The Year in AI Papers
The year/Labs/Moonshot AI

Research lab

Moonshot AI

Long-context, agentic, multimodal, and open foundation-model research from Moonshot AI.

Papers
6
Citations
477
Official code
6

6 papers from Moonshot AI

01

Research paper

PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models

PerceptionBench is a benchmark introduced by Moonshot AI to evaluate atomic visual perception in Multimodal Large Language Models (MLLMs). It addresses limitations of existing benchmarks that conflate perception with reasoning or knowledge. The benchmark was constructed bottom-up: failures of frontier MLLMs on 42 existing benchmarks were attributed to…

Zichao Lin, Yifeng Xie, Bowen Qu, Haiming Wang, et al.
Published
Jul 2026
Citations
0
Code
170 stars
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02

Research paper

Kimi K3: Open Frontier Intelligence

Kimi K3 is a 2.8-trillion-parameter Mixture-of-Experts model with 104 billion activated parameters, native vision, and a 1-million-token context window. It uses Kimi Delta Attention, Attention Residuals, and Stable LatentMoE (16 of 896 routed experts active per token), achieving about 2.5× scaling efficiency over Kimi K2. Post-training includes…

Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, et al.
Published
Jul 2026
Citations
2
Code
8.1K stars
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03

arXiv.org

Attention Residuals

The paper proposes Attention Residuals (AttnRes), replacing fixed unit-weight residual connections in LLMs with learned, input-dependent softmax attention over preceding layer outputs. This addresses PreNorm dilution, where hidden-state magnitudes grow with depth, diluting layer contributions. A scalable variant, Block AttnRes, partitions layers into…

Kimi Team, Guangyu Chen, Yu Zhang, Jianlin Su, et al.
Published
Mar 2026
Citations
42
Code
3.5K stars
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04

Research paper

Kimi K2.5: Visual Agentic Intelligence

Kimi K2.5 is an open-source multimodal agentic model that jointly optimizes text and vision through techniques including joint pre-training, zero-vision SFT, and joint reinforcement learning. It introduces Agent Swarm, a parallel agent orchestration framework using Parallel-Agent Reinforcement Learning (PARL) to decompose tasks into heterogeneous…

Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, et al.
Published
Feb 2026
Citations
313
Code
2.3K stars
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05

arXiv.org

WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models

WorldVQA is a benchmark introduced to evaluate the atomic visual world knowledge of Multimodal Large Language Models (MLLMs), decoupling visual knowledge retrieval from reasoning. It comprises 3,500 VQA pairs across nine semantic categories, from common head-class entities to long-tail rarities. The benchmark follows four design principles: atomic…

Runjie Zhou, Youbo Shao, Haoyu Lu, Bowei Xing, et al.
Published
Jan 2026
Citations
4
Code
121 stars
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06

arXiv.org

Kimi Linear: An Expressive, Efficient Attention Architecture

Kimi Linear is a hybrid linear attention architecture that outperforms full attention under fair comparisons across short-context, long-context, and reinforcement learning (RL) scaling regimes. Its core, Kimi Delta Attention (KDA), extends Gated DeltaNet with a finer-grained, channel-wise gating mechanism, improving memory control and enabling efficient…

Kimi Team, Yu Zhang, Zongyu Lin, Xingcheng Yao, et al.
Published
Oct 2025
Citations
116
Code
1.6K stars
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