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Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing

Sadegh Majidi, Niloofar Mireshghallah, Kazem Taram 2026-07-24
Attention Inference Language Model Transformer Hardware Architecture ×

LeakyLMs introduces a set of attacks that leak proprietary model architecture and deployment information from production language models using only per-token generation timing. The method builds a detailed timing model of token generation on NVIDIA GPUs and performs a search over the architecture space to infer properties like transformer layers, hidden dimension size, and attention heads. Experimental evidence shows that for Llama models, the near-correct architectural configuration appears in the top-10 guesses over 90% of the time, and the attack successfully detects speculative decoding in Google Gemini Flash 2.5 with a draft context window of approximately 128K tokens. This matters because it demonstrates that sensitive model and deployment details can be stolen remotely via timing side channels, posing a significant security risk to proprietary language model services.

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A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors

Philip John, Eloghosa Ikponmwoba, Pinaki Pal, Opeoluwa Owoyele 2026-07-24
Design Space Exploration Agentic Architecture × Network

The problem is that existing methods for determining reactor cluster boundaries in lean blowout (LBO) prediction rely on manual heuristics or distance-based metrics. The method introduces a reinforcement learning (RL) framework with a multi-stage clustering–classification strategy, using an actor-critic agent to merge micro-clusters into optimal reactor zones. Experimental evidence using a Jet-A mechanism shows the RL framework improves predictive fidelity over k-means and captures correct LBO trends with substantial speedups. This matters because the RL-driven approach offers a computationally efficient reduced-order model for rapid design-space exploration in gas turbine combustors.

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DMG: A Scalable and Efficient Memory-Disaggregated Graph Processing System

Yizou Chen, Tsun-Yu Yang, Zhisheng Hu, Baotong Lu 2026-07-24
Data Architecture × Cache Workload Runtime Scheduling

Traditional graph processing systems on monolithic servers suffer from resource under-utilization, and existing disaggregated memory (DM) systems are impractical due to poor scalability and excessive compute-side cache demands. DMG introduces a DM-friendly graph store, an adaptive update coordinator, and a two-stage workload manager to address these issues. Experiments show DMG achieves up to 4.9X better performance and reduces cache demands by up to 18.9X compared to state-of-the-art DM-based systems. This matters because DMG is the first practical, scalable, and cache-efficient graph processing system for DM architectures, enabling efficient handling of growing graph sizes in data centers.

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