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2 curated events
papersTODAY 04:00 UTC

arXiv Paper Proposes Graph-Based Prompt Compression Using Lévy Walk Pruning

A new arXiv preprint argues that current prompt compression techniques treat text as a flat token sequence and therefore miss how key information is scattered across a document and linked by syntactic and semantic ties. The authors instead model the text as a multiplex graph and prune it with a Lévy walk-guided procedure to decide which parts to keep. The work is listed as a cross-listing replacement on cs.AI.

papersSEP 10 04:00 UTC

LiFTER: A Neuro-Symbolic Method for Interpretable Continuous-Time Graph Forecasting

Researchers introduce LiFTER, a neuro-symbolic framework aimed at making continuous-time dynamic graph forecasting more transparent. Instead of leaving link predictions hidden inside opaque neural states that compress past interactions, the method surfaces which entities are shared across events and how temporal patterns contribute to forecasts. A revised version (v2) of the preprint has been posted on arXiv.