[HN Gopher] Owl: Optimized Workforce Learning for multi-agent co...
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       Owl: Optimized Workforce Learning for multi-agent collaboration
        
       Author : simonpure
       Score  : 21 points
       Date   : 2025-03-11 12:14 UTC (3 days ago)
        
 (HTM) web link (github.com)
 (TXT) w3m dump (github.com)
        
       | bob1029 wrote:
       | Looking at the linked paper:
       | 
       | > Our proposed framework is a novel role-playing approach for
       | studying multiple communicative agents. Specifically, we
       | concentrate on task-oriented role-playing that involves one AI
       | assistant and one AI user. After the multi-agent system receives
       | a preliminary idea and the role assignment from human users, a
       | task-specifier agent will provide a detailed description to make
       | the idea specific. Afterwards, the AI assistant and AI user will
       | cooperate on completing the specified task through multi-turn
       | conversations until the AI user determines the task is done. The
       | AI user is responsible for giving instructions to the AI
       | assistant and directing the conversation toward task completion.
       | On the other hand, the AI assistant is designed to follow the
       | instructions from the AI user and respond with specific
       | solutions. (below Figure 1)
       | 
       | This appears to be another perpetual information machine. You
       | really need to have a human in the loop at some level. I agree
       | that you can go very far with a good initial prompt, but once you
       | hit the first ambiguity you need an external signal to correct.
       | This stuff goes sideways quickly with bad assumptions.
       | 
       | The best I've been able to achieve with "multi-agent" is to
       | recursively invoke prompts and pass a summary of the prior
       | context + request each time. The prompts are effectively agents,
       | each with a goal to narrow and re-focus context as the task
       | progresses through the tool call stack. I have never seen
       | multiple agents talking to each other autonomously evolve into
       | anything the business would care about.
        
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       (page generated 2025-03-14 23:00 UTC)