AI Post Transformers • Interactive Visualization

Explicit Information Transmission for Context Compression

Instead of hoping a few learned memory tokens survive layer-by-layer overwriting, this method freezes the backbone, reads hidden states across depth, and explicitly allocates token information into a small set of latent slots with a global transport plan.

arXiv2602.03784
Setup512 tokens → 128 slots
BackboneFrozen 1B / 3B
Trainable~1% overhead
ClaimLess slot redundancy
Episode Snapshot
66.55
Avg F1, 1B ComprExIT
72.88
Avg F1, 3B ComprExIT
38.37
Effective rank
4×
Compression ratio
Detected arXiv IDs from provided material: 2602.03784

Frozen Backbone, Explicit Transmission

One normal forward pass builds a hidden-state tape. A lightweight side system then performs depth-wise selection and width-wise allocation instead of repeatedly mutating memory tokens inside the model.

Soft slotsUpdated every layer, vulnerable to overwriting
Depth stageGates useful layers per token position
Width stageGlobal token-to-slot assignment
RiskExact recall still bottlenecked by latent capacity

Inside the Transmission System

Use the stage toggle to switch between layer selection and coordinated transport. Hover the matrices to inspect where information mass goes.

Benchmark Wins, with Scope Caveats

The transcript reports large gains over other soft compressors on six QA sets. Toggle backbones to compare the method landscape and dataset pattern.

ComprExIT ICAE 500x Beacon Prompt-tuning baseline / reference

Where the Method Helps, and Where It Still Hurts

The deployment story motivates the work, but the experiment regime is still 512→128. The visuals below separate what was shown from what remains open for real long-context systems.

References

Compact links for the paper, comparison baselines, and related episode context.

Ye, Yan, Shen, Chang, Mao, He • 2026 • arXiv:2602.03784
Ge, Hu, Wang, Chen, Wei • 2024 • ICAE comparison target
Mu, Li, Goodman • 2023 • classic gist-token baseline family
Rae et al. • 2019 • earlier memory compression lineage
Deng et al. • 2024 • failure modes for latent compression
AI Post Transformers • context-length deployment perspective