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Tuesday

  8. Shao Meng

    MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems

    Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.

    Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the

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    #hot-signal#Tutorials / practice#Agents#English text is being prepared.

Monday

  2. Tencent Cloud Developer Community

    SpringAI Customer Service Security Compliance in Practice: Sensitive Word Filtering, PII Masking, and Conversation Auditing

    Based on SpringBoot 3.3.x and SpringAI 1.0.x, this article presents a security and compliance solution for launching AI customer service in financial and government/enterprise scenarios: using the DFA algorithm for bidirectional sensitive word filtering, with a 100,000-word lexicon matched within 1ms; using regular expressions to mask phone numbers, ID card numbers, bank card numbers, and email addresses before calling Hunyuan; storing audit logs independently in Tencent Cloud CDB, allowing only INSERT, retaining them for 5 years, and keeping both original and masked records. The solution is designed with reference to Level 3 of the Multi-Level Protection Scheme and the Personal Information Protection Law.

    Based on SpringBoot 3.3.x and SpringAI 1.0.x, this article presents a security and compliance solution for launching AI customer service in financial and government/enterprise scenarios: using the DFA algorithm for bidirectional sensitive word filtering, with a 100,000-word lexicon matched within 1ms; using regular expressions to mask phone numbers, ID card numbers, bank card numbers, and email addresses before calling Hunyuan; storing audit logs independently in Tencent Cloud CDB, allowing only

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    #hot-signal#Tutorials / practice#Safety / alignment#RAG