Catalog Systems Memory benchmarks & evaluation GAIA

GAIA

https://huggingface.co/gaia-benchmark

Benchmark for general AI assistants made of real-world questions that require reasoning, multimodality, web browsing and tool use, with a public leaderboard on Hugging Face. Traction: ~917 likes on the Hugging Face dataset

Active · Verified 2026-10-29 · fresh as of 2026-10-29 · 6 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type
Benchmark vendor fix
Tier
T3
Section
Memory benchmarks & evaluation
Created
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Latest release
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License
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GitHub
not applicable — no GitHub repo fix
Pricing
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Funding
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Freshness
2026-10-29 fix
Layer
agent-infra repo fix

Memory taxonomy

storage
n/a
retrieval
n/a
persistence
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update
read-only
unit
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governance
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conflict
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When to use

Optimised for: searched not found

Anti-fit: searched not found

Pros & cons

Pros

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Cons

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Claims & capabilities

Abstract: We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at this https URL .

Technical surface

API surface
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Backend storage
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Deployment
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Embedding model
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Multi-tenancy
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MCP
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A2A
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OpenTelemetry
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Similar systems

Other memory benchmarks & evaluation in the catalog, ranked by inbound references.

  • LoCoMo T3

    Conversations spanning up to 35 sessions / 300 turns / ~9K tokens, built from LLM-agent personas grounded on temporal event graphs and verified by human annotators. Tests QA (single-hop, multi-hop, temporal, commonsense, adversarial), event-graph summarisation, multimodal dialogue.

  • LongBench T3

    Tsinghua THUDM. 21 datasets across 6 categories (single-doc QA, multi-doc QA, summarisation, few-shot ICL, synthetic, code completion) in English + Chinese. ~6.7K words English / 13.4K chars Chinese average. v2 at ACL 2025 with harder, longer instances.

  • LongMemEval T3

    5 core memory abilities — info extraction, multi-session reasoning, temporal reasoning, knowledge updates, abstention — via 500 curated questions in synthetic chat histories. Two scales: LongMemEval-S (115K tokens), LongMemEval-M (up to 1.5M).

  • RULER T3

    NVIDIA. Extends NIAH with multi-needle retrieval, multi-hop entity tracing, aggregation, QA — configurable for arbitrary length / difficulty. Evaluated 17 long-context models on 13 tasks.

  • InfiniteBench (∞Bench) T3

    Tsinghua / OpenBMB. First benchmark with average input >100K tokens. 12 tasks across English + Chinese: long novel QA, code debugging, math in long context, summarisation, retrieval. Realistic documents (novels, code) rather than synthetic filler.

  • Mind2Web T3

    Dataset and benchmark for generalist web agents with 2,350 language-instruction tasks on 137 real websites across 31 domains.

Sources (5 cited facts, 0 third-party)

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What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-30 Added to the catalog #347

Related systems

Referenced by (5)