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20102026
most citedDeep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

1.7k citations · 2.1k across the 35 of their papers we have counts for

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20 papers · 1 filter

cs.CL2026

SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge

Lucio M. Dery, Benedict Aaron Tjandra, Siavash Samiei +4

Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-based knowledge and procedures…

cs.CL2026

Context Training with Active Information Seeking

Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng +4

Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent wor…

cs.CL2026

Decoupled DiLoCo for Resilient Distributed Pre-training

Arthur Douillard, Keith Rush, Yani Donchev +14

Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across accelerators. Due to this co…

cs.CL202549 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2025

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch

Arthur Douillard, Yanislav Donchev, Keith Rush +11

Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need…

cs.CL20239 cited

Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models

Jimmy Wei, Kurt Shuster, Arthur Szlam +3

Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this wo…