This paper offers a machine learning perspective on uncertainty in dynamical systems, distinguishing aleatoric and epistemic uncertainties, and analyzing their propagation across tasks.

Yusuf Sale, Christopher Bülte, Felix Czaja et al.

EEVEE framework employs a router-conditioned prompt set with co-evolution to enhance LLM robustness across heterogeneous task streams, improving scores by 10.38-24.32 points.

Weixian Xu, Shilong Liu, Mengdi Wang

Unified analysis of GP-UCB and DEC in RKHS bandits via MAIR framework, revealing their fundamental differences and advantages.

Yunbei Xu

COGENT integrates Graph Neural Networks with Neural ODEs for continuous long-term physical forecasting on irregular meshes, outperforming traditional autoregressive models.

Zesheng Liu, Maryam Rahnemoonfar

Proposes an efficient online algorithm for drifting halfspaces under Massart noise, achieving an error bound of η + ˜O(Δ^{1/3}/γ), nearly matching theoretical limits.

Mingchen Ma, Guyang Cao, Jelena Diakonikolas et al.

This paper introduces flexible sequence kernels based on evolutionary substitution matrices, leveraging Gaussian processes for data-efficient protein property prediction, outperforming embedding-based methods.

Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani et al.

DRPO introduces smooth advantage-weighted quadratic regularization to improve stability and efficiency in LLM RL training, replacing hard masks with continuous gradient weights.

Jiarui Yao, Xiangxin Zhou, Penghui Qi et al.

Introducing Topological Neural Operators (TNO), a framework leveraging cell complexes and discrete exterior calculus to improve PDE modeling on complex geometries, achieving over 20% accuracy gains.

Lennart Bastian, Samuel Leventhal, Mustafa Hajij et al.

Proposes an fully automated time-series forecasting framework combining high-frequency dataset TimeTrack with dynamic local telemetry, using NAS to generate accurate models, effectively addressing cold-start issues.

Abd Elghani Meliani, Arora Sagar, Adlen Ksentini et al.

Proposes CoMetaPNS, integrating continual Bayesian GMM with set-conditioned generative models for personalized cardiac electrophysiology simulation, achieving superior accuracy and anti-forgetting.

Ryan Missel, Xiajun Jiang, Linwei Wang

Introduces RP-Regret for adaptive opponents, with algorithms achieving sublinear regret and better equilibria in repeated games.

Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu et al.

Proposes Polynomial Weight Preconditioning (PC) layer to regulate singular-value spectrum, accelerating LLM pretraining; achieves 2× speedup on Llama-1B with no inference overhead.

Senmiao Wang, Tiantian Fang, Haoran Zhang et al.

GraphDETR formulates subgraph detection as set prediction, achieving 91.2 AP on molecular datasets with graphs up to 1000 nodes and 50-node substructures.

Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

Proposes an algebraic identity and low-rank SVD approximation to compute mean curvature efficiently on high-dimensional data manifolds, reducing complexity from O(m^4) to near O(k^2 m).

Alexandre L. M. Levada

MolE-RAG integrates literature, molecular features, and structural similarity to enhance LLM-based molecular property prediction, boosting ROC-AUC by up to 28% and reducing RMSE by 67%.

Joey Chan, Wonbin Kweon, Ashley Shin et al.

Proposes DistIL, a distributional imitation learning algorithm with monotonic improvement guarantees, leveraging rich feedback for complex reasoning tasks.

Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad

Skill-RM unifies heterogeneous evaluation criteria via agent skills, enabling dynamic resource orchestration, outperforming traditional judges with a 3-6% improvement on RewardBench2.

Tao Chen, Gangwei Jiang, Pengyu Cheng et al.

Introducing the 'Sleep' paradigm with Knowledge Seeding and Dreaming mechanisms enables LLMs to self-modify and consolidate memories for continual learning.

Ali Behrouz, Farnoosh Hashemi, Vahab Mirrokni

Proposes ROSA, a reward distribution-based framework for inducing diverse behaviors without performance loss, leveraging set functions and unbiased gradient estimators.

Anthony GX-Chen, Ankit Anand, Gheorghe Comanici et al.

This paper introduces an equilibrium propagation (EP)-based training method for deep predictive coding networks (PCNs), achieving 13.23% Top-5 error on ImageNet with a 10-layer VGG model, close to the 12.2% baseline of backpropagation.

Tugdual Kerjan, Rasmus Høier, Benjamin Scellier