Deep Cogito推出了一系列名为Cogito v2的开源AI模型，其核心亮点在于模型能够自主“打磨”自身的推理技能。该系列包含不同参数规模的模型，其中671B的混合专家（MoE）模型被认为是目前最强大的开源AI之一，能与顶尖闭源模型媲美。Cogito v2采用“迭代蒸馏与增强”（IDA）技术，使模型能将搜索过程中的发现内化到核心参数中，从而形成更强的“直觉”，大幅缩短推理路径。这种高效的内部优化不仅减少了计算资源消耗，也降低了训练成本。此外，该模型还展现了在未明确训练领域（如图像推理）的惊人能力，预示着其在多模态推理领域的巨大潜力。Deep Cogito承诺将持续以开源模式推动AI的自我完善与发展。

🚀 **自主推理能力提升**：Cogito v2系列模型通过“迭代蒸馏与增强”（IDA）技术，实现了AI模型自主优化推理过程。模型不再仅仅依赖于在推理时延长计算时间来寻找答案，而是将搜索和发现过程内化为模型本身的“直觉”，从而提升了推理效率和准确性。

💡 **高效的推理路径**：得益于内化的推理能力，Cogito v2模型的推理链比同类竞品（如Deepseek R1）短60%，这意味着在解决问题时，模型能够更直接、更有效地找到解决方案，避免了不必要的“迂回”。

💰 **成本效益显著**：Deep Cogito成功地将整个模型的开发（从实验到最终训练）成本控制在350万美元以下，这对于AI模型而言是一笔相对较小的开销，尤其与许多领先AI实验室的巨额投入相比，显示了其在成本控制和效率上的优势。

🖼️ **跨领域推理能力**：一个令人惊喜的发现是，Cogito v2模型在未经过明确图像推理训练的情况下，也能对图像进行深入分析，例如比较两张图片的差异并分析其栖息地、颜色和构图。这为未来多模态推理系统的训练提供了新的思路和可能性。

🌐 **坚持开源承诺**：Deep Cogito重申了其将所有AI模型保持开源的承诺，并计划继续通过“迭代自我改进”来追求通用人工智能（AGI）的实现。这一举措有助于推动整个AI社区的进步和创新。

Deep Cogito has released Cogito v2, a new family of open-source AI models that sharpen their own reasoning skills.

Released under an open-source licence, the new Cogito v2 lineup includes four hybrid reasoning AI models: two mid-sized at 70B and 109B parameters, and two large-scale versions at 405B and 671B. 

The largest, a 671B Mixture-of-Experts (MoE) model, is already being touted as one of the most powerful open-source AIs in the world. The company reports that it competes with the latest from DeepSeek and is closing the gap on proprietary systems like O3 and Claude 4 Opus.

But the real story isn’t just about size or power; it’s about a fundamental shift in how the AI learns. Instead of just ‘thinking’ longer at inference time to find an answer, Cogito v2 is designed to internalise its own reasoning processes.

This internalised reasoning is achieved through a technique called Iterated Distillation and Amplification (IDA), which distils the discoveries from a search back into the model’s core parameters. The goal is to build a stronger ‘intuition’, allowing the model to anticipate the outcome of its own reasoning without having to perform the entire search.

Because the open-source AI models have a better “gut feeling” for the right approach, their reasoning chains are 60% shorter than those of rivals like Deepseek R1.

This efficiency extends to the budget. Deep Cogito says that it developed all its models – from experiments to final training – for a combined total of less than $3.5 million. Still a large sum likely for you or I, but miniscule compared to the spending of many of the leading AI labs.

The flagship 671B model received special attention, trained not only to improve its final answers but to refine the thinking process itself. This approach discourages the model from “meandering” and rewards a more direct path to the solution. The performance data suggests it works, with Deep Cogito’s open-source AI model matching or exceeding the latest DeepSeek versions on key benchmarks while being close to proprietary alternatives:

Perhaps one of the most surprising outcomes is the models’ ability to reason about images; a skill they were never explicitly trained for.

The team shared an example of this reasoning where Deep Cogito’s open-source AI model compared two images of a duck and a lion, demonstrating a deep thinking process about their habitats, colours, and composition purely through transfer learning. Deep Cogito believes this emergent property could be a powerful way to bootstrap training data for future multimodal reasoning systems.

Looking ahead, the Deep Cogito team plans to “hill climb on the gains of iterative self-improvement” in its quest to build superintelligence. They have restated their commitment that all AI models they create will be open-source.

See also: Leak suggests OpenAI’s open-source AI model release is imminent

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