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We present SciForge, a multimodal research-native AI workbench that reserves the graphical interface for human judgment while search, parsing, model routing, workflow execution, plotting, writing, and presentation generation run as modular agent-accessible services. SciForge is built around five pillars: (i) \\emph{goal-scoped scientific decision governance} for \\textbf{goal-oriented} research, with review gates and shared review surfaces; (ii) \\emph{translate-then-reason} for \\textbf{multimodal} input, routing scientific objects through domain translators before the agent reasons; (iii) \\emph{evidence governance} for \\textbf{auditable} traceability, linking claims to ","title":"SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery","url":"https://arxiv.org/abs/2607.16038","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.16038v1 Announce Type: new \nAbstract: Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet general-purpose AI assistants rarely preserve these objects as a coherent, auditable research state. 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