10 papers
Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection
Seohyeon Cha, Huancheng Chen, Haris Vikalo
Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting past data. However, existing ap…
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
Haoran Zhang, Dongjun Kim, Seohyeon Cha +1
Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise…
Foundation-Preserving Adaptation via Generalized Rayleigh-Quotient Optimization
Dongjun Kim, Adrian de Wynter, Huancheng Chen +2
While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraining. Existing forgetting aware…
Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
Jianing Zhu, Yeonju Ro, John Robertson +5
Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic syst…
When Is Rank-1 Steering Cheap? Geometry, Granularity, and Budgeted Search
John T. Robertson, Jianing Zhu, Haris Vikalo +1
Activation steering offers a lightweight way to control LLMs without retraining, but its effectiveness varies sharply across concepts. Prior work often reads this variability as ev…
CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization
Seohyeon Cha, Huancheng Chen, Dongjun Kim +4
Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats without retraining, typically using a small…