In a stochastic k-ary tree, a two-head transformer learns randomized DFS via policy gradient under depth-wise curriculum, generalizes to deeper trees, and adapts to imbalanced goals via discounting.
0-shot” in the paper is “0-shot
4 Pith papers cite this work. Polarity classification is still indexing.
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LLMs display clear performance stratification on formal language tasks aligned with Chomsky hierarchy complexity levels, limited by severe efficiency barriers rather than absolute capability.
A framework extracts a latent state machine from logs, induces a multi-table relational schema, and uses it as a generative prior to create synthetic data that augments real logs for better anomaly detection.
LMs solve entity tracking with state changes by parallel aggregation at the query token instead of incremental tracking, with REMOVE using a global suppression tag.
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Agentic Transformers Provably Learn to Search via Reinforcement Learning
In a stochastic k-ary tree, a two-head transformer learns randomized DFS via policy gradient under depth-wise curriculum, generalizes to deeper trees, and adapts to imbalanced goals via discounting.