Papers
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PAPERDPDM routes agent decisions through one frozen conditional predictor instead of prompting
A paper proposes DPDM, which pairs a language model encoder with a frozen pretrained predictor so agents learn from deployment history without any weight updates.
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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.