25 citations · 32 across the 25 of their papers we have counts for
5 papers · 1 filter
DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation
Peyman Hosseini, Ondrej Bohdal, Ahmed Alajrami +6
Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models,…
Diffusion Alignment Beyond KL: Variance Minimisation as Effective Policy Optimiser
Zijing Ou, Jacob Si, Junyi Zhu +4
Diffusion alignment adapts pretrained diffusion models to sample from reward-tilted distributions along the denoising trajectory. This process naturally admits a Sequential Monte C…
Feature-Space Generative Models for One-Shot Class-Incremental Learning
Jack Foster, Kirill Paramonov, Mete Ozay +1
Few-shot class-incremental learning (FSCIL) is a paradigm where a model, initially trained on a dataset of base classes, must adapt to an expanding problem space by recognizing nov…
Clustering-driven Memory Compression for On-device Large Language Models
Ondrej Bohdal, Pramit Saha, Umberto Michieli +2
Large language models (LLMs) often rely on user-specific memories distilled from past interactions to enable personalized generation. A common practice is to concatenate these memo…
Data-driven Clustering and Merging of Adapters for On-device Large Language Models
Ondrej Bohdal, Taha Ceritli, Mete Ozay +4
On-device large language models commonly employ task-specific adapters (e.g., LoRAs) to deliver strong performance on downstream tasks. While storing all available adapters is impr…