{"total":13,"items":[{"citing_arxiv_id":"2607.01677","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ICDepth: Taming Video Diffusion Models for Video Depth Estimation via In-Context Conditioning","primary_cat":"cs.CV","submitted_at":"2026-07-02T04:05:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ICDepth adapts text-to-video diffusion transformers for video depth estimation via in-context conditioning, achieving SOTA results on benchmarks with 6-13x less training data than prior generative methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.30599","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video 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discriminative signal in RL-based preference optimization for generative flow models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.08392","ref_index":77,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ST-BiBench: Benchmarking Multi-Stream Multimodal Coordination in Bimanual Embodied Tasks for MLLMs","primary_cat":"cs.RO","submitted_at":"2026-02-09T08:47:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.","context_count":1,"top_context_role":"other","top_context_polarity":"unclear","context_text":"Follow-your-emoji: Fine-controllable and expressive freestyle portrait animation. 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