Under lossy context compression, larger compressors reduce reconstruction error but increase unfaithfulness via knowledge overwriting and semantic drift, violating scaling laws for faithful preservation.
REEF proposes relation tokens as graph foundation model units and uses hypernetworks to adapt aggregators and classifiers, outperforming existing methods in pre-training and transfer learning.
FairMT introduces a unified fairness framework for heterogeneous multi-task learning with partial labels, using asymmetric constraint aggregation and head-aware optimization to improve fairness without sacrificing utility.
DiMP applies diffusion modeling to masked tube-center inference and inter-frame motion prediction, eliminating positional leakage and deterministic trajectory collapse to improve dynamic point cloud pretraining.
SMI replaces MIA-based unlearned model auditing with training-free statistical estimation of non-member mixture proportions in feature space, yielding reliable forgetting rates and bootstrap reliability ranges.
Reformulating quality-diversity optimization as multi-objective optimization with many objectives enables set-based scalarization methods to solve QD problems with theoretical guarantees and competitive performance.
IBAHGT unifies high-order and long-range brain network dependencies via information bottleneck guidance to achieve precise, minimally redundant disease diagnosis.
AlignDrive conditions longitudinal planning on the lateral path via anchor-based 1D displacement prediction and safety-critical augmentation, achieving state-of-the-art Bench2Drive results.
ESSAM combines evolution strategies with sharpness-aware maximization to fine-tune LLMs with 10-18x lower GPU memory than RL while matching or exceeding PPO and GRPO accuracy on math reasoning.
A frozen vision-language-action model improves test-time reliability by retrieving past successful actions to guide flow-matching generation without parameter updates.
FoMEMO proposes foundation models for expensive multi-objective optimization that use synthetic pre-training and in-context preference-conditioned posteriors to optimize unknown problems without further training.
Cola DLM is a hierarchical latent diffusion language model using continuous latent priors and block-causal DiTs to achieve scalable non-autoregressive text generation. It demonstrates strong scaling behavior and quality competitive with matched autoregressive baselines across benchmarks.