University of Tennessee at Knoxville

Rebuilding a 128×128 vorticity field from 8×8 observations is a 16× upscaling in which the coarse grid resolves only wavenumbers up to 4, so fine vortical structure must be inferred rather than interpolated. SIMR-NO splits the reconstruction into 32→64 and 64→128 stages, each adding a Fourier-layer residual correction gated by radial wavenumber on top of a bicubic base, with local convolutions recovering features beyond the truncated Fourier basis. Its mean relative error is 0.2506 on Kolmogorov-forced turbulence, below LapSRN, FNO and EDSR, and about 3% on decaying homogeneous isotropic turbulence at Re = 1000, 4000 and 8000, with little change across Reynolds number.

Rebuilding a 128×128 vorticity field from 8×8 observations is a 16× upscaling in which the coarse grid resolves only wavenumbers up to 4, so fine vortical structure must be inferred rather than interpolated. SIMR-NO splits the reconstruction into 32→64 and 64→128 stages, each adding a Fourier-layer residual correction gated by radial wavenumber on top of a bicubic base, with local convolutions recovering features beyond the truncated Fourier basis. Its mean relative error is 0.2506 on Kolmogorov-forced turbulence, below LapSRN, FNO and EDSR, and about 3% on decaying homogeneous isotropic turbulence at Re = 1000, 4000 and 8000, with little change across Reynolds number.

Surgical robots need to anticipate how instrument motion will deform tissue when visibility is interrupted, and prior differentiable physics simulation predicted absolute tissue positions, so reconstruction error, initialization, and simulation offsets left the whole surface persistently displaced. The forecast now anchors each tissue point to its last valid observation and predicts only the deformation induced by the known future tool trajectory, using mesh-free position-based dynamics with scene-specific rigidity, attachment, and grasp coupling. Across 12 surgical manipulation clips, the referenced forecast has a median within-clip error ratio of 0.912 against the persistence baseline, 95% interval 0.787–1.070, improving 8 of 12 clips, with the interval including parity.

Surgical robots need to anticipate how instrument motion will deform tissue when visibility is interrupted, and prior differentiable physics simulation predicted absolute tissue positions, so reconstruction error, initialization, and simulation offsets left the whole surface persistently displaced. The forecast now anchors each tissue point to its last valid observation and predicts only the deformation induced by the known future tool trajectory, using mesh-free position-based dynamics with scene-specific rigidity, attachment, and grasp coupling. Across 12 surgical manipulation clips, the referenced forecast has a median within-clip error ratio of 0.912 against the persistence baseline, 95% interval 0.787–1.070, improving 8 of 12 clips, with the interval including parity.

MolP2SBench recasts molecular property-to-structure inference as masked-scaffold multiple choice and adds shuffled and counterfactual property substitutions, separating a correct answer from genuine use of the supplied evidence. Across seven models, accuracy on a property channel does not track how strongly a model responds when that property is replaced, and model performance does not always match chemists' intuition about which properties are easier.

MolP2SBench recasts molecular property-to-structure inference as masked-scaffold multiple choice and adds shuffled and counterfactual property substitutions, separating a correct answer from genuine use of the supplied evidence. Across seven models, accuracy on a property channel does not track how strongly a model responds when that property is replaced, and model performance does not always match chemists' intuition about which properties are easier.

The work identifies context reliance in unstructured knowledge editing, where the standard passage-level objective gives later facts richer ground-truth context and thus lower initial losses so they appear easier to learn; it proposes FOVEATED, a plug-and-play framework that constructs focused views of each sentence by randomly shifting the RoPE positions assigned to the keys of its preceding context during editing and removing the perturbation afterward so native positional encoding is unchanged at inference, instantiated for both direct-optimization and locate-then-edit editors, with a theoretical analysis of how it counteracts context-induced difficulty underestimation and empirical improvements across five KE editors, two LLM backbones, and three benchmarks.

The work identifies context reliance in unstructured knowledge editing, where the standard passage-level objective gives later facts richer ground-truth context and thus lower initial losses so they appear easier to learn; it proposes FOVEATED, a plug-and-play framework that constructs focused views of each sentence by randomly shifting the RoPE positions assigned to the keys of its preceding context during editing and removing the perturbation afterward so native positional encoding is unchanged at inference, instantiated for both direct-optimization and locate-then-edit editors, with a theoretical analysis of how it counteracts context-induced difficulty underestimation and empirical improvements across five KE editors, two LLM backbones, and three benchmarks.