Progressive Cramming: Reliable Token Compression and What It Reveals
Token cramming compresses sequences into learned embeddings with near-perfect reconstruction, but fixed token budgets and 99\% accuracy thresholds leave it unclear whether residual errors reflect optimization failures or fundamental limits. We introduce progressive cramming, which grows the target prefix token-by-token, stopping only when reconstruction is no longer achievable within a fixed optimization budget. Progressive trajectories occupy low-dimensional structure in embedding space. Prepending a crammed embedding causes a moderate but consistent accuracy drop on multiple-choice benchmarks even with the original prefix in context, and collapses capability almost entirely under genera...
| Authors | Dmitrii Tarasov, Timofei Lashukov, Elizaveta Goncharova et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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