JanusMesh is a fast, zero-shot framework for generating dual-semantic 3D illusions using cross-space denoising, completing in 3-5 minutes with high geometric and semantic fidelity.

Siang-Ling Zhang, Huai-Hsun Cheng, Tsung-Ju Yang et al.

UNIEGO employs proxy-mediated hierarchical distillation from nine heterogeneous teachers to unify egocentric video representations, achieving state-of-the-art results.

Wenhao Chi, Arkaprava Sinha, Dominick Reilly et al.

Proposes Spatially Speculative Decoding (SSD), leveraging 2D spatial prediction to accelerate autoregressive image generation by up to 13.3×.

Shilong Xiang, Zirui Zhang, Lijun Yu et al.

CalTennis is a large multi-view tennis video dataset with over 11 million frames, used to evaluate monocular-to-3D pose estimation, highlighting challenges in depth and foot contact accuracy.

Ilona Demler, Xinran Xie, Blake Werner et al.

Proposes graph-bound execution-state capsules for low-latency, small-batch on-device AI, enabling byte-exact snapshot and restore with sub-millisecond GPU performance.

Liang Su

DeepSWIP leverages neural materialization and quotient WMC for exact single-world counterfactual inference in neural probabilistic logic programs, achieving a 2.14× speedup.

Saimun Habib, Vaishak Belle, Fengxiang He

Propose probe-and-refine tuning, an iterative method using synthetic bug probes to enhance repository guidance, improving mean resolve rate from 28.3% to 33.0% with Qwen3.5-35B.

Asa Shepard, Jeannie Albrecht

Integrating Genetic Algorithm and A* with AHP for multi-criteria path planning enhances continuum robot resilience by increasing path diversity and robustness.

Oxana Shamilyan, Ievgen Kabin, Zoya Dyka et al.

Proposes SSH-Net, a deep neural network integrated with cause-specific competing risks model, for failure time distribution prediction on GPU data, leveraging hierarchical data structures.

Jie Min, Yueyao Wang, Mengkun Chen

Proposes ARC: a unified block-coordinate descent framework combining adaptive robust loss, IRLS, and MWCD for joint state and covariance estimation, robust against outliers and non-Gaussian noise, with automatic parameter tuning.

Alexandre Hadji-Thomas, Andrew Stirling, James R. Forbes

ELVA employs a ranking-driven reinforcement learning framework with rule-based rewards, achieving 13.1% improvement on MRBench for multi-grain retrieval.

Yuhan Liu, Pei Fu, Hang Li et al.

Proposes the nine-dimensional Meaning Intelligence Framework (MIF) to distinguish surface sentiment from true intent in Nigerian discourse; zero-shot accuracy 33.3%, schema-guided 73.3%.

Celestine Achi

ScholarQuest introduces a taxonomy-guided benchmark with over 1000 CS topics, four research intents, and automated answer construction, advancing systematic evaluation of academic search agents.

Tingyue Pan, Mingyue Cheng, Daoyu Wang et al.

Proposed Multi-Agent Transactive Memory (MATM) enhances heterogeneous agent populations by sharing trajectories, improving success rate by 8% and reducing steps by 0.59 in interactive tasks.

To Eun Kim, Xuhong He, Dishank Jain et al.

Proposes a query-aware routing framework using lightweight regression models to optimize filtered ANN search, achieving superior recall-QPS balance across six datasets.

Qianqian Xiong, Mengxuan Zhang

Proposes Piecewise Polynomial Interpolation-based Gradient Descent (PPI-GD) achieving oracle complexity of O((p/ε)^{d/(2ℓ)}) for data dimension d=O(log^{0.49}(n)), outperforming classical GD/SGD.

Dongmin Lee, William Lu, Anuran Makur

Proposes Turing-RL, a reinforcement learning approach using discriminative Turing rewards to train human user simulators, outperforming traditional response matching methods.

Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu et al.

Proposes DO AS I DO framework that reconstructs human hand-object interactions from monocular RGB videos and retargets them to dexterous robots, outperforming state-of-the-art methods.

Bhawna Paliwal, Haritheja Etukuru, William Liang et al.

Analyzes observability and consistency of VINS with anchored feature parameterization, demonstrating improved estimator reliability through theoretical and experimental validation.

Mitchell Cohen, Vassili Korotkine, James Richard Forbes

This study compares webpage retraction, self-correction, and expert correction strategies, revealing that self-correction maintains credibility and enhances trust, especially when social connection is strong.

Biswadeep Sen, Yi-Chieh Lee