• Home
  • Technology
  • Gaming
  • Entertainment
  • World & Business
  • Science
  • Sports
  • AI
HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
  • HomeTechnologyGamingEntertainmentWorld & BusinessScienceSportsAI
    • Home
    • Technology
    • Gaming
    • Entertainment
    • World & Business
    • Science
    • Sports
    • AI
    AI
    Announcement

    MINTEval benchmark for AI memory systems accepted to NeurIPS 2026

    MINTEval’s creators report that seven representative systems averaged 27.9% accuracy on its changing-context tasks.

    MB
    ES
    HA
    4 Sources, ,

    TLDR

    The MINTEval team says its benchmark was accepted to NeurIPS 2026. It tests how AI agents and memory systems handle changing information, including Wikipedia revisions and GitHub commits. The team reports that seven representative systems averaged 27.9% accuracy, with the best reaching 33.4%. It attributes the difficulties mainly to problems with retrieval and memory construction.

    Combined views

    1.3K

    4 Sources, first seen 4h ago

    Combined views

    1.3K

    4 Sources, first seen 4h ago

    44 likes
    4h ago
    first seen 4h ago
    44 likes
    1 comments
    3 saves
    16 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Featured Source
    1 comments
    3 saves
    16 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    Today's Rank

    —

    Not ranked yet

    Today's Rank

    —

    Not ranked yet

    4 Sources

    @hyunji_amy_leeHappy to share that MINTEval is accepted to #NeurIPS2026! 🥳 How well do LLM agents and memory systems handle long-horizon, continually changing environments (e.g., Wikipedia pages, Git repos)? We find they struggle to track changes, recall precise details, and combine information across interfering updates, mainly due to limitations in retrieval and memory construction. Models confuse old and new information, memory systems tend to insert redundant entries, and retrieval often fails to recover the right information. Details 👇
    @EliasEskinRT @hyunji_amy_lee: Happy to share that MINTEval is accepted to #NeurIPS2026! 🥳 How well do LLM agents and memory systems handle long-hori…
    @cyjustinchenSo happy to see MINTEval accepted to #NeurIPS2026! 🎉 Check out our benchmark and analysis on memory systems, where they must process long contexts and reason over many updates that create interference between old and new information! 👇
    @mohitban47RT @cyjustinchen: So happy to see MINTEval accepted to #NeurIPS2026! 🎉 Check out our benchmark and analysis on memory systems, where they m…

    4 Sources

    @hyunji_amy_leeHappy to share that MINTEval is accepted to #NeurIPS2026! 🥳 How well do LLM agents and memory systems handle long-horizon, continually changing environments (e.g., Wikipedia pages, Git repos)? We find they struggle to track changes, recall precise details, and combine information across interfering updates, mainly due to limitations in retrieval and memory construction. Models confuse old and new information, memory systems tend to insert redundant entries, and retrieval often fails to recover the right information. Details 👇
    @EliasEskinRT @hyunji_amy_lee: Happy to share that MINTEval is accepted to #NeurIPS2026! 🥳 How well do LLM agents and memory systems handle long-hori…
    @cyjustinchenSo happy to see MINTEval accepted to #NeurIPS2026! 🎉 Check out our benchmark and analysis on memory systems, where they must process long contexts and reason over many updates that create interference between old and new information! 👇
    @mohitban47RT @cyjustinchen: So happy to see MINTEval accepted to #NeurIPS2026! 🎉 Check out our benchmark and analysis on memory systems, where they m…