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AI is dramatically lowering the cost of reading papers, writing code, and running experiments. But as research output explodes, telling which results can be trusted keeps getting harder. CiteArk connects the claims in scientific papers with their experimental conditions, execution process, result evidence, and verification records, so researchers and agents can verify claims, reuse experimental results, and build follow-up research on them instead of repeating work. We hope these trustworthy research records will grow into a scientific foundation that researchers, labs, and agents can all build on together.

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Search papers, claims, runs, and users/

Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration · CiteArk

Recent activity

—Starting the worker
—Existing research plan reused
—Orchestrating training, evaluation and analysis
—Analysis environment started
—Execution needs attention

Credit estimate for this reproduction

Expected usage

549

Maximum reservation

1683

Estimated compute time

About 210 min

Actual usage was 0 credits; 0 credits were charged. Compute ran for about 0 minutes and the model used 0 tokens.

Failed attempts also used about 14 minutes of compute and 7142207 model tokens. They produced no importable result and were not billed.

Finding Where the Buck Stops: An Automated Failure Attribution-Based Reflection Framework for Multi-Agent Collaboration

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Authors:Xiaoqing Wang, Keman Huang, Bin Liang, Hongyu Li, Xiaoyong Du, Wuqiong Pan

EMNLP 2026 arXiv 2026 Artificial Intelligence Computation and Language Machine Learning (stat.ML)

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19 files · 1.3 MB

CCiteArk Research CompilerCompilation AgentImport verified CAP 1.0 research plan—Current version

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README.md1.9 KB

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