The paper gives the first tight necessity and sufficiency conditions for successful reward poisoning attacks in linear MDPs.
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Adam-HNAG is a splitting-based reformulation of Adam that yields the first convergence proof for Adam-type methods, including accelerated rates, in convex smooth optimization.
MeshTok uses AMR-inspired adaptive multiscale tokenization to improve the efficiency-accuracy trade-off of Transformer models for PDEs over uniform-grid baselines.
MERIT enables decentralized instruction tuning via conflict-aware PCA splitting and parameter-space merging, raising average benchmark scores above joint training on multimodal and text mixtures.
FedQual improves federated label distribution learning under heterogeneous annotation quality via quality-adaptive training with a global anchor and reliability-aware aggregation, backed by new benchmarks and a proof that client-specific calibration strictly outperforms uniform calibration.
ABox abduction under repair semantics for inconsistent KBs yields a full complexity landscape in lightweight description logics DL-Lite and EL_bot.
DLM4G applies graph-aware adaptive noising in a diffusion framework to generate text from graphs, outperforming larger autoregressive and diffusion baselines in factual grounding and edit sensitivity on three datasets plus molecule captioning.
LoRA gradient descent converges to a stationary point at rate O(1/log T).
MLHCA is a new ML-powered combinatorial auction combining value and demand queries to reduce efficiency loss by up to 10x and queries by up to 58% versus prior SOTA.
Visual graph mind maps outperform text-flattened versions as internal reasoning scaffolds for LLMs on multi-hop QA, with the advantage holding after fine-tuning and distillation.
UHD-GCN-BIQA models structural dependencies among sampled patches via a hybrid kNN graph and residual graph convolutions to achieve competitive PLCC and SRCC with the lowest RMSE on the UHD-IQA benchmark for blind ultra-high-definition image quality assessment.
Derives expectation consistency condition as necessary and sufficient for calibration under covariate shift and proposes ECL loss with matching sample complexity to ECE.
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
Random slicing for subsampling combined with Nadaraya-Watson smoothing enables faster and improved persistence-based topological optimization of point clouds in 2D and 3D.
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
SHINE trains a scalable in-context hypernetwork to generate high-quality LoRA adapters from contexts in one pass, enabling efficient LLM adaptation that saves time and compute compared to standard fine-tuning.
Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
Requiring multiple properties or optimality criteria for abduction hypotheses in ELbot under brave and AR semantics often does not raise complexity.
ResGIN-Att predicts drug synergy by extracting multi-scale molecular features with residual GIN, fusing them via LSTM, and modeling interactions with cross-attention, achieving competitive results on five benchmark datasets.
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.