10 papers
Revisiting Cauchy-Schwarz Norm Bounds for SDC Detection in LLM Inference
Anton Altenbernd, Dylan Everingham, Philipp Wiesner +1
Large Language Models (LLMs) execute billions of floating-point operations per output token across many hardware accelerators. At this scale, hardware-induced faults can bypass sys…
Where Should RL Post-Training Compute Go? Model Size, Search, Learning, and Feedback
Patrick Wilhelm, Odej Kao
Reinforcement Learning (RL) post-training is increasingly used to adapt foundation models for reasoning, planning, and feedback-driven robot-learning pipelines, but constrained pos…
From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents
Patrick Wilhelm, Odej Kao
Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal model state and environment cont…
Exploring Silent Data Corruption as a Reliability Challenge in LLM Training
Anton Altenbernd, Philipp Wiesner, Odej Kao
As Large Language Models (LLMs) scale in size and complexity, the consequences of failures during training become increasingly severe. A major challenge arises from Silent Data Cor…
Revisiting Gradient Staleness: Evaluating Distance Metrics for Asynchronous Federated Learning Aggregation
Patrick Wilhelm, Odej Kao
In asynchronous federated learning (FL), client devices send updates to a central server at varying times based on their computational speed, often using stale versions of the glob…
Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding
Patrick Wilhelm, Inese Yilmaz, Odej Kao
Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data…
Monitoring Emergent Reward Hacking During Generation via Internal Activations
Patrick Wilhelm, Thorsten Wittkopp, Odej Kao
Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has…
Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
Philipp Wiesner, Soeren Becker, Brett Cornick +3
Training large language models (LLMs) requires substantial compute and energy. At the same time, renewable energy sources regularly produce more electricity than the grid can absor…
QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Haci Ismail Aslan, Syed Muhammad Mahmudul Haque, Joel Witzke +1
Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-…
Towards a Decentralised Application-Centric Orchestration Framework in the Cloud-Edge Continuum
Amjad Ullah, Andras Markus, Hacı İsmail Aslan +5
The efficient management of complex distributed applications in the Cloud-Edge continuum, including their deployment on heterogeneous computing resources and run-time operations, p…