February 2026

arXiv:2602.09190v1 Announce Type: new Abstract: Existing work has linked properties of a function’s gradient to the difficulty of function approximation. Motivated by these insights, we study how gradient information can be leveraged to improve neural network’s ability to approximate high-frequency functions, and we propose a gradient-based residual connection as a complement to the standard identity skip connection used in residual networks. We provide simple theoretical intuition for why gradient information can help distinguish inputs and improve the approximation […]

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arXiv:2602.09188v1 Announce Type: new Abstract: As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, their circuit-switched nature raises a fundamental question for collective communication: when and how should the interconnect be reconfigured to realize these benefits? Establishing direct optical paths can reduce congestion and propagation delay, but each reconfiguration incurs non-negligible overhead, making naive per-step reconfiguration impractical. We present Harvest, a systematic approach for synthesizing topology reconfiguration schedules that minimize collective completion time in photonic […]

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arXiv:2602.09185v1 Announce Type: new Abstract: AI coding agents are rapidly transforming software engineering by performing tasks such as feature development, debugging, and testing. Despite their growing impact, the research community lacks a comprehensive dataset capturing how these agents are used in real-world projects. To address this gap, we introduce AIDev, a large-scale dataset focused on agent-authored pull requests (Agentic-PRs) in real-world GitHub repositories. AIDev aggregates 932,791 Agentic-PRs produced by five agents: OpenAI Codex, Devin, GitHub Copilot, Cursor, and […]

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arXiv:2602.09182v1 Announce Type: new Abstract: Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choices and implementations across different frameworks, software dependencies, and hardware backends along with the lack of statistical validation can lead to previously unexplored attack vectors on machine learning systems. Such attacks on randomness sources […]

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arXiv:2602.09181v1 Announce Type: new Abstract: Exploiting the analogy between Gaussian Distributions and Gaussian Processes’ posterior, we present how the weighted Wasserstein Barycenter of Gaussian Processes (W2BGP) can be used to unify, under a common framework, different exotic Bayesian Optimization (BO) tasks. Specifically, collaborative/federated BO, (synchronous) batch BO, and multi-fidelity BO are considered in this paper. Our empirical analysis proves that each one of these tasks requires just an appropriate weighting schema for the W2BGP, while the entire framework […]

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arXiv:2602.09176v1 Announce Type: new Abstract: We consider finite blocklength lossy compression of information sources whose components are independent but non-identically distributed. Crucially, Gaussian sources with memory and quadratic distortion can be cast in this form. We show that under the operational constraint of exceeding distortion $d$ with probability at most $epsilon$, the minimum achievable rate at blocklength $n$ satisfies $R(n, d, epsilon)=mathbb{R}_n(d)+sqrt{frac{mathbb{V}_n(d)}{n}}Q^{-1}(epsilon)+O left(frac{log n}{n}right)$, where $Q^{-1}(cdot)$ is the inverse $Q$-function, while $mathbb{R}_n(d)$ and $mathbb{V}_n(d)$ are fundamental characteristics of […]

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arXiv:2602.09174v1 Announce Type: new Abstract: Processing large-scale graph datasets is computationally intensive and time-consuming. Processor-centric CPU and GPU architectures, commonly used for graph applications, often face bottlenecks caused by extensive data movement between the processor and memory units due to low data reuse. As a result, these applications are often memory-bound, limiting both performance and energy efficiency due to excessive data transfers. Processing-In-Memory (PIM) offers a promising approach to mitigate data movement bottlenecks by integrating computation directly within […]

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arXiv:2602.09173v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) shows that small, specialized language models (SLMs) can exhibit structured reasoning without relying on large monolithic LLMs. We introduce soft hidden-state collaboration, where multiple heterogeneous frozen SLM experts are integrated through their internal representations via a trainable attention interface. Experiments on Reasoning Gym and GSM8K show that this latent integration is competitive with strong single-model RLVR baselines. Ablations further reveal a dual mechanism of […]

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arXiv:2602.09169v1 Announce Type: new Abstract: Fully finetuning foundation language models (LMs) with billions of parameters is often impractical due to high computational costs, memory requirements, and the risk of overfitting. Although methods like low-rank adapters help address these challenges by adding small trainable modules to the frozen LM, they also increase memory usage and do not reduce inference latency. We uncover an intriguing phenomenon: sparsifying specific model rows and columns enables efficient task adaptation without requiring weight tuning. […]

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arXiv:2602.09165v1 Announce Type: new Abstract: Accurate interpretation and visual representation of complex prompts involving multiple objects, attributes, and spatial relationships is a critical challenge in text-to-image synthesis. Despite recent advancements in generating photorealistic outputs, current models often struggle with maintaining semantic fidelity and structural coherence when processing intricate textual inputs. We propose a novel approach that grounds text-to-image synthesis within the framework of scene graph structures, aiming to enhance the compositional abilities of existing models. Eventhough, prior approaches […]

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