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20242026
most citedDeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

8 citations · 15 across the 12 of their papers we have counts for

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cs.CL2026

LatentOmni: Rethinking Omni-Modal Understanding via Unified Audio-Visual Latent Reasoning

Yifan Dai, Zhenhua Wu, Bohan Zeng +18

Joint audio-visual reasoning is essential for omnimodal understanding, yet current multimodal large language models (MLLMs) still struggle when reasoning requires fine-grained evid…

cs.CL2026

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

DeepSeek-AI, Anyi Xu, Bangcai Lin +315

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…

cs.CL2026

Let's Verify Math Questions Step by Step

Chengyu Shen, Zhen Hao Wong, Runming He +8

Large Language Models (LLMs) have recently achieved remarkable progress in mathematical reasoning. To enable such capabilities, many existing works distill strong reasoning models…

cs.CL2026

Canvas-of-Thought: Grounding Reasoning via Mutable Structured States

Lingzhuang Sun, Yuxia Zhu, Ruitong Liu +10

While Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), relying solely on linear text sequences re…

cs.CL20258 cited

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

DeepSeek-AI, Aixin Liu, Aoxue Mei +260

We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. The key technical breakthroughs of DeepSeek-V3.2 ar…

cs.CL2025

QAEncoder: Towards Aligned Representation Learning in Question Answering Systems

Zhengren Wang, Qinhan Yu, Shida Wei +6

Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders…