由 DeepMind 校友创办的 Inherent 称，其 AI“队友”在复现研究方面击败 Anthropic 和 OpenAI

Inherent 是一家由 Google DeepMind 校友创办的伦敦 AI 实验室。该公司表示，其 AI Agent 刚刚以远小于竞争对手的模型规模，击败了 Anthropic 和 OpenAI 的更大型模型。

在众多由 Google DeepMind 校友创办的初创公司中，Inherent 此前获得的关注相对较少。但在资金更充裕的竞争对手尚未向外界展示具体成果之际，这支总部位于伦敦的团队正开始披露其研发进展。

就在结束隐身模式并宣布完成 5000 万美元种子轮融资数周后，这家英国创业公司表示，其新发布的 AI Agent Faraday 已经胜过了规模更大、知名度更高的模型，具体任务是：在预先不知道答案的情况下，独立复现已发表科学论文的研究发现。

鉴于 Inherent 的目标更为宏大——构建能够发现新科学知识，而非仅验证已有结果的 AI——这或许听起来只是一个小把戏。但联合创始人兼首席科学家 Edward Hughes 表示，论文复现同样是人类科学家的标准训练项目。“很多博士生实际上就是从做这件事开始的。”

Hughes 对 TechCrunch 表示，击败其他 AI 系统并不是重点；实现这一结果的方式才是重点。“对我们来说，最有意思的并不完全是击败那些前沿 Agent 的结果——当然我们对此很高兴——而是我们构建它的方式。”

以下一点应该会引起投资者关注：与 Anthropic 的 Claude Opus 4.8 和 OpenAI 的 GPT-5.5 相比——二者都是规模大得多的前沿级系统——Faraday 运行在一个相对小巧、名为 Qwen 3.6 的模型之上，该模型仅有 270 亿个参数。（粗略而言，“参数”是衡量模型规模的一个代理指标，通常也与其训练成本相关。）Inherent 对成功的标准也不只是准确性。除了复现结果外，该公司还希望 Faraday 展现“研究品味”——即判断哪些实验值得进行，以及如何高质量设计这些实验的直觉。

教授这种难以言说的“品味”并不容易，这正是强化学习发挥作用的地方。这是一种根据良好结果奖励 AI 系统、而不是为其逐条规定执行规则的训练方法。Inherent 并未主要通过训练 Agent 学习科学研究本身的开展方式，而是依赖这种基于奖励的方法，并押注它能更好地泛化到其长期目标：开发能够在多个科学领域作出贡献的 Agent。

Hughes 表示：“我们始终以构建 AI 科学家 Agent、并赋予 Agent 研究品味这一北极星目标为指引。”这一重点也影响了 Inherent 决定不开发什么。该公司没有自行开发编程工具，而是让 Faraday 使用 OpenAI 的 GPT-5.5 Codex；据公司介绍，这与人类科学家依赖现有软件、而不是亲自构建一切的方式类似。

Inherent 还试图避免构建那些只会告诉用户他们想听什么的 Agent。Hughes 说，其目标是打造他最喜欢的那种队友：这类队友会回来告诉你：“我对这件事产生了好奇，所以我去做了这些实验。你觉得这些结果怎么样？”

这种协作意识也延伸至 Inherent 的公司运营方式。该公司 12 名员工全部在 King’s Cross 的办公室线下工作。这个伦敦地区曾经较为衰败，而 Google DeepMind 的入驻帮助其转变为全球顶级 AI 中心之一。Hughes 表示：“我们相信伦敦就是应该在的地方。”

Hughes 看好伦敦高密度的 AI 人才资源，但他也公开支持取消“花园假期”（garden leave）的呼声。该做法在英国很常见，即禁止离职员工在辞职后的数月内加入或创办竞争对手公司。美国研究人员通常不面临这类限制，因此美国初创公司在招聘刚离开原岗位的人才时拥有先发优势。他对 TechCrunch 表示：“这是个人观点，而不是公司观点，但我确实受到过花园假期问题的影响。”

Hughes 最终绕过了这一限制，与另外两名 DeepMind 校友以及第四位联合创始人共同创办了 Inherent。这家初创公司也没有放缓脚步：计划在今年年底前将员工人数扩充至“约 20 至 25 人”。鉴于该公司在世界模型方面同样抱有雄心，以及 Demis Hassabis 的新职务令部分 DeepMind 员工感到不安，Inherent 的招聘行动可能会使其成为正在考虑跳槽的 DeepMind 员工颇具吸引力的去处。

图片从左至右：Inherent 联合创始人 Louis Kirsch、Kaloyan Aleksiev、Tantum Collins 和 Edward Hughes。

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.

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.

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.”

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.”

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’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. 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. 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 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?”

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 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 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.

Pictured from left to right: Inherent co-founders Louis Kirsch, Kaloyan Aleksiev, Tantum Collins and Edward Hughes.