Papers with Code

Business Profile

Core Value Proposition

Papers with Code provides trending AI research papers with direct links to code (GitHub) and related resources, plus email updates for staying current.

Target Customer

Researchers, ML engineers, data scientists, academics, students, and developers seeking to discover and access AI papers and their implementations

Key Differentiator

Centralized hub for AI papers with code, community-driven trending updates, and easy access to arXiv pages and GitHub repositories; includes email subscription options for daily/weekly/monthly trends

Implementation Timeline

Not specified in the content; publication dates range from mid-2025 to late-2025 for the papers listed

Pain Points

1Difficulty finding cutting-edge AI research with accessible, corresponding code
2Fragmented discovery environment across papers, code, datasets, and models
3Keeping up with rapidly evolving AI research and trends

Key Features

1Trending Papers feed (updated by the research community and contributors)
2Email subscription options: daily, weekly, monthly
3Links to arXiv pages and GitHub repositories for papers
4Header navigation suggesting models, datasets, spaces, community, docs, and pricing

Target Customer Industries

Client Results

1MinerU2.5: claims state-of-the-art recognition accuracy with significantly lower computational overhead due to a coarse-to-fine two-stage parsing approach and a comprehensive data engine
2WebWatcher: outperforms proprietary baselines, RAG workflows, and open-source agents in four challenging VQA benchmarks
3WebShaper: reports state-of-the-art performance among open-sourced information-seeking agents on GAIA and WebWalkerQA benchmarks
4WebDancer: demonstrates strong performance on GAIA and WebWalkerQA benchmarks within a four-stage training paradigm
5ReSum: shows 4.5% absolute improvement over ReAct with up to 8.2% gains after ReSum-GRPO training; capable of improved performance with limited training data
6WebSailor-V2: substantially outperforms open-source agents in complex information-seeking tasks, matching proprietary agent performance
7WebSailor (general): outperforms all open-source web agents on complex information-seeking benchmarks
8CapRL: capRL demonstrates improved captioning capabilities and achieves competitive performance relative to large VL models; CapRL-5M pretraining yields gains across benchmarks
9Paper2Agent: converts research papers into AI agents to accelerate downstream use, adoption, and discovery

Competitive Advantages

1Centralized access to trending AI research with direct code links (GitHub) and publication pages (arXiv)
2Community-driven curation and upvoting signals to surface impactful work
3Email-based updates deliver ongoing, convenient discovery of new papers
4Direct exposure to state-of-the-art results reported in the papers themselves

Key Benefits

1Discover trending AI research quickly

2Access code implementations and related resources directly from papers

3Receive curated updates via email (daily/weekly/monthly)

Additional Product Information

Product Description

A platform that aggregates AI research papers and links to their code implementations, offering trending papers, author contributions, and direct access to related resources (arXiv, GitHub) with optional email updates.

Target Market

Researchers, ML practitioners, academics, students, and developers who need to discover and access AI papers and their implementations

Unique Value Proposition

A centralized, community-driven hub that connects AI research papers to their implementations and resources, with real-time trend insights and easy discovery via arXiv and GitHub links.

Technical Requirements

Not specified in the content

Pricing Model

Not specified in the content; the header mentions 'Enterprise Pricing' but no details are provided

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