AutoTrustAI/PaperGuru-Benchmark resource

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Adoption not part of the score

1324 stars · 196 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PaperGuru is a benchmark and research repository for Lifecycle-Aware Memory (LAM), a long-term memory primitive for long-horizon LLM agents, reporting state-of-the-art results on PaperBench (66.05%) and SurveyBench (94.66%). The repository contains benchmark materials, papers, and figure-reproduction assets written largely in TeX.

Use cases

• evaluate long-term memory systems for LLM agents
• benchmark agents on long-horizon research tasks like paper reproduction
• compare memory architectures against PaperBench and SurveyBench baselines
• research lifecycle-aware memory for multi-step agent workflows
• reproduce figures and results from the PaperGuru papers
• study agent performance on academic survey generation

When to choose

• you need rigorous benchmarks for long-horizon LLM agent memory
• you are researching memory primitives for multi-step agents
• you want to compare your agent against published PaperBench/SurveyBench results

When to avoid

• you need a production-ready memory library or SDK to drop into your app
• you want a simple plug-and-play vector store or RAG tool
• you need a permissively licensed codebase (license is unclear/NOASSERTION)

Facets

dataset · maturity active

benchmarking rag llm-inference agent-framework data-science artificial-intelligence large-language-models education python cross-platform long-horizon-agents lifecycle-aware-memory paperbench surveybench academic-research evaluation-benchmark memory-systems ai-agents retrieval-augmented-generation research

Member repositories

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