Multimodal
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PAPERNEO-VLA drops the vision encoder so action policies can read visual features at any depth
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.
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PAPERFactEmbed 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.
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PAPERRoute Anything studies when routing between models beats one fixed model
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.
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PAPERReviso trains a video captioning agent to choose which visual evidence to inspect
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.