6 papers · 1 filter
Emergent and Subliminal Misalignment Through the Lens of Data-Mediated Transfer
Baris Askin, Muhammed Ustaomeroglu, Anupam Nayak +3
Fine-tuning LLMs on narrow harmful datasets can induce Emergent Misalignment (EM), where models exhibit misaligned behavior far beyond the fine-tuning distribution. We argue that e…
Towards Effective Theory of LLMs: A Representation Learning Approach
Muhammed Ustaomeroglu, Guannan Qu
We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather than microscopic details. RET…
PubSwap: Public-Data Off-Policy Coordination for Federated RLVR
Anupam Nayak, Baris Askin, Muhammed Ustaomeroglu +2
Reasoning post-training with reinforcement learning from verifiable rewards (RLVR) is typically studied in centralized settings, yet many realistic applications involve decentraliz…
BLOCK-EM: Preventing Emergent Misalignment via Latent Blocking
Muhammed Ustaomeroglu, Guannan Qu
Emergent misalignment can arise when a language model is fine-tuned on a narrowly scoped supervised objective: the model learns the target behavior, yet also develops undesirable o…
Transformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions
Vidur Sinha, Muhammed Ustaomeroglu, Guannan Qu
Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations. First, they typically rely on an expon…
A Theoretical Study of (Hyper) Self-Attention through the Lens of Interactions: Representation, Training, Generalization
Muhammed Ustaomeroglu, Guannan Qu
Self-attention has emerged as a core component of modern neural architectures, yet its theoretical underpinnings remain elusive. In this paper, we study self-attention through the…