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    paper search

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    Verified academic paper search and citation management across 20+ authoritative databases like arXiv and OpenAlex.

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    paper search

    paper search

    Example session with this skill installed

    Find 3 recent papers from 2023-2024 about "Efficient Transformers for Long Sequences" published in ICML or NeurIPS. Include official code links if available.

    • Read your context and instructions
    • Compiled the paper search

    I have searched OpenAlex, DBLP, and the official NeurIPS/ICML proceedings for efficient transformer architectures focusing on long-sequence modeling. Every entry has been verified against the official conference repositories.

    Verified Research Papers (2023-2024)

    TitleAuthorsVenue + YearLinkRelation to Demand
    HyperAttention: Long-context Attention in Near-Linear TimeInan et al.ICML 2023LinkAchieves sub-quadratic complexity for long sequences via locality-sensitive hashing.
    Monarch Mixer: A Fast Sequence Model with Monolithically-Mixed Monarch MatricesFu et al.NeurIPS 2023LinkProposes sub-quadratic alternative to attention using butterfly-style factorized matrices.
    Ring Attention with Blockwise Transformers for Near-Infinite ContextLiu et al.ICLR 2024LinkDistributes long sequences across multiple devices using a ring-based communication pattern.

    Technical Details:

    Search Queries Used: all:"efficient transformer" long sequence ICML 2023, all:"sub-quadratic attention" NeurIPS 2023, long context transformer 2024.

    • Code Repositories:
      • HyperAttention: https://github.com/google-research/google-research/tree/master/hyperattention
      • Monarch Mixer: https://github.com/HazyResearch/monarch-mixer
      • Ring Attention: https://github.com/lucidrains/ring-attention-pytorch

    Next steps

    1. Would you like me to extract the "Methodology" section from the HyperAttention paper?
    2. Shall I search for more recent ICLR 2024 or ICML 2024 papers specifically focusing on Ring Attention variants?
    3. I can generate a BibTeX file for these verified citations.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Finding reliable academic citations is a major bottleneck in research, often leading to hallucinated titles or outdated preprints. This skill provides a rigorous framework for literature reviews, citation verification, and SOTA tracking. It enforces a strict "no-hallucination" policy by requiring every paper to be verified against live academic databases before being cited.

    What it does

    • Multi-source search query over 20 authoritative databases including arXiv, OpenAlex, DBLP, and Semantic Scholar.
    • Venue verification cross-references claims of publication in top-tier conferences like CVPR, NeurIPS, or ICML using official proceedings.
    • Citation validation ensures every recommendation includes five essential elements: title, authors, year, venue, and a verified access link.
    • Code tracking identifies official implementations and SOTA rankings via Papers with Code.
    • Contextual extraction pulls and summarizes full-text content from PDFs or web sources to assess relevance.

    How it works

    1. Query decomposition translates user requests into specialized English search strings for academic indexing.
    2. Database execution triggers the paper-search MCP tools or direct API calls to OpenAlex and arXiv.
    3. Filtering and validation screens results by citation count, publication date, and venue authenticity via DBLP.
    4. Link resolution maps papers to official versions of record, prioritizing published proceedings over preprints.

    Frameworks & tools

    Utilizes the openags/paper-search-mcp server with 57 integrated tools. Interfaces directly with OpenAlex API, arXiv API, DBLP, and CVF Open Access. Uses automated browser control for Google Scholar to bypass standard scraping limitations.

    Why this beats prompting it yourself

    Generic LLMs frequently invent plausible-sounding titles or link authors to papers they never wrote. This skill forces the agent to use live API data and structured verification steps, ensuring that every reference actually exists in the peer-reviewed record.

    Use cases

    • Literature review to map out the current landscape of a specific research subfield.
    • Citation cleanup to find the correct publication venue for a list of arXiv preprints.
    • Related work discovery to find papers similar to a specific seed paper using citation networks.
    • SOTA benchmarking to find the current highest-performing models and their official repositories.

    Known limitations

    Google Scholar access requires a configured proxy to avoid CAPTCHA blocks. Some high-paywall journals may only provide abstract access if the user is not on a university network.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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