arXiv:2606.09672v1 Announce Type: cross Abstract: Ask a pretrained biomedical language model whether "cortisol 28 ug/dL" and "stock-market volatility" are related, and it returns a cosine similarity of 0.83 on a scale where 1.0 means identical. The two share no mechanism. This is not a corner case: every off-the-shelf biomedical encoder we tested (BioBERT, PubMedBERT, BioM-ELECTRA) scores unrelated cross-domain pairs between 0.76 and 0.92 when the answer should be near zero. Accuracy on cross-domain discrimination is 0%. Retrieval systems survive this, because a language model downstream filte
The proliferation of large language models in specialized domains like biomedicine is exposing fundamental limitations in their ability to infer true causal relationships from mere correlation, especially across disparate domains.
This highlights a critical shortcoming in current AI methodologies, where models can generate high similarity scores between unrelated concepts, posing challenges for reliable scientific discovery, medical applications, and agentic systems.
The reliance on basic similarity metrics for complex reasoning in AI is being formally challenged, pushing for the integration of human metadata and more sophisticated causal discovery mechanisms in model training and inference.
- · Researchers in causal AI
- · Developers of human-in-the-loop AI systems
- · Specialized data providers with rich metadata
- · AI safety and interpretability researchers
- · Developers relying solely on cosine similarity for complex reasoning
- · Generative AI applications without robust grounding mechanisms
- · Oversimplified domain-specific encoders
- · Systems treating correlation as causation
Immediate efforts will focus on integrating external knowledge graphs and explicit causal models into existing biomedical language models to improve reliability.
This will likely lead to a divergence in AI development, with a greater emphasis on 'grounded AI' for critical applications versus pure statistical pattern matching.
The development of robust causal AI could significantly accelerate scientific discovery and medical breakthroughs, while ungrounded AI might face increasing regulatory scrutiny for high-stakes uses.
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Read at arXiv cs.LG