NeurIPS 2025: L2M: Mutual Information Scaling Law for Long-Context Language Modeling
Listen to episode
About this episode
On the October 2025 in a joint collaboration between NSF AI Institute for Artificial Intelligence and Fundamental Interactions,Massachusetts Institute of Technology, Polytechnic University of Catalonia, Harvard University and University of California, Los Angeles researchers present a universal theoretical framework for understanding long-context language modeling based on a bipartite mutual information scaling law that is rigorously verified. This is in the paper "L2M: Mutual Information Scaling Law for Long-Context Language Modeling".
This research paper investigates how **large language models** manage **long-range dependencies** by applying principles from **information theory**. The authors introduce the **L2M framework**, which establishes that a model's ability to process extensive context is strictly limited by the size of its **history state**. While **transformers** utilize a growing cache of data to maintain performance across long sequences, models with fixed-size states, such as **SSMs and RNNs**, face inherent capacity bottlenecks as input length increases. By utilizing **bipartite mutual information** as a metric, the study formalizes the theoretical requirements for an architecture to be considered **MI-capable**. Ultimately, this work provides a principled method for evaluating and designing **efficient architectures** that can sustain complex reasoning over thousands of tokens.
Source:
https://arxiv.org/pdf/2503.04725
More AI podcast episodes
Browse all →Want to find AI jobs?
Join thousands of AI professionals finding their next opportunity