做科学计算或模拟仿真的团队，终于有了让通用AI代理直接操作专业软件的轻量方案——SIGA 把数小时的学习成本压缩到几分钟，且能自我进化，建议做计算物理、流体力学或分子动力学的开发者点开看看。
科学家使用专业模拟软件时，需要学习复杂的输入语言，耗时数小时到数天。SIGA 是一种轻量级适配层，通过检索、程序记忆、轨迹内验证和验证强制终止，为通用编码代理提供模拟器的“可执行契约”（词汇、结构约束、验证规则等）。在 GEOS 多物理场模拟器上，SIGA 约5分钟即可生成完整配置，TreeSim 得分超0.90，相当于专家3小时的工作，速度提升约36倍。在更难的保留数据集上，SIGA 将 TreeSim 从0.720提升至0.789，相对提升约10%，并将跨种子标准差降低16倍。自进化机制通过重写先前轨迹中的适配器内容，进一步提升了性能，甚至超越最强的手工设计配置。在 OpenFOAM 和 LAMMPS 上的迁移实验表明，不同模拟器的瓶颈不同：结构完整性不足时验证最重要，领域正确性不足时记忆和检索最重要。
SIGA: Self-Evolving Coding-Agent Adapters for Scientific Simulation
Advanced scientific simulators expose specialized input languages that turn simulation goals into executable configurations, but learning them can cost domain scientists hours to days. We study simulator setup as a problem of agent-tool interface grounding: what minimal simulator-specific adaptations are needed for an off-the-shelf coding agent to operate real scientific software? Our intuition is that coding agents already know how to navigate files, edit code, run commands, and repair outputs, but they lack the simulator's executable contract: its vocabulary, structural constraints, validation rules, and termination conditions. We introduce SIGA, a Simulator-Interface Grounding Adapter that supplies this contract through retrieval, procedural memory, in-trajectory validation, and validation-enforced termination. We primarily evaluate SIGA on GEOS, an open-source multiphysics simulator used in subsurface science. SIGA produces a complete GEOS deck in about five minutes with TreeSim above 0.90, matching an extended-budget human expert who took about three hours, a roughly 36x wall-clock speedup. On a harder held-out set, grounding raises TreeSim from 0.720 to 0.789, a roughly 10% relative gain over the bare agent, and can reduce the across-seed standard deviation by 16x. Self-evolution further improves SIGA by rewriting adapter contents from prior trajectories, yielding the highest held-out GEOS mean and matching or outperforming the strongest hand-designed configuration. Transfers to OpenFOAM and LAMMPS show that the dominant mechanism shifts by interface: validation matters most when structural completeness is the bottleneck, while memory and retrieval matter most when domain correctness is the bottleneck. These results suggest that lightweight, self-improvable grounding layers can turn general coding agents into practical operators of scientific software.