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This paper theoretically analyzes how curriculum learning, by decomposing complex problems into simpler sub-problems and composing solutions, can dramatically reduce the sample complexity of learning to simulate sequential computations (semiautomata) compared to direct methods, achieving subpolynomial supervision requirements in supervised fine-tuning and exponentially weaker coverage conditions in reinforcement learning with verifiable rewards.
This paper formalizes 'compositional behavioral leakage' (CBL), a failure mode in prompt-composed agentic systems where editing one prompt module silently shifts the behavior of others due to transformer self-attention lacking module-level isolation. It presents an operational definition, a reusable three-channel protocol, and empirical evidence from 144 trials on a Claude Sonnet 4.6 agent, finding sub-threshold interference that could compound across thousands of decisions.
This paper proposes Adversarial Concept Search, a method that uses the representational geometry of large language models to predict compositional failures without evaluating specific inputs. The approach identifies high-risk scenarios by measuring interference between salient features.