Training a GNN surrogate model on exact graph zeta functions of long-range interacting lattices yields lower prediction errors on unseen infinite-system eigenvalue problems than a GNN trained on equivalent Monte Carlo-sampled lattice datasets.
Adversarial Debate Score
47% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Graph lattice sums and graph zeta functions for long-range interacting quantum lattice models
Taming the exponential increase of the Hilbert space dimension with system size in the simulation of gapped quantum lattice models is of the highest relevance for understanding and designing exotic qu...
- When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
Modeling multivariate time series by representing them as graphs, where individual series act as nodes and pairwise temporal corre- lations serve as edges, has gained significant traction. Recent adva...
- Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers
Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields...
- Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP). Howe...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.