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Simile Secures $100M to Tackle LLM Coherence

  //business-finance.news-articles.net/content/202 .. simile-secures-100m-to-tackle-llm-coherence.html
  Published in Business and Finance on Friday, February 13th 2026 at 21:04 GMT by Observer
      Locales: UNITED STATES, CANADA

Saturday, February 14th, 2026 - The future of text generation may be shifting. Simile, a freshly funded startup aiming to tackle the longstanding issues of coherence and consistency in large language models (LLMs), announced today a significant $100 million seed round. This substantial investment, led by Lux Capital and Gradient Ventures, isn't just about the money; it's about the high-profile backing the company has secured. Simile has drawn support from two giants in the AI field: Fei-Fei Li, renowned Stanford professor and pioneer in computer vision and AI ethics, and Andrej Karpathy, the former driving force behind Tesla's AI initiatives.

LLMs, such as OpenAI's GPT series, have captivated the world with their ability to produce human-like text. From crafting poems to writing code, their potential seems limitless. However, beneath the fluent surface lies a fundamental flaw: a tendency towards inconsistency, particularly when tasked with generating extended content. While adept at stringing together a sentence or two, these models often struggle to maintain a logical flow and coherent narrative over longer outputs. This limitation significantly hinders their application in areas demanding sustained, meaningful communication, such as long-form journalism, in-depth technical documentation, or complex storytelling.

Simile's core mission is to address this very issue. "Current LLMs are good at generating a sentence or two, but fall apart when you ask them to generate a longer piece of text," explains Mitchell Melsky, Simile's CEO and co-founder. "The problem isn't that they don't understand the words, it's that they don't understand the connections between the words." This suggests a paradigm shift in how LLMs are approached. Instead of simply predicting the next word in a sequence, Simile aims to build a model that understands the relationships between concepts, allowing for a more holistic and logical construction of text.

While the specifics of Simile's technology remain closely guarded, the company hints at a process that involves actively connecting related concepts. This implies a move away from purely statistical language modeling towards a more knowledge-graph-like approach, where ideas and entities are represented as nodes and their relationships as edges. By explicitly modeling these connections, Simile hopes to ensure that generated text isn't just grammatically correct, but also semantically sound and logically consistent.

The Implications of Coherent LLMs

The potential impact of truly coherent LLMs extends far beyond simply improving the quality of generated text. Consider the ramifications for various industries. In education, AI tutors could provide personalized learning experiences with complex, evolving narratives. In healthcare, LLMs could generate detailed patient summaries and personalized treatment plans with enhanced accuracy and clarity. For creative writers, these models could serve as powerful collaborators, assisting with plot development, character building, and world-building.

Furthermore, a resolution to the coherence problem would be a significant step towards more trustworthy AI. Currently, the unreliability of LLMs makes them unsuitable for critical applications where accuracy is paramount. Improved coherence, coupled with rigorous fact-checking mechanisms, could unlock the potential of LLMs in areas such as legal research, financial analysis, and scientific discovery.

The Competitive Landscape and Future Outlook

Simile enters a crowded field. OpenAI, Google, Anthropic, and numerous other companies are actively researching and developing LLMs. However, Simile differentiates itself by focusing specifically on coherence - a problem that many competitors seem to be treating as a secondary concern. The backing of Li and Karpathy also provides the startup with invaluable expertise and credibility. Li's work on AI ethics ensures that Simile's development prioritizes responsible AI practices, while Karpathy's deep understanding of machine learning architecture will be crucial for building a robust and scalable model.

With $100 million in funding, Simile plans to aggressively expand its team and further refine its technology. The company anticipates releasing more details about its approach in the coming months. Melsky is optimistic about the future. "We want to build a world where AI can reliably generate high-quality, coherent text for everyone," he says. "That's a really ambitious goal, but we think it's achievable." Whether Simile can deliver on this promise remains to be seen, but its focused approach and stellar backing suggest it is a venture to watch closely. The coming years will likely reveal whether Simile's technology can truly reimagine text generation and unlock the full potential of LLMs.