Piper decouples training strategies via IR, enabling flexible multi-strategy scheduling with performance parity and efficiency gains.

Megan Frisella, Shubham Tiwari, Andy Ruan et al.

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

Introduces Itô maps for arbitrary-step SDE sampling, enabling conditional sampling and control, enhancing diversity and efficiency.

Zhengkai Pan, Peter Potaptchik, Wenxi Yao et al.

JOIN employs opposition-score and task-conditioned manipulability for autonomous heterogeneous bimanual collaboration, achieving 95% success in real-world tests.

Drake Moore, Matt Cheng, Xiang Zhi Tan et al.

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.

MOFA-VTON employs diffusion models with dual-region masks and cross-attention-based layout adjustment, enabling user-controlled, fine-grained virtual try-on with diverse styles.

Xiaoyu Han, Chenyang Wang, Jing Wang et al.

This paper introduces a fully distributed multi-UGV exploration framework combining descriptor-based loop closure detection and loop-aware hierarchical planning, achieving AR@1 of 89.9% and reducing exploration time by 15%.

Zhiwei Li, Haiou Liu, Xijun Zhao et al.

VISTA introduces a hybrid user simulator combining UI and API actions, with six metrics for realism and coverage, outperforming existing methods in diverse scenarios.

Yunan Lu, Ryan Shea, Yusen Zhang et al.

Proposes HiViG, a history-aware visually grounded test-time framework, boosting GUI task success rates by 5.8% (Qwen3-VL-32B) and 9% (Gemini-3-Flash) through macro-action history and visual error verification.

Jaewoo Lee, Zaid Khan, Archiki Prasad 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.

This paper introduces a language-driven adaptive cost optimization framework for autonomous driving, leveraging GPT-4 to interpret natural language queries and adjust MPPI control parameters in real-time.

Diego Martinez-Baselga, Khaled Mustafa, Javier Alonso-Mora

miniReranker employs visual cache reuse and interaction sparsity to reduce reranking runtime to <1% with >96% performance, based on Qwen3-VL.

Yingqi Fan, Xuan Lu, Anhao Zhao et al.

Introduces SkillResolve-Bench and SkillResolve, achieving Recall@3 0.766, NDCG@3 0.699, and HSR@3=0, effectively reducing same-capability ambiguity risks.

Jiandong Ding

MemoryVLA++ integrates memory and imagination for full temporal modeling, significantly improving robotic task success rates.

Hao Shi, Weiye Li, Bin Xie 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.

iMaC translates future robot actions into image controls, significantly improving spatial accuracy in video prediction and policy evaluation.

Zhenyu Wu, Xiuwei Xu, Yukun Zhou 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.

FASE employs graph-based semantic embeddings to approximate code correctness, achieving 25% higher correlation and only 0.3% of traditional computational cost.

Shizhe Lin, Ladan Tahvildari

POTATR is a lightweight 29M-parameter image-to-graph model that significantly improves page-level table extraction accuracy and efficiency.

Brandon Smock, Libin Liang, Max Sokolov 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.