Mirror Particle is building a ‘world model’ of human behavior
Mirror Particle is building a ‘world model’ of human behavior
Mirror Particle 正在构建人类行为的“世界模型”
Startups that promise to predict how humans will behave are having a moment. Over the past year, Simile raised $200 million at a $2 billion valuation; Aaru raised $88 million at a $1 billion valuation; and Humans&, an AI startup that announced a massive $480 million seed round in January at a $4.48 billion valuation, launched Persimmon to model human behavior. 承诺预测人类行为的初创公司正迎来高光时刻。过去一年里，Simile 以 20 亿美元的估值筹集了 2 亿美元；Aaru 以 10 亿美元的估值筹集了 8800 万美元；而人工智能初创公司 Humans& 在 1 月份宣布了一轮高达 4.8 亿美元的种子轮融资，估值达到 44.8 亿美元，并推出了用于模拟人类行为的 Persimmon。
The status quo for human behavior prediction today relies heavily on large language models (LLMs) that are prompted or fine-tuned to role-play as a target demographic. But two-year-old, San Francisco-based Mirror Particle thinks that approach is fundamentally broken. 目前，人类行为预测的现状在很大程度上依赖于大型语言模型（LLM），通过提示词或微调让模型扮演特定的目标人群。但总部位于旧金山、成立两年的 Mirror Particle 认为这种方法从根本上是有缺陷的。
“It’s like bringing a super soaker to Niagara Falls,” says Abhivyakti Ahuja, co-founder and CEO of Mirror Particle, which provides brands with an AI engine that predicts consumer behavior and the reasons behind it. “LLMs have been trained on hundreds of billions of data points. How much can you influence its behavior by [fine-tuning] with such a small amount of data? It’s still stuck in the past.” “这就像拿着一把水枪去尼亚加拉大瀑布，”Mirror Particle 的联合创始人兼首席执行官 Abhivyakti Ahuja 说道。该公司为品牌提供一种人工智能引擎，用于预测消费者行为及其背后的原因。“大语言模型是在数千亿个数据点上训练出来的。你用这么少量的数据进行微调，能对它的行为产生多大影响？它依然停留在过去。”
Ahuja doesn’t think LLMs see the world the way a human does. “LLMs are modeling written language, but humans are made of visual perception, spatial reasoning, social intelligence.” Relying on them, she says, means getting insights based on what humans don’t notice, which is beside the point when trying to predict human behavior. Ahuja 认为大语言模型看待世界的方式与人类不同。“大语言模型是在模拟书面语言，但人类是由视觉感知、空间推理和社会智能构成的。”她说，依赖这些模型意味着基于人类未注意到的事物获取洞察，而这在试图预测人类行为时是偏离重点的。
Mirror Particle is taking another approach: building a foundation model, or as Ahuja describes it, a world model built from scratch that simulates why humans do what they do and how human behavior changes over time. “We don’t want to capture the static person,” Ahuja said. “We want to capture the changing person. That means capturing the longitudinal data on how people are changing, what triggers are changing them and to what degree.” If they aren’t changing, she added, “that’s also a signal.” Mirror Particle 采取了另一种方法：构建一个基础模型，或者用 Ahuja 的话来说，是一个从零开始构建的“世界模型”，用以模拟人类行为的原因以及人类行为随时间的变化。“我们不想捕捉静态的人，”Ahuja 说，“我们想捕捉不断变化的人。这意味着要获取关于人们如何变化、什么触发因素导致了这些变化以及变化程度的纵向数据。”她补充道，如果他们没有变化，“那也是一种信号。”
Mirror Particle has already raised an angel round and says it’s close to closing its first venture round. The company is also competing next week in Startup Battlefield 200, TechCrunch’s renowned startup competition taking place at TechCrunch Disrupt 2026 in San Francisco on October 13-15. Mirror Particle 已经完成了天使轮融资，并表示即将完成首轮风险投资。该公司下周还将参加 TechCrunch 的著名创业竞赛 Startup Battlefield 200，该竞赛将于 10 月 13 日至 15 日在旧金山举行的 TechCrunch Disrupt 2026 大会上进行。
The startup relies on a proprietary combination of data that includes its clients’ customer data, current events, pop culture, social media, and more to model a demographic segment, thinking of it as a system that evolves over time and tracking how motivations shift as it moves through experiences. Much of the focus is on “revealed behavior” — what people actually do rather than self-reported survey answers. 这家初创公司依靠其专有的数据组合（包括客户数据、时事、流行文化、社交媒体等）来模拟特定的人口群体，将其视为一个随时间演变的系统，并追踪动机如何随着经历而转变。其重点很大程度上在于“揭示行为”——即人们实际做了什么，而不是调查问卷中的自述答案。
Like its rivals, Mirror Particle’s initial go-to-market strategy focuses on where budgets already exist for these kinds of insights: market research and brand and product strategy. Mirror might, for instance, help a beauty brand not just write better ad copy for makeup that would appeal to Gen Z, but also determine if that demographic even wants that product. 与竞争对手一样，Mirror Particle 最初的市场进入策略专注于那些已经为这类洞察投入预算的领域：市场研究以及品牌和产品策略。例如，Mirror 不仅可以帮助美妆品牌撰写更吸引 Z 世代的化妆品广告文案，还能判断该群体是否真的需要这款产品。
“What if [the target demographic] doesn’t want eyeshadow palettes?” Ahuja said. “Maybe blush is a better option to go for if you want to sell a product to this market.” Mirror Particle’s prediction engine also provides customers with the “why” behind current or future behavior — the motivations, constraints, and additional context that justify its recommendation, helping brands make smarter decisions. “如果（目标群体）不想要眼影盘怎么办？”Ahuja 说，“如果你想向这个市场销售产品，也许腮红是更好的选择。”Mirror Particle 的预测引擎还为客户提供当前或未来行为背后的“原因”——即支持其建议的动机、限制因素和额外背景，从而帮助品牌做出更明智的决策。
In one early pilot, a well-known pet food brand wanted to know what imagery to put on the packaging to boost sales. Chicken? Beef? Vegetables? Mirror’s technology found that the brand was asking the wrong question. The imagery didn’t matter. The problem was that the brand was so recognizable that it was considered mass market and cheap, and sales would plateau until it addressed that perception issue. 在一次早期试点中，一家知名宠物食品品牌想知道包装上应该放什么图案来促进销售。鸡肉？牛肉？蔬菜？Mirror 的技术发现该品牌问错了问题。图案并不重要。问题在于该品牌知名度太高，被视为大众化且廉价的产品，如果不解决这一认知问题，销售额就会陷入停滞。
“The way we see our model evolving is like how a baby learns about the world,” Ahuja said, noting that babies move from vision to language to body awareness to social intelligence. That fundamental interest in modeling the human brain comes from Ahuja’s background studying neuroscience and computer science. “我们看待模型演进的方式就像婴儿了解世界的过程，”Ahuja 说。她指出，婴儿的学习过程是从视觉到语言，再到身体意识，最后到社会智能。这种对模拟人类大脑的根本兴趣源于 Ahuja 在神经科学和计算机科学方面的学习背景。
Originally from India, she ended up studying at the University of Toronto, where she became inspired by AI pioneer Geoffrey Hinton’s contributions to neural networks. After school, Ahuja ended up at Amazon Robotics building robots that build other robots. That’s where she met her co-founders, Will Song and Thomson Yen. Song has spent a chunk of his career building sales personalization engines, and Yen focused on using deep learning to learn about how AI agents understand human behavior. Ahuja 来自印度，后来在多伦多大学学习，在那里她受到了人工智能先驱 Geoffrey Hinton 在神经网络方面贡献的启发。毕业后，Ahuja 进入亚马逊机器人部门，负责制造能够制造其他机器人的机器人。在那里，她遇到了她的联合创始人 Will Song 和 Thomson Yen。Song 的职业生涯中很大一部分时间都在构建销售个性化引擎，而 Yen 则专注于利用深度学习来研究人工智能代理如何理解人类行为。
The startup’s long-term vision is to be the “general layer for anticipating human behavior” and moving from broader population-level analyses to individual-level insights. “We just need a better model of humans if we’re going to work alongside AI and with each other,” Ahuja said. 该初创公司的长期愿景是成为“预测人类行为的通用层”，并从更广泛的人口层面分析转向个体层面的洞察。“如果我们想要与人工智能并肩工作，并与彼此协作，我们就需要一个更好的人类模型，”Ahuja 说。