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- 💥 Gate Square Event: #PostToWinCC 💥
Post original content on Gate Square related to Canton Network (CC) or its ongoing campaigns for a chance to share 3,334 CC rewards!
📅 Event Period:
Nov 10, 2025, 10:00 – Nov 17, 2025, 16:00 (UTC)
📌 Related Campaigns:
Launchpool: https://www.gate.com/announcements/article/48098
CandyDrop: https://www.gate.com/announcements/article/48092
Earn: https://www.gate.com/announcements/article/48119
📌 How to Participate:
1️⃣ Post original content about Canton (CC) or its campaigns on Gate Square.
2️⃣ Content must be at least 80 words.
3️⃣ Add the hashtag #PostTo - 🚀 #GateNewbieVillageEpisode6 ✖️ @discovery
💬 Your greatest enemy is your own emotions.
Share your trading journey | Discuss strategies | Grow with the Gate Family
⏰ Event Time: Nov 12 10:00 – Nov 19 16:00 UTC
How to Join:
1️⃣ Follow Gate_Square + @discovery
2️⃣ Post on Gate Square with the hashtag #GateNewbieVillageEpisode6
3️⃣ Share your trading experiences, insights, or growth stories
— The more genuine and insightful your post, the higher your chance to win!
🎁 Rewards
3 lucky participants → Gate X RedBull Cap + $20 Position Voucher
If delivery is unavailable, the prize will be replaced - 💥 Gate Square #ETHPricePredictionChallenge 💥
📈 What’s your prediction for ETH in future?
🎁 4 lucky users will share $40 Position Vouchers!
👉 How to participate:
1️⃣ Follow Gate_Square
2️⃣ Like this post
3️⃣ Comment with your prediction
🗓️ Deadline: November 17, 2025, 04:00 UTC
OpenAI Releases CoT Monitoring to Stop Malicious Behavior of Large Models
Golden Finance reported that OpenAI released the latest research, using CoT (chain of thought) monitoring, it can prevent malicious behaviors such as large models talking nonsense and hiding true intentions, and it is also one of the effective tools for supervising super models. OpenAI uses the newly released cutting-edge model o3-mini as the monitored object, and the weaker GPT-4o model as the monitor. The test environment is a coding task that requires the AI to implement functionality in the codebase to pass unit tests. The results showed that the CoT monitor performed well in detecting systematic “reward hacking” behavior, with a recall rate of up to 95%, far exceeding the 60% of behaviors that were only monitored.