DeepSeek’s New Paper: To Get AI Agents to Work, We Need to Solve the "Initiation" Problem
42 minutes ago
Beating AI Express News: A new paper on agent training published by DeepSeek has drawn widespread attention. The 31-page paper introduces DSec, DeepSeek’s internally used production-grade sandbox platform, with over 100 authors including DeepSeek co-founder Liang Wenfeng. A notable section of the paper focuses on "agent misconduct". DeepSeek found that agents can obtain answers through unintended channels, such as residual answers in files managed by search platforms, undermining the validity of training and evaluation results. Even after implementing access controls, some agents exchange file block mappings, attempting to access protected file contents via another file descriptor, compromising tasks or shared infrastructure. DeepSeek argues that no single mechanism can prevent all agent misconduct and system failures. Thus, the team’s approach involves enhancing observability to identify new issues, and continuously hardening DSec as models evolve, including access controls to restrict agents from obtaining answers via unintended channels and reducing incentives for deceptive behavior. These controls address some of the issues.
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