arXiv:2606. 30139v1 Announce Type: new Abstract: Relevance is not permission.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The paper introduces Warrant, a method that locates and controls the contributions of attention mechanisms to model metrics. Warrant exposes the item‑wise contribution path to the reported metric and applies query‑conditioned permission on that path. Experiments on multiple datasets show that Warrant improves primary metrics in most comparisons, reveals a weak correlation between attention and prediction utility, and demonstrates that learned permission can recover evidence ranking while suppressing distractors.
The paper introduces Warrant, a method that learns to gate attention-derived item contributions before they are aggregated for prediction. Unlike traditional attention, which assumes relevance guarantees usefulness, Warrant applies learned, item‑wise permissions to control both the relative allocation and total transmission mass. Experiments on CyGNet and HotpotQA datasets show that ungated attention paths degrade performance, while Warrant’s selective gating recovers or improves metrics such as MRR and reduces unsupported selections.
CaST-POI is a next‑point‑of‑interest recommender that conditions the user representation on each candidate location by adding bucketised biases for visit recency and geographic distance to the candidate. Unlike prior models that treat all candidates uniformly, CaST-POI lets each candidate read the trajectory with different attention weights grounded in real distance. Experiments on NYC, TKY, and CA datasets show significant MRR gains over seven baselines, with ablation revealing the importance of the revisit gate and spatial bias.
arXiv:2606. 12945v1 Announce Type: new Abstract: Long-running LLM agents accumulate interaction histories far larger than any context window, forcing a standing decision: what to encode deeply, what to forget, and what to retrieve under a fixed memory budget.
arXiv:2608. 15877v1 Announce Type: new Abstract: Search and recommendation serve a shared discovery objective but encode intent differently.
arXiv:2608.21702v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity,...