<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:news="http://www.google.com/schemas/sitemap-news/0.9"><url><loc>https://chiosa.app/papers/autocompress-automated-hardware-guided-model-compression-2611-08290</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>Hardware-aware pruning finds models that fit serving limits</news:title></news:news></url><url><loc>https://chiosa.app/papers/evomemstate-evolving-memory-state-for-personal-agents-2611-03743</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>Personal agents learn how to use evolving memories</news:title></news:news></url><url><loc>https://chiosa.app/papers/interactive-visual-gym-evaluating-visual-interaction-models-with-simulated-users-in-interactive-worlds-2611-08286</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>Simulated user reactions expose limits in visual assistance</news:title></news:news></url><url><loc>https://chiosa.app/papers/on-the-opportunities-and-challenges-of-prefill-to-autoregressive-cross-model-transfer-2611-06976</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>PLatour links larger prompt models to smaller generators</news:title></news:news></url><url><loc>https://chiosa.app/papers/sla-scaling-spatial-language-action-model-via-continual-pre-training-2611-08373</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>Robot training aligns 2D vision with 3D spatial knowledge</news:title></news:news></url><url><loc>https://chiosa.app/papers/the-intrusion-pathway-how-non-evidential-context-degrades-language-model-performance-2611-07778</loc><news:news><news:publication><news:name>Chiosa</news:name><news:language>en</news:language></news:publication><news:publication_date>2026-11-06T20:02:35Z</news:publication_date><news:title>Irrelevant text weakens answers while subject information remains</news:title></news:news></url></urlset>
