Unverified50% confidenceFactExact time
An empirical study conducted hardware-level energy measurements on all 1000 EffiBench problems across 11 open-source models and 4 decoding configurations, finding that 42 of the 44 configurations generated code that was more energy-consuming than the human reference implementation.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCodex使用了126个子智能体、32500次工具调用、391次浏览器测试和2300多次单元测试；Claude Code使用35个子智能体、102次浏览器测试和296次单元测试70% similarUnverifiedDevelopers should use their team's real tickets and codebase to run small-scale controlled tests themselves, set the same constraints, and observe each tool's first-pass rate, retry count, and total time consumed, which is more valuable as a reference than any public benchmark.69% similarUnverifiedThe research team tested nine proprietary and open-weight models across five coding agent frameworks, and concluded that even the strongest agents could not fully solve the task of digitizing protocols.65% similarUnverifiedThe sampler implementation given in the article is about forty lines of code, a proposal based on run_id hashing, reading SALT from environment variables, and covering three storage modes, and has not been benchmarked against production traffic.65% similarUnverifiedThe paper's controlled experiments cover 5 foundation models, 5 base classifiers and 3 scales, and the results show that the gains hold across different encoders, with larger improvements observed at smaller scales.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098279API
curl https://kongchang.com/api/v1/knowledge/claims/1098279MCP
get_claim(id=1098279)