Late Fusion and Multi-Level Fission Amplify Cross-Modal Transfer in Text-Speech LMs
Bibliographic record
Abstract
Text-pretrained language models (LMs) encode rich world knowledge, but adapting them to process and generate perceptual modalities such as audio and images while effectively leveraging that knowledge remains challenging. Perceptual modalities are finer-grained and less semantically dense than text, making it unclear how functions learned during text pretraining can be reused. We study this problem through the lens of a layerwise abstraction-refinement dynamic observed in transformer LMs: representations first become more abstract and compositional, then are refined into representations predictive of fine-grained structure. This perspective suggests that adapting an LM to finer-grained modalities requires: (i) allocating additional fine-to-coarse processing at the input and coarse-to-fine processing at the output, consistent with late modality fusion and an output-side analogue we term late fission; and (ii) allowing the output predictor to preserve input-dependent selective access to both high-level semantic structure and low-level perceptual detail, motivating our use of attention residuals in fission. We instantiate this view in LF${}^{2}$AR, a simple architecture combining these mechanisms, and study it on text-as-images and speech, two modalities for which semantic correspondence to text can be controlled. Across models ranging from 135M to 2B parameters and adapted to these modalities, we find that these components increase feature abstraction, strengthen cross-modal alignment, enable such alignment to emerge at smaller compute budgets, improve preservation of text-like predictive structure, and yield better performance on text-as-images and speech versions of language understanding and reasoning benchmarks. Additionally, attention residuals induce sparse, interpretable use of deep backbone layers, enabling early-exit decoding with a 1.9$\times$ generation speedup.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".