Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models
Omnimodal language models (OLMs) enable unified audio-visual understanding, but processing long joint token sequences makes inference computationally prohibitive. While recent token compression methods attempt to alleviate this burden, compressing modalities in isolation often destroys the temporal cross-modal anchors necessary for coherent reasoning. We introduce Omni2LoRA, a two-stage framework for efficient parametric memory compression via coherence-preserving context distillation that bypasses the token bottleneck entirely. First, a Perceiver hypernetwork processes intermediate representations from a frozen OLM to encode the multimodal context into a full-rank Low-Rank Adaptation (Lo...
| Authors | Puneet Mathur, Manan Suri, Dinesh Manocha |
|---|---|
| Primary category | cs.AI |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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