Expired80% confidenceFactTime unknown
Using context files caused the Agent to perform more exploration, testing, and reasoning, increasing costs by over 20%.
1
Sources
80%
Confidence
Medium-term (~90 days)
Relevance
6/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 8/31/2026(expired)
Sources
Related Entities
Related Claims
UnverifiedThe research team measured inference and training speed improvements of more than 20% on a billion-parameter-scale sparse LLM.66% similarUnverifiedA typical deep research Agent task involves 30–50 independent model inference calls, with cumulative token consumption easily exceeding 100,000.66% similarUnverified有研究表明，针对同一任务精心设计的上下文可将任务完成率提升40%以上65% similarUnverified研究显示复杂代理任务的token消耗往往是等效单次对话的20-100倍65% similarUnverifiedResearch shows that high-quality prompts can bring a 20%–40% improvement in task completion rates compared to casually written prompts under the same model conditions.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/38031API
curl https://kongchang.com/api/v1/knowledge/claims/38031MCP
get_claim(id=38031)