Multimodal on skulldchttps://skulldc.net/tags/multimodal/Recent content in Multimodal on skulldcHugoen-usThu, 05 Nov 2026 10:25:39 +0000NEO-VLA drops the vision encoder so action policies can read visual features at any depthhttps://skulldc.net/2026/11/neo-vla-drops-the-vision-encoder-so-action-policies-can-read-visual-fe/Thu, 05 Nov 2026 10:25:39 +0000https://skulldc.net/2026/11/neo-vla-drops-the-vision-encoder-so-action-policies-can-read-visual-fe/A paper from MMLab@NTU argues that vision-language-action models lose action-critical detail in their visual encoders and proposes NEO-VLA, an encoder-free design.FactEmbed factorizes MLLM embeddings for composed image retrievalhttps://skulldc.net/2026/11/factembed-factorizes-mllm-embeddings-for-composed-image-retrieval/Wed, 04 Nov 2026 20:26:21 +0000https://skulldc.net/2026/11/factembed-factorizes-mllm-embeddings-for-composed-image-retrieval/A KAIST AI paper makes an MLLM imagine the requested image edit through factorized latents before building retrieval embeddings, and reuses those latents for cheap reranking.Route Anything studies when routing between models beats one fixed modelhttps://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/Wed, 04 Nov 2026 10:27:47 +0000https://skulldc.net/2026/11/route-anything-studies-when-routing-between-models-beats-one-fixed-mod/A new paper builds a 374,401-run trajectory matrix over 25 benchmarks and 452 candidate configurations to test how far learned routers can beat a single fixed candidate.Reviso trains a video captioning agent to choose which visual evidence to inspecthttps://skulldc.net/2026/11/reviso-trains-a-video-captioning-agent-to-choose-which-visual-evidence/Tue, 03 Nov 2026 10:27:42 +0000https://skulldc.net/2026/11/reviso-trains-a-video-captioning-agent-to-choose-which-visual-evidence/An agentic video captioner plans what to look at from a low-frame-rate preview, revisits key segments with tools, and is trained with GRPO and cost-aware rewards.