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#artificial intelligence Preprint Oct 2026

TPBench: A Turning-Point Benchmark for Dialogue Compression

A compressor can keep the facts of a dialogue and still drop the turn that changed them. A user corrects a price, reverses a choice, or adds a constraint. We call this failure turning-point eviction. One overall retention score hides it, because that score mixes what the user first wanted with what the user wants now....

Minji Park, Seunghyun Yoon, Hyuk Lim · 0 citations
Preprint Jul 2026

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory

Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizons, and active overwriting of stale ones. In our diagnostic suite, the strongest efficient baselines tend to solve only one side well. Continuous-time-parameterized state-space mode...

Hyuk Lim, Seunghyun Yoon · 0 citations
#artificial intelligence Preprint Sep 2026

Transfiver: Human-AI Co-Inference through a Shared Editable State

The TRANSparent Framework for Interactive, Verifiable, Editable Representation (Transfiver) is introduced, an architecture for human-AI co-inference through a shared editable state that separates shared parameters from the persistent state, which evolves during deployment without parameter retraining.

Minji Park, Seunghyun Yoon, Hyuk Lim · 0 citations

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