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20232026
most citedDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

978 citations · 1.4k across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

DeepSeek-AI, :, Anyi Xu +585

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…

cs.CL2025★ 8 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★ 5 cited

FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging

Zichen Tang, Haihong E, Ziyan Ma +10

We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compare…

cs.CL2025★ 978 cited

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

DeepSeek-AI, Daya Guo, Dejian Yang +195

General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-tho…

cs.CL2024★ 268 cited

DeepSeek-V3 Technical Report

DeepSeek-AI, Aixin Liu, Bei Feng +195

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effec…

cs.CL2024★ 95 cited

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

DeepSeek-AI, :, Xiao Bi +85

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions,…

cs.CL2023★ 15 cited

An Interdisciplinary Outlook on Large Language Models for Scientific Research

James Boyko, Joseph Cohen, Nathan Fox +9

In this paper, we describe the capabilities and constraints of Large Language Models (LLMs) within disparate academic disciplines, aiming to delineate their strengths and limitatio…