design, NeurIDA enables ease-of-use and yet effective and efficient in-database AI analytics.
+
+**Results:** Up to 12% improvement in AUC-ROC and 25% relative reduction in MAE across ten tasks on five real-world datasets.