AdaptMerge: Inference Time Adaptive Visual and Language-Guided Token Merging for Efficient Large Multimodal Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advances in Large Multimodal Models (LMMs) have showcased impressive visual understanding and vision-language reasoning capabilities, yet their computational cost hinders practical deployment, especially in resourceconstrained settings.A key bottleneck is the large number of visual tokens generated by its vision encoders, which increases latency and memory demands.Existing token reduction methods often require costly fine-tuning or apply fixed token reduction ratios, ignoring image complexity and vision-language interactions.We propose AdaptMerge, a trainingfree, inference-time token merging strategy that adaptively reduces visual tokens by leveraging feature diversity and language-guided relevance.By dynamically adjusting to image complexity and ensuring multimodal coherence, AdaptMerge significantly lowers floating-point operations while improving performance.Extensive experiments on Google's latest Gemma 3 models (4B and 12B parameters) across four challenging benchmarks demonstrate that AdaptMerge outperforms state-of-the-art token reduction techniques, achieving both reduced computational costs and improved performance, thereby providing a practical pathway to more efficient LMMs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it