Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research

由 Google DeepMind 校友创立的伦敦人工智能实验室 Inherent 表示，其 AI 智能体仅用极小的模型规模，就击败了来自 Anthropic 和 OpenAI 的大型模型。在所有由 Google DeepMind 校友创办的初创公司中，Inherent 受到的关注相对较少。但当资金更雄厚的竞争对手尚未向世界展示任何具体成果时，这支位于伦敦的团队已经开始分享他们的研发进展。

Inherent, a London AI lab founded by Google DeepMind alumni, says its AI agent just outperformed much larger models from Anthropic and OpenAI using a fraction of the size. Of all the startups launched by Google DeepMind alumni, Inherent has gotten relatively little attention. But while better-funded rivals have yet to show the world anything concrete, the London-based team is starting to share what it’s been building.

在结束隐身状态并获得 5000 万美元种子轮融资仅几周后，这家英国初创公司表示，其新发布的 AI 智能体 Faraday 在一项特定任务中表现优于规模更大、更知名的模型：即在不预先告知答案的情况下，独立复现已发表科学论文的研究结果。

Just weeks after emerging from stealth with a $50 million seed round, the British startup says its newly released AI agent, Faraday, has outperformed larger, better-known models at a specific task: independently reproducing the findings of published scientific papers without being told the answer in advance.

考虑到 Inherent 更宏大的目标——构建能够发现新科学知识而不仅仅是验证旧结果的 AI，这听起来可能只是个“小把戏”。但联合创始人兼首席科学家 Edward Hughes 表示，论文复现对于人类科学家来说也是一项标准的训练练习。“许多博士生实际上就是从做这件事开始的。”

That may sound like a mere party trick given Inherent’s much loftier goal — building AI that can discover new scientific knowledge and not just verify old results. But paper replication is a standard training exercise for human scientists, too, cofounder and chief scientist Edward Hughes said. “Many PhD students actually start by doing this.”

Hughes 告诉 TechCrunch，在任务中击败其他 AI 系统并不是重点，重点在于他们实现这一目标的方式。“对我们来说，最有趣的地方不在于击败那些前沿智能体（当然我们也很高兴），而在于我们构建它的过程。”

Beating other AI systems at the task wasn’t the point, Hughes told TechCrunch; how they got there was. “What was most interesting to us about this was not so much the result of beating those frontier agents — which of course we liked — but was actually the way we went about building this.”

以下是让投资者眼前一亮的部分：与 Anthropic 的 Claude Opus 4.8 和 OpenAI 的 GPT-5.5（两者都是规模大得多的前沿系统）相比，Faraday 运行在一个相对微小的模型上，即拥有 270 亿参数的 Qwen 3.6。（粗略地说，“参数”是模型规模及其训练成本的代名词。）

Here’s the part that should catch an investor’s eye: measured against Anthropic’s Claude Opus 4.8 and OpenAI’s GPT-5.5 — both much larger, frontier-scale systems — Faraday runs on a comparatively tiny model called Qwen 3.6 that has just 27 billion parameters. (Roughly speaking, “parameters” is a proxy for a model’s size and, typically, its training costs, as well.)

Inherent 对成功的衡量标准也远不止于准确性。除了复现结果外，它还希望 Faraday 展示出“研究品味”——即一种判断哪些实验值得进行以及如何设计实验的直觉。教授这种无形的“品味”非常困难，这正是强化学习发挥作用的地方。这是一种通过奖励 AI 系统良好结果，而不是为其制定具体规则的训练方法。

Inherent’s bar for success was also higher than simply accuracy. Beyond replicating results, it wanted Faraday to demonstrate “research taste” — an instinct for what experiments are worth running and how to design them well. Teaching something as intangible as taste is hard, which is where reinforcement learning comes in. It’s a training method that rewards an AI system for good outcomes rather than spelling out rules for it to follow.

Inherent 并没有主要通过研究科学本身是如何进行的来训练其智能体，而是倾向于这种基于奖励的方法，并押注它能更好地推广到其长期目标——即能够为多个科学领域做出贡献的智能体。“我们始终以构建 AI 科学家智能体并赋予其品味为北极星目标，”Hughes 说。

Rather than training its agents primarily on the study of how science itself is conducted, Inherent leans on this reward-based approach, betting it will generalize better to its longer-term goal of agents capable of contributing across many scientific fields. “We’re always guided by that north star of building an AI scientist agent and imbuing our agents with taste,” Hughes said.

这种专注也决定了 Inherent 选择不开发什么。据该公司称，他们没有开发自己的编码工具，而是让 Faraday 使用 OpenAI 的 GPT-5.5 Codex，就像人类科学家倾向于使用现有软件而不是事事亲力亲为一样。

That focus has also shaped what Inherent chooses not to build. Rather than developing its own coding tool, it had Faraday use OpenAI’s GPT-5.5 Codex instead, much the way human scientists lean on existing software rather than building everything themselves, according to the company.

Inherent 还试图避免构建只会对用户唯唯诺诺的智能体。相反，Hughes 说，其目标是模仿他最喜欢的那种队友——那种会回来告诉你：“我对这个感到好奇，所以我去做了这些实验。你觉得这些结果怎么样？”

Inherent is also trying to avoid building agents that simply tell users what they want to hear. Instead, Hughes said, the goal is modeled on his favorite kind of teammate — the kind who comes back and says: “I got curious about this, and I went off and I did these experiments. What do you think of these results?”

这种协作本能也延伸到了 Inherent 的公司运营方式上。其十几名员工都在伦敦国王十字区（King’s Cross）的办公室里现场办公——这个曾经破败的伦敦街区，在 Google DeepMind 的入驻下，已成为全球顶级 AI 中心之一。“我们相信伦敦是最佳选择，”Hughes 说。

That collaborative instinct extends to how Inherent operates as a company. Its dozen employees all work in person out of an office in King’s Cross — the once-rundown London neighborhood that Google DeepMind’s presence helped turn into one of the world’s top AI hubs. “We believe that London is the place to be,” Hughes said.

Hughes 看好伦敦的人才密度，但也加入了呼吁结束“园艺假”（garden leave）的行列——这是英国常见的一种做法，即禁止离职员工在辞职后的几个月内加入或创办竞争对手公司。美国研究人员通常不会面临这种限制，这让美国初创公司在招聘离职人才时占据了先机。“这是个人观点而非公司观点，但我确实受到了‘园艺假’问题的困扰，”他告诉 TechCrunch。

Hughes is bullish on London’s density of AI talent, but he has also added his voice to calls to end “garden leave” — the practice, common in the U.K., of barring departing employees from joining or starting a rival company for months after they resign. It’s a restriction American researchers generally don’t face, giving U.S. startups a head start on hiring talent who’ve left a prior role. “This is a personal view rather than a company view, but I was affected by the garden leave problem,” he told TechCrunch.

Hughes 最终克服了这一限制，与另外两名 DeepMind 校友及第四位联合创始人共同创立了 Inherent。这家初创公司的发展步伐并未放缓。它计划在今年年底前将员工人数增加到“约 20 到 25 人”。考虑到其在世界模型方面的雄心，以及 Demis Hassabis 的新职位让一些 DeepMind 员工感到不安，Inherent 的招聘攻势可能会使其成为考虑跳槽的 DeepMind 员工的理想去处。

Hughes eventually got around that constraint and started Inherent alongside two other DeepMind alumni and a fourth cofounder. The startup isn’t slowing down either. It plans to grow its headcount to “about 20 to 25” by the end of the year. Given its ambitions in world models as well, and with Demis Hassabis’s new role leaving some DeepMind staff unsettled, Inherent’s hiring push could make it an appealing landing spot for DeepMind employees weighing a move.