LLM-based tree search discovers predictive zebrafish neural models that outperform forecasting baselines, though structural priors are needed to prevent shortcut exploitation and ensure mechanistic recovery.
OmniSpace improves autonomous vehicle MLLM spatial reasoning via camera pose injection, multi-view epipolar attention, and 3D geometric distillation without auxiliary 3D models, surpassing existing methods across planning, risk detection, and language benchmarks.
Controllable user simulation is formalized as causal inference, proving supervised fine-tuning injects look-ahead bias causing geometric variance explosion and controllability collapse, with proposed mitigations restoring consistency and robust generalization.
Pointwise consistency requires a connected co-occurrence graph and PAC learning needs completeness, with optimal sample complexity ranging from nearly linear to quadratic.
Differentially private online clustering transforms streams into private semi-coresets via a generic reduction, matching or improving approximation, space, and runtime while inheriting consistency from underlying non-private algorithms.
A unified framework bounds DP privacy leakage against multi-target membership, attribute, and reconstruction attacks using only privacy parameters and adversarial baseline success rates.
SkillOS uses RL to train a skill curator that updates an external SkillRepo from experience, improving self-evolving agents across reasoning and multi-turn tasks.
Post-hoc learning to defer is cast as density-ratio estimation between ideal distributions, yielding adjustable deferral rules that recover Chow's rule and outperform baselines.
This paper proposes a linear programming-based minimum s-t cut algorithm with an optimal Lipschitz constant, yielding the first dynamic algorithm with non-trivial recourse and improved b-matching stability.
This paper bounds the query complexity of multi-round local search on general graphs, proving deterministic upper and randomized lower bounds that extend grid results to arbitrary connected graphs.
DriveSpatial benchmarks vision-language models' spatiotemporal autonomous driving intelligence, finding a 28.4-point human gap with cognitive scene construction as the key bottleneck.
Constellation-Aware Transformer injects geometric inductive biases into semi-supervised equalization via constellation-aware attention and FIR-inspired filtering, outperforming baselines with fewer pilots.