{"podcast":{"title":"Daily Paper Cast","slug":"daily-paper-cast-7079649","podcast_index_feed_id":7079649,"rss_url":"https://feeds.transistor.fm/daily-paper-cast-ai","website_url":"https://dailypapercast.transistor.fm/","image_url":"https://img.transistorcdn.com/IxaBeiMluxrMS9W9wB8hFMfmvH27KvwaSMzuhucupn0/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.jpg","author":"Jingwen Liang, Gengyu Wang","episode_count":2000,"summary":"We update every weekday to discuss highest-voted papers from Huggingface Daily Paper (https://huggingface.co/papers). Both the podcast scripts and audio are generated by AI. Feedback and suggestions are welcome! Email us: dailypapercast.ai@gmail.com Creator: Jingwen Liang, 3D ML, https://www.linkedin.com/in/jingwen-liang/ Gengyu Wang, LLM ML, http://wanggengyu.com Listen on: Spotify: https://open.spotify.com/show/21nrhmdaA8qoBiH8q03NXL Apple Podcast: https://podcasts.apple.com/us/podcast/daily-paper-cast/id1777620236 Cover Image by Kawen Kuang https://kawen.art","last_synced_at":"2026-09-09T20:18:08.781137+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649"},"episode":{"title":"LatentPress: Context Compression Beyond Text and Vision","slug":"latentpress-context-compression-beyond-text-and-vision","published_at":"2026-09-04T08:21:45+00:00","page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649/latentpress-context-compression-beyond-text-and-vision","show_page_url":"https://stenobird.com/podcast/daily-paper-cast-7079649","url":"https://share.transistor.fm/s/d0235eed","audio_url":"https://media.transistor.fm/d0235eed/ad827368.mp3","summary":"🤗 Upvotes: 52 | cs.LG, cs.AI Authors: Zhengze Zhou, Hejian Sang Title: LatentPress: Context Compression Beyond Text and Vision Arxiv: http://arxiv.org/abs/2609.01507v2 Abstract: Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. We introduce LatentPress, which writes conversational histories and long documents into a third representation: continuous memory tokens that a frozen decoder reads directly through its input-embedding interface, with no text reconstruction at inference. A small reader-matched writer compresses $4$-$16\\times$ while training only an adapter (4.2M-26.2M parameters, $\\sim\\!0.1\\%$ of the decoder). On LongMemEval, LatentPress reaches $0.504$ accuracy at $7.70\\times$ compression versus $0.490$ for uncompressed evidence, outperforming text summaries (0.184) and OCR-based compression (0.426 to 0.312). On LongBench-QA, in-domain writers match or exceed raw-context reading at $4$-$8\\times$ compression, while $16\\times$ trails raw. Writing takes 43ms per conversation, roughly an order of magnitude faster than text summarization or OCR reconstruction, and reading is $5$-$9\\times$ faster than raw context or cached OCR. We validate the interface under two transfer settings, zero-shot from UltraChat to LongMemEval memory QA and from LongMemEval-derived QA to unseen LongBench document domains, establishing direct soft tokens as a practical machine-facing context interface beyond text and vision. The implementation of the experiments could be found at: https://github.com/HJSang/LatentPress .","meta_description":"🤗 Upvotes: 52 | cs.LG, cs.AI Authors: Zhengze Zhou, Hejian Sang Title: LatentPress: Context Compression Beyond Text and Vision Arxiv: http://arxiv.org/abs…","key_points":[],"chapters":[],"topics":[],"duration_seconds":1222,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/daily-paper-cast-7079649/episodes/latentpress-context-compression-beyond-text-and-vision/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/daily-paper-cast-7079649/latentpress-context-compression-beyond-text-and-vision.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}