research
The Model Learned Which Measurement Was Worth Buying
ECHO-k chooses additional modalities from internal representations when the eventual prediction task is unknown.
Summary
ECHO-k chooses additional modalities from internal representations when the eventual prediction task is unknown.
Collecting every available modality can be expensive or slow. ECHO-k uses a pretrained model’s internal representations as proxy targets for cross-modal information, then learns a policy that acquires measurements sequentially. The authors provide guarantees in a stylized linear setting and report improved budgeted downstream performance across several foundation-model backends against task-agnostic, label-free baselines. The theory’s restricted setting and the evaluated budgets matter; the work does not establish that omitted measurements are always dispensable.
Why it matters
ECHO-k chooses additional modalities from internal representations when the eventual prediction task is unknown.
Limits and context
- The theory’s restricted setting and the evaluated budgets matter; the work does not establish that omitted measurements are always dispensable.
Key claims
ECHO-k chooses additional modalities from internal representations when the eventual prediction task is unknown.
Qualification: The theory’s restricted setting and the evaluated budgets matter; the work does not establish that omitted measurements are always dispensable.
Evidence: source-2026-10-04-010
Sources
- arXiv preprint 2610.03454, original v1arXiv / submitting research authors · primary research
Corrections
: Publication note: This October 4 edition was reconstructed on October 5 after the workstation was offline. It uses original October 2 submissions from the standard Sunday-evening arXiv release, rather than claiming those papers were available for the missed 9 AM run. The date and 9 AM publication slot preserve archive chronology; October 5 developments are excluded. The September 27 Invention Desk is carried over because its replacement was generated October 5.