High-reputation, high-skill users stay engaged, but their roles are shifting—less novice Q&A, more expert-to-expert interactions. A potential silver lining: while #quantity declines, #quality may evolve in ways that sustain AI training data. 3/n

Our preprint (w/ Negin Maddah; link below) confirms the scale of decline via user-level analysis but reveals a key nuance: lower-skill, low-rep users disengaged, while high-skill, high-rep users remain active. (Figs. show both raw data & causal estimates+standard errors). 2/n

Decline of Stack Overflow user engagement is more nuanced than it seems.

The sharp activity drop in #knowledge-sharing platforms has sparked concerns re the sustainability of #GenAI. If LLMs depend on human-generated data but reduce content generation, are we hitting the so-called #peak_data? 1/n

This post is not about DeepSeek.

To address this issue, we use VAE (a generative model), to condense the high-dimensional, discrete action space into a low-dimensional, continuous one. This transformation enables the managing RL agent to efficiently operate within the reduced space, optimizing the network structure continuously 3/n

New Preprint: Scalable adaptive network intervention for multi-agent systems using RL+Variational Auto Encoders (VAE)

Past research has shown that the network structure of agents interactions influences system-level behavior, thus can be used as an effective system governance intervention. 1/n

Finishing the move to the new office on the Thanksgiving eve.