The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The paper investigates the macroscopic dynamics of large language model (LLM)-driven agents by applying a least‑action principle to estimate the generative directionality of LLMs embedded within agents. Through experimental measurement of transition probabilities between LLM‑generated states, the authors find evidence of a tendency toward detailed balance, suggesting that LLM generation may be governed by underlying potential functions rather than explicit learning rules. This approach offers a model‑agnostic, effective description of LLM generative dynamics that could serve as a foundational theory for complex AI systems.
arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...
The paper introduces Particle GFlowNets, showing that Generative Marginalization Models (MaMs) are equivalent to Generative Flow Networks. It extends MaMs to non‑autoregressive sampling and proposes an automatic full‑state rejuvenation criterion based on the Gelman‑Rubin statistic to accelerate learning. Experiments demonstrate significant training speedups in large combinatorial spaces.
arXiv:2502. 19049v3 Announce Type: replace Abstract: Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by a diffusion function.
arXiv:2607.00588v2 Announce Type: replace Abstract: Continuous diffusion language models offer an alternative to autoregressive generation, but their generations may suffer from repetition. We find t...
arXiv:2602. 14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows.