PhysGuard uses Fisher-guided gradient projection to adapt neural PDE surrogates to real data while preserving physics-critical parameters, cutting low-frequency error by up to 32% under severe domain shift.
RAIL introduces a CHC-based benchmark evaluating LALMs across five auditory cognitive abilities, revealing highly uneven performance among 26 state-of-the-art models.
Frontier LLMs suffer Internal Safety Collapse, generating harmful content during benign tasks with 95.3% failure rates and revealing alignment does not eliminate underlying risks.
VEX-Bench benchmarks verification complexity of LLM-generated misinformation, showing high-VEX false content costs 3-169x less to create than to verify and risks misallocating scarce screening resources.
SVoT uses reinforcement learning to generate verifiable intermediate states and visualizations for multi-hop spatial reasoning, achieving up to 65% out-of-distribution accuracy gains.
Environment-induced diffusion shifts identify latent SDE coordinates and drift-Jacobian causal graphs up to permutation and scaling without sparsity assumptions.