Extending HGF to Other Modalities (Vision and Audio)

Extend and evaluate the Hybrid Gated Flow (HGF) architecture on non-text modalities, including vision and audio, to determine whether its efficiency–quality trade-offs generalize beyond language modeling.

Background

The paper evaluates HGF exclusively on language modeling (TinyStories) and frames the method as a general architectural approach to combine ternary quantization with selective FP16 correction.

The authors explicitly pose the application of HGF to other modalities—vision and audio—as an open question to test whether the hybrid gating concept translates to different data domains and architectures.

References

Key open questions include: (1) scaling behavior to billion-parameter models, (2) hardware kernel optimization for ternary operations, (3) adaptive gating mechanisms that vary across layers or heads, and (4) application to other modalities (vision, audio).

— Hybrid Gated Flow (HGF): Stabilizing 1.58-bit LLMs via Selective Low-Rank Correction  (2602.05269 - Pizzo, 5 Feb 2026) in Conclusion, Future Directions