Unverified50% confidenceBenchmarkExact time
Across five consecutive policy version iterations, the laboratory planning score improved from 21.80% to 46.01%
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedExperiments by the Long-Term team showed that using the same model with only adjustments to rule constraints and the memory system raised the score from 52.8% to 66.5%, jumping the ranking from Top 30 to Top 5.73% similarUnverified6–8 steps still saves 60–70% of inference time compared to 20 steps, and the quality improvement over pure 4-step inference is often significant.69% similarUnverifiedOn the Natural Plan benchmark without task-specific training, the diffusion model's plan repair success rate was 53.7%, while the autoregressive model's was 27.0%.68% similarUnverifiedIn providing correct, domain-specific justifications, performance across different models ranged from 25.6% all the way to 76.9%.67% similarUnverifiedResearch shows that, for the same task, an optimized prompt can improve model output quality by 30%–50% compared to arbitrary input.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1002013API
curl https://kongchang.com/api/v1/knowledge/claims/1002013MCP
get_claim(id=1002013)