Unverified50% confidenceBenchmarkExact time
On a test of 300 tutorial questions, Paper2Agent achieved 91.2% accuracy, higher than the 80.3% achieved when a chat Agent directly accesses the corresponding code repository.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/20/2026
First Seen
Valid until: 1/18/2027
Sources
Related Entities
Related Claims
UnverifiedFor 26 data- and discovery-oriented papers, Paper2Agent's resource layer answered 100 comprehensive questions with 89% accuracy, higher than the 82% baseline of browsing the web.75% similarUnverifiedThe Intelligent Chemical Industry Large Model 3.0 Pro achieved a text Q&A accuracy rate of 81.96% and a multimodal Q&A accuracy rate of 80.75%, representing improvements of 20.2% and 31.4% respectively in overall scores compared to the previous generation.70% similarUnverifiedOn 42 tasks from 10 non-biological-computing papers, Paper2Agent achieved 98.1% accuracy.69% similarUnverifiedFor comparison, the Program-of-Thought baseline achieves 72.0% overall accuracy and the tool-calling agent achieves 58.7%.68% similarUnverifiedAfter 15 training epochs on the demonstration dataset, the text classification model achieves an accuracy of 99%, with a total of 110,000 model parameters (16,000 for Attention, 33,000 for FFN, and the remainder for Embedding)68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1077719API
curl https://kongchang.com/api/v1/knowledge/claims/1077719MCP
get_claim(id=1077719)