Memory ideas

External memories, state compression, retrieval-style read/write policies and calibrated stopping rules.

✗ Failed on benchmark 2026

Response-Sufficient Neural Memory

Replace correlation-based memory pruning in an RNN or state-space model by measuring how hidden-state history changes the response to individual past input events. Train a compressed memory coordinate only if it preserves the event-consequence kernel for the target observable, such as future loss, prediction, or control return. A memory representation is accepted when the conditional variance of this kernel within compressed-state groups is small, even if dwell-time or autocorrelation…

Useful8/10
Difficulty6/10
Novelty8/10
Paper: The Memory Hidden in Response Fluctuations: Trajectory-Level Fluctuation-Response Theory and Inequalities for Non-Markovian Jump Dynamics arXiv:2608.20328
✗ Failed on benchmark 2026

Equal-Volume KV Vector Quantization

Replace consecutive or randomly assigned transformed KV coefficients with groups whose variance-volume is approximately equal. Train one equal-size vector-quantizer codebook per group, so a fixed-width cache does not waste its low-rate budget by forcing high-variance and low-variance coordinates into badly mismatched groups. This is a drop-in quantization-layout change that can be applied to keys, values, or both.

Useful8/10
Difficulty5/10
Novelty7/10
Paper: Spend Bits Where Queries Look: KV Cache Vector Quantization with Attention-Preserving Transforms arXiv:2608.04074