finding
active
finding:rudimentary-language-models-are-challenged-by-long-sequences-of-outputsRudimentary language models are challenged by long sequences of outputs.
Empirical observation explained by topological constraints: flat autoregressive architectures lack multiscale structure needed for long-range order.
Source paper
extracted_from(2026) · Francesco Sacco · Dalton Sakthivadivel · Michael Levin
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Communities (2)
community
- Spans attention head decomposition, benchmark awareness, and genomic pathogenicity prediction via neural models.
- Theoretical and empirical analysis of why AR language models cannot maintain coherence or convergence beyond their context window through local interactions alone.
Concepts (1)
concept
- Ability to maintain organized behavior over extended scales; shown limited in flat autoregressive models, enabled in hierarchical/biological systems.
Frameworks (1)
framework
- Second model system studied; used to show why flat autoregressive LLMs struggle with long-range coherence.
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