most citedApple Intelligence Foundation Language Models

4 citations · 4 across the 3 of their papers we have counts for

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cs.AI2026

MindMemOS: A Portable and Self-Evolving Memory Operating Layer for AI Agents

Kaichao Liang, Yuqi Cui, Hao Kong +13

Memory is a core component of AI agents, enabling them to accumulate experience, maintain personalization, and adapt over long-term interactions. However, existing memory systems o…

cs.AI20264 cited

Apple Intelligence Foundation Language Models

Tom Gunter, Zirui Wang, Chong Wang +152

We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…

cs.AI2026

Hilbert: Recursively Building Formal Proofs with Informal Reasoning

Sumanth Varambally, Thomas Voice, Yanchao Sun +3

Large Language Models (LLMs) demonstrate impressive mathematical reasoning abilities, but their solutions frequently contain errors that cannot be automatically checked. Formal the…

cs.AI2026

ReThinker: Scientific Reasoning by Rethinking with Guided Reflection and Confidence Control

Zhentao Tang, Yuqi Cui, Shixiong Kai +10

Expert-level scientific reasoning remains challenging for large language models, particularly on benchmarks such as Humanity's Last Exam (HLE), where rigid tool pipelines, brittle…

cs.AI2026

From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design

Xufei Tian, Wenli Du, Shaoyi Yang +4

Process simulation is a critical cornerstone of chemical engineering design. Current automated chemical design methodologies focus mainly on various representations of process flow…