{"pubkey":"cebf16d58a55b2d724744de583eec49a12c276385a5903bac4b81917276cdc58","posts":[{"contentHash":"76878799c067403764914be0fde036ca78874af38de080294c6fc9e800cae27e","protocol":"bitcoin","authorPubkey":"cebf16d58a55b2d724744de583eec49a12c276385a5903bac4b81917276cdc58","text":">mfw Research news 06/11/2025 >SeedEdit 3.0: Fast and High-Quality Generative Image Editing https://seed.bytedance.com/tech/seededit >CIVET: Systematic Evaluation of Understanding in VLMs https://arxiv.org/abs/2506.05146 >Refer to Anything with Vision-Language Prompts https://Ref2Any.github.io >HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation https://research.nvidia.com/labs/dir/hmar >Multi-scale Image Super Resolution with a Single Auto-Regressive Model https://arxiv.org/abs/2506.04990 >APVR: Hour-Level Long Video Understanding with Adaptive Pivot Visual Information Retrieval https://arxiv.org/abs/2506.04953 >Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations https://arxiv.org/abs/2506.04951 >DualX-VSR: Dual Axial Spatial$\\times$Temporal Transformer for Real-World Video Super-Resolution without Motion Compensation https://arxiv.org/abs/2506.04830 >Robust Few-Shot Vision-Language Model Adaptation https://hannawang09.github.io/projects/srapf >HoliSafe: Holistic Safety Benchmarking and Modeling with Safety Meta Token for Vision-Language Model https://youngwanlee.github.io/holisafe >Gen-n-Val: Agentic Image Data Generation and Validation https://arxiv.org/abs/2506.04676 >Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts https://arxiv.org/abs/2506.04673 >FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion https://fps.ziplab.co >Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders https://arxiv.org/abs/2506.04641 >Video World Models with Long-term Spatial Memory https://spmem.github.io >Contrastive Flow Matching https://arxiv.org/abs/2506.05350 >Perceive Anything: Recognize, Explain, Caption, and Segment Anything in Images and Videos https://Perceive-Anything.github.io","topicHash":"913c62c2ee31793c7e8517600dbc8f0c337afae133700a128a7d8ffb8a21c93e","topicName":"technology","parentHash":"da27d7600f8c6b5f6af63f6e5fc999a35a4cad97cbdc5a734f0d91c4aff056f9","costSunk":"0","biasSupport":0,"biasOppose":0,"biasVouch":0,"biasNull":0,"blockHeight":null,"confirmations":0,"timestamp":"2026-11-06T04:05:40.270Z","tripFp":"13bf3f69fb74","viewCount":0,"powDifficulty":0,"relation":0,"ephemeral":true,"nonce":"66e0563c6e39ae68","sig":"e58e4c5560cb909f982f00b37318edfa652040b884f3f3ba5150bb23c0219c15f17dbb68969cf100514973989b45da1b9554458c30656c7ea4c93313bf8dfe60","ephStamp":{"blockHashHex":"00000000000000000001b42ffd6d98932a48823e9c17f3c5ea9872729b4bbb89","nonceHex":"8972000000000000"},"replyCount":1,"chainReplyCount":0,"vaporReplyCount":1,"clusterSize":2,"receiptHeight":974758,"expiryHeight":976918,"authorMarks":{"since":"2026-11-06T01:05:35.471Z","via":"sight","inscribed":0,"likes":0,"biases":0},"lang":"en"}],"nextCursor":null,"pageReceipt":{"host":"6af58b1822e0cabfe57d76540f745d059b438f489a796ec6a705c6540233845e","t":"2026-11-08T20:13:02.595Z","sig":"921e22c5e367671390578fcf543aecbf826e6d092d16572b8b93d56cb348ed6ed9213c2e00ac1c816c7ddca54194344324798792c7cab22c4394fe94d7017819"},"marks":{"since":"2026-11-06T01:05:35.471Z","via":"sight","inscribed":0,"likes":0,"biases":0}}