Topics

• Architecture Design with Scientific Priors
  • Inductive biases for invariances, conservation laws, units, and geometry
  • Modular, interpretable, controllable designs across multimodal scientific data
• Evidence Grounding and Provenance
  • Retrieval over databases/knowledge graphs with explicit attribution
  • Faithfulness and version-aware evidence tracking
• Tool- and Simulator-Integrated Modeling
  • Reliable interfaces to simulators, solvers, and symbolic engines
  • Hybrid mechanistic data-driven learning with traceable tool execution
• Reliable Data and Pretraining
  • Curation for quality and coverage (de-dup, contamination checks, uncertainty metadata)
  • Robust objectives and continual updates under shifting protocols
• Uncertainty and Risk Control
  • Calibrated uncertainty, abstention/defer, and risk–coverage trade-offs
  • Uncertainty under shift and tool feedback
• Validity, Robustness, and Stress Tests
  • Constraint checks, invariance/counterfactual tests, and closed-loop tool validation
  • Robustness to rare regimes and distribution shifts
• Reproducibility and Auditing
  • Versioned pipelines, environment/seed capture, and tool-trace logs
  • Standardized reporting for third-party verification
• Benchmarks and Community Testbeds
  • Reliability-focused benchmarks (correctness, faithfulness, constraints, calibration)
  • Shared evaluation harnesses and open resources