Public articles linked to the same research event.
arXiv The authors introduce the Language Model Agent Machine (LAM), a resource-bounded abstraction that fixes the underlying semantic model while explicitly charging harness-level resources, yielding four classes of results on communication, access, recomputation, and reliability, and testing communication and reliability predictions in controlled and held-out experiments on GPT-6 Astra, including checkpoint optima, policy selection under programmatic checking, and tradeoffs among call granularity, logical input traffic, and reliability on chained MATH tasks.
The authors introduce the Language Model Agent Machine (LAM), a resource-bounded abstraction that fixes the underlying semantic model while explicitly charging harness-level resources, yielding four classes of results on communication, access, recomputation, and reliability, and testing communication and reliability predictions in controlled and held-out experiments on GPT-6 Astra, including checkpoint optima, policy selection under programmatic checking, and tradeoffs among call granularity, logical input traffic, and reliability on chained MATH tasks.
The authors introduce the Language Model Agent Machine (LAM), a resource-bounded abstraction that fixes the underlying semantic model while explicitly charging harness-level resources, yielding four classes of results on communication, access, recomputation, and reliability, and testing communication and reliability predictions in controlled and held-out experiments on GPT-6 Astra, including checkpoint optima, policy selection under programmatic checking, and tradeoffs among call granularity, logical input traffic, and reliability on chained MATH tasks.
The authors introduce the Language Model Agent Machine (LAM), a resource-bounded abstraction that fixes the underlying semantic model while explicitly charging harness-level resources, yielding four classes of results on communication, access, recomputation, and reliability, and testing communication and reliability predictions in controlled and held-out experiments on GPT-6 Astra, including checkpoint optima, policy selection under programmatic checking, and tradeoffs among call granularity, logical input traffic, and reliability on chained MATH tasks.