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Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities

large-language-models › evaluation

📄 Abstract

Abstract: As model context lengths continue to grow, concerns about whether models effectively use the full context length have persisted. While several carefully designed long-context evaluations have recently been released, these evaluations tend to rely on retrieval from one or more sections of the context, which allows nearly all of the context tokens to be disregarded as noise. This represents only one type of task that might be performed with long context. We introduce Oolong, a benchmark of long-context reasoning tasks that require analyzing individual chunks of text on an atomic level, and then aggregating these analyses to answer distributional questions. Oolong is separated into two task sets: Oolong-synth, a set of naturalistic synthetic tasks, where we can easily ablate components of the reasoning problem; and Oolong-real, a downstream setting which requires reasoning over real-world conversational data. Oolong requires models to reason over large quantities of examples, to perform both classification and counting in-context, and to reason over temporal and user relations. Even frontier models struggle on Oolong, with GPT-5, Claude-Sonnet-4, and Gemini-2.5-Pro all achieving less than 50% accuracy on both splits at 128K. We release the data and evaluation harness for Oolong to enable further development of models that can reason over large quantities of text.

Key Contributions

Introduces Oolong, a benchmark designed to evaluate the long-context reasoning and aggregation capabilities of LLMs. Oolong focuses on tasks requiring atomic-level analysis and aggregation of information, distinguishing itself from benchmarks that primarily rely on retrieval, and includes both synthetic and real-world task sets.

Business Value

Enables the development of more capable LLMs that can process and reason over extensive documents, leading to better summarization, analysis, and question-answering systems for complex information.

Paper Metadata

Innovation Type

Benchmark and Evaluation Methodology

Deployment Feasibility

The benchmark is a research tool for evaluation, not a deployable system. Its results guide the development of deployable models.

Limitations Addressed

Existing long-context evaluations often focus on retrieval, which may not fully assess a model's ability to reason over and aggregate information distributed across a long context.

Performance Gains

Provides a standardized way to measure and compare LLM performance on specific long-context reasoning and aggregation tasks.

Technical Tags

long context reasoningaggregation capabilitiesbenchmarkdistributional questionssynthetic tasksreal-world dataatomic level analysiscontext length

Research Topics

Large Language ModelsContext Window ManagementReasoning CapabilitiesBenchmark DesignNatural Language Understanding

Methods & Architectures

Oolong BenchmarkSynthetic Task GenerationReal-world Data EvaluationAtomic Level AnalysisAggregation Tasks Large Language Models (LLMs)

Applications & Tasks

Natural Language Processing AI Research Machine Learning Evaluation Evaluating effective use of full context length in LLMsAssessing reasoning and aggregation over long contextsDeveloping benchmarks that go beyond simple retrieval Reasoning over large quantities of examplesAggregating information from distributed text chunksAnswering distributional questions

Datasets & Benchmarks

Datasets

Oolong-synth, Oolong-real

Performance on reasoning tasksAggregation accuracyAbility to handle long contexts

Related Fields

Natural Language ProcessingMachine LearningArtificial IntelligenceEvaluation Metrics

Keywords

Long ContextLLM EvaluationReasoningAggregationBenchmarkOolongContext LengthNatural Language ProcessingSynthetic DataReal-world DataDistributional QuestionsAI Performance

Academic Context

#Large Language Models#Context Window Management#Reasoning Capabilities#Benchmark Design#Natural Language Understanding

Commercial Potential

Target Industries

TechnologyAI Research

Use Case Examples

Evaluating LLMs for summarizing lengthy legal documentsAssessing AI's ability to analyze large codebasesTesting models for understanding extensive research papers

Competitive Edge

Offers a novel benchmark specifically targeting complex reasoning and aggregation over long contexts, complementing existing evaluations.

Market Opportunity

Growing market for advanced LLM evaluation tools.

Resource Requirements

Compute Needs

Requires significant computational resources to run LLMs through the Oolong benchmark tasks.

Data Requirements

Requires the Oolong benchmark datasets (Oolong-synth and Oolong-real).

Deployment Constraints

The benchmark is for evaluation, not deployment. Models evaluated may have specific deployment requirements based on their context length capabilities.

Scalability

The benchmark is designed to test models with varying context lengths.

Production Readiness

Maturity Level

Evaluation Benchmark

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