The release of the ROMA framework marks an important turning point for the open-source AI intelligent agent ecosystem. From a data perspective, @ SentientAGI ROMA has indeed performed well in several key benchmark tests: Achieved a score of 45.6% in the SEAL-0 benchmark test, surpassing all existing closed source platforms The FRAMES benchmark reaches 81.7% The SimpleQA benchmark has reached 93.9% These data mean that for the first time, open-source frameworks have truly surpassed commercial products such as ChatGPT, Perplexity, and Gemini in complex inference tasks. The core logic of ROMA is very clear The system processes complex tasks through recursive decomposition: one ️⃣ Atomizer determines whether the task needs to be disassembled two ️⃣ Planner is responsible for dismantling molecular tasks three ️⃣ Executor executes specific tools and agents four ️⃣ Aggregator collects and synthesizes results Each node follows the same recursive logic, which allows ROMA to naturally extend into deep task trees. Independent subtasks can run in parallel and can also be manually checked at any node. Meanwhile, ROMA exposes the complete contextual process through stage tracing, making the entire system debuggable and iterable. Addressing the core pain points of long-term tasks The reliability of single step tasks is 99%, and they will crash after 10 steps. Errors will compound between steps, and the success rate will sharply decrease. ROMA makes long-term tasks reliable through structured reasoning and transparent processes. A completely open-source framework. For users, any model can be implanted, any data source can be connected, and custom AI agents can be built. For builders, ROMA makes multi-agent systems modular and scalable. For researchers, this is a transparent research platform. From the perspective of @ SentientAGI, what they are doing is providing infrastructure for the entire open-source AI community. Enable everyone to build commercial grade multi-agent systems without the need to build wheels from scratch. This' open source first 'strategy is worth paying attention to!