arXiv:2601.02366v1 Announce Type: new Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store essential collaborative information. However, this ID-based paradigm faces challenges in transferring to a new domain, making it hard to build a pre-trained graph recommendation model. This phenomenon primarily stems from two inherent challenges: (1) the non-transferability of ID embeddings due to isolated domain-specific ID spaces, and (2) structural incompatibility between heterogeneous interaction graphs across […]

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Key Highlights: Liquid AI Launches New On-Device AI Models for Phones, Cars, and Smart Devices AI startup Liquid AI announced on January 5, 2026, that it is launching a new family of models called LFM2.5-1.2B, designed to run artificial intelligence directly on devices such as smartphones, vehicles, and Internet-connected hardware. The launch was detailed in a blog post published by the company, outlining its latest push toward on-device AI systems. Instead of relying on cloud servers, these models […]

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arXiv:2601.03269v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, yet their deployment in enterprise environments reveals a critical limitation: inconsistent adherence to custom instructions. This study presents a comprehensive evaluation of 13 leading LLMs across instruction compliance, response accuracy, and performance metrics in realworld RAG (Retrieval-Augmented Generation) scenarios. Through systematic testing with samples and enterprise-grade evaluation protocols, we demonstrate that instruction following varies dramatically across models, with Claude-Sonnet-4 […]

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arXiv:2601.01238v1 Announce Type: new Abstract: Bayesian model selection commonly relies on Laplace approximation or the Bayesian Information Criterion (BIC), which assume that the effective model dimension equals the number of parameters. Singular learning theory replaces this assumption with the real log canonical threshold (RLCT), an effective dimension that can be strictly smaller in overparameterized or rank-deficient models. We study linear-Gaussian rank models and linear subspace (dictionary) models in which the exact marginal likelihood is available in closed form […]

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Science Robotics, Volume 10, Issue 109, December 2025.

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OpenAI introduces a new framework and evaluation suite for chain-of-thought monitorability, covering 13 evaluations across 24 environments. Our findings show that monitoring a model’s internal reasoning is far more effective than monitoring outputs alone, offering a promising path toward scalable control as AI systems grow more capable.

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This paper argues that transformers are being overused as universal execution engines. I propose a meaning-first execution framework that decouples semantic proposal from model execution, allowing inference to be conditionally invoked only when needed. The result is that a large fraction of transformer calls can be skipped without changing correctness on invoked cases, suggesting many current efficiency limits are architectural rather than model-intrinsic. The work is model-agnostic and sits above existing transformers. Feedback welcome, especially around routing guarantees […]

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Preliminaries A Simple Linear Regression Least Squares Estimation From Linear Regression to Linear Classification pseudo-inverse Least Squares for Classification1 Least-squares for linear regression had been talked about in ‘Simple Linear Regression’. And in this post, we want to find out whether this powerful algorithm can be used in classification. Recalling the distinction between the properties of classification and regression, two points need to be emphasized again(‘From Linear Regression to Linear Classification’):

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