Autoregressive Language Models are Secretly Energy-Based Models: Insights into the Lookahead Capabilities of Next-Token Prediction
arXiv:2512.15605v4 Announce Type: replace Abstract: Autoregressive models (ARMs) currently constitute the dominant paradigm for large language models (LLMs). Energy-based models (EBMs) represent another class of models, which have historically been less prevalent in LLM development, yet naturally characterize the optimal policy in post-training alignment. In this paper, we provide a unified view of these two model classes. Taking the chain rule of probability as a starting point, we establish an explicit bijection between ARMs and EBMs in function space, which we show to correspond to a specia
This paper represents a significant theoretical advancement in understanding the foundational equivalence between two dominant AI model architectures, emerging as the field matures and seeks more unified theories.
A unified theoretical understanding of autoregressive and energy-based models could lead to more robust, efficient, and controllable AI systems, particularly for large language model development and alignment.
This paper redefines the theoretical relationship between ARMs and EBMs, potentially enabling the cross-application of insights and techniques between these previously distinct model paradigms.
- · AI researchers
- · LLM developers
- · AI compute infrastructure providers
- · AI development relying solely on heuristic advancements
This research provides a deeper theoretical foundation for current and future large language models.
It may lead to novel architectures combining the strengths of autoregressive and energy-based models, improving performance and alignment.
More robust and aligned LLMs could accelerate the development of autonomous AI agents and complex AI applications.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG