arXiv Artificial Intelligence

Convergent Emergence of In-Context Learning Across Modalities

1 day ago 15.09.2026 07:00 👁 1 views
Convergent Emergence of In-Context Learning Across Modalities

Quick summary

arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models trained for next-token prediction on human text. Recently, few-shot ICL has been demonstrated in autoregressive genomic models as well. This raises a question: does ICL emerge broadly across domains, and if so, what common structure is shared? To address both, we develop a controlled cross-modality framework that instantiates the

Key takeaways

  • arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models trained for next-token prediction on human text.
  • Recently, few-shot ICL has been demonstrated in autoregressive genomic models as well.
  • This raises a question: does ICL emerge broadly across domains, and if so, what common structure is shared?

Why it matters

“Convergent Emergence of In-Context Learning Across Modalities” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗