Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
Finite-length analysis of low-density parity-check codes on the binary erasure channel,
3 Pith papers cite this work, alongside 816 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
Extends density evolution to role-typed factor graphs with nonlinear Boolean verifiers to predict asymptotic unresolved subclaims in AI agent networks under three erasure failure modes.
citing papers explorer
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Pre-trained Large Language Models Learn Hidden Markov Models In-context
Pre-trained LLMs learn to predict HMM-generated sequences via in-context learning, approaching theoretical optimum on synthetic HMMs and matching expert models on real animal decision data.
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Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models
QATS is a new polylog-time approximate decoding procedure for HMMs that builds admissible state sequences by locally maximizing likelihoods over paths with at most three segments via adaptive ternary segmentation and cumulative sum storage.
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On the Reliability of Networks of AI Agents: Density Evolution, Stopping Sets, and Architecture Optimization
Extends density evolution to role-typed factor graphs with nonlinear Boolean verifiers to predict asymptotic unresolved subclaims in AI agent networks under three erasure failure modes.