SEATS adaptively selects and removes non-text tokens before and inside the LLM layers of omni-modal models, yielding 9.3x FLOPs reduction and 4.8x prefill speedup at 10% token retention while keeping 96.3% performance.
Llava- video: Video instruction tuning with synthetic data.Transactions on Machine Learning Research
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
VISD proposes structured self-distillation with a multi-dimensional judge model and direction-magnitude decoupling to improve token-level credit assignment and convergence speed in VideoLLM reasoning training.
citing papers explorer
-
Stage-adaptive Token Selection for Efficient Omni-modal LLMs
SEATS adaptively selects and removes non-text tokens before and inside the LLM layers of omni-modal models, yielding 9.3x FLOPs reduction and 4.8x prefill speedup at 10% token retention while keeping 96.3% performance.
-
VISD: Enhancing Video Reasoning via Structured Self-Distillation
VISD proposes structured self-distillation with a multi-dimensional judge model and direction-magnitude decoupling to improve token-level credit assignment and convergence speed in VideoLLM reasoning training.