Tag
ProgramAsWeights is a tool from the University of Waterloo that lets users describe AI functions in English, compile them into LoRA adapters, and run them locally on CPU using a shared base model for offline execution without API calls.
This paper proposes a modular framework using Activated LoRA adapters and a context-aware routing mechanism to efficiently mitigate harms in large language models, improving safety alignment while preserving task performance.
Lorivo is a serverless platform that allows sharing GPU servers for hosting multiple LoRA adapters, simplifying deployment and reducing costs for fine-tuned AI models.
VDN-Minimax-H3 is an open-source hybrid-attention model that speeds up video generation with near-lossless quality, featuring fast inference and plug-and-play adapters powered by MiniMax H3.
This paper introduces SLAaaT, a method that enables AI agents to dynamically switch between specialized LoRA adapters during trajectories, improving composition of capabilities and outperforming baselines on synthetic coding tasks.
LatentSkill converts textual skills into LoRA adapters stored in weight space, reducing context overhead while maintaining modularity and composability for LLM agents, achieving significant improvements on ALFWorld and Search-QA benchmarks.
Proposes KOFF, a framework that decomposes pretrained LLMs into a sparse shared backbone and domain-specific external memories using structured pruning and LoRA adapters, achieving 12% sparsity without significant performance loss.
This paper characterizes backdoors in LoRA adapters that activate at the token feature level, and proposes behavioral and weight-level detection methods. The backdoor generalizes across related token patterns but not structurally identical ones, and detection methods show strong separation.
LoREnc is a training-free framework that secures foundation models and LoRA adapters via spectral truncation and compensation, preventing unauthorized model recovery while maintaining performance for authorized users. Accepted at ICIP 2026.