DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for general-domain, factoid-style atomic claims, and there is no benchmark to test whether such verifiers transfer to DRRs. Yet building such a benchmark is itself difficult. We first show that static expert-labeled benchmarks are brittle in this setting: in a controlled study with PhD-level specialists, unassisted experts achieve only 60.8% accuracy on a hidden micro-gold set of verifiable claims. We propose Evolving Benchmarking via Audit-then-Score (AtS), where benchmark labels and rationales are explicitly revisab...
| Authors | Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov et al. |
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| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 3 April 2026 |
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