ENTRAP-VL uses a dual-modality probe to study contextual entrainment in vision-language models with a 1,500-item dataset.

Karan Goyal, Afreen Hossain, Debojyoti Das et al.

Proposes two reliability-aware distillation methods, CHAD and EWAD+CPDP, outperforming standard KD with +0.0219 ROUGE-L on low-resource summarization.

Dipto Sumit, Ankan Kumar Roy Srizon, Sadia Khair Rodela et al.

Empirical study using YAHMP framework shows how modeling and training choices impact humanoid motion tracking performance, validated on Unitree G1.

Fabio Amadio, Enrico Mingo Hoffman

Introduces CAMA-BC with multi-layer multimodal alignment, achieving 78.5% F1 in backchannel prediction, especially for complex cues like empathy.

Min-Jae Kim, Jun-Yeong Moon, Mujeen Sung et al.

Proposes Sentence Splitter, a T5-based model that automatically identifies factual sentence boundaries, improving knowledge graph completion and QA by 4-6%.

Ahmad Pouramini, Mahsa Afsharizadeh

Trace environment uses scene grammar and executable task programs to enhance multi-domain visual reasoning, improving transferability by 3.5-4% on external benchmarks.

Md Tanvirul Alam

RPPNet generates long-structure melodies via boundary-aware modeling, enhancing musicality and structure.

Tieyao Zhang, Yuke Liu, Jiaxing Yu et al.

Proposes a multi-view panoptic segmentation method leveraging implicit geometric correspondence in large view synthesis models, achieving 33.56dB PSNR and 0.5949 mIoU without 3D reconstruction.

Kwonyoung Ryu, In-Jae Lee, Jonghyun Jin et al.

Knowledge-centric self-improvement uses a curated knowledge base to enhance task solving and transferability, reducing costs by 30%.

Xuefei Julie Wang, Lauren Hyoseo Yoon, Chengrui Qu et al.

Proposes streaming detection of AI-generated videos from compressed bitstream using motion field analysis, achieving anytime-valid inference with five orders of magnitude lower compute than pixel CNNs.

Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan Kurban

Introduces 'Speculative Correction' to enhance decoding efficiency and accuracy in diffusion language models.

Brian K Chen, Chong Wu, Kenji Kawaguchi

This paper introduces Riemannian flow matching in VGGT's latent space for 3D scene generation, outperforming baselines with improved geometric consistency.

Lisa Weijler, Irene Ballester, Guofeng Mei et al.

Proposes a geometric contrastive pretraining framework for GNNs, improving TSP solutions by 6.57% on 1000-node instances.

David Aguado, Daniel Fuertes, Carlos R. del-Blanco et al.

COPD enhances student model reasoning efficiency by using contrastive teaching signals to reduce reasoning length.

Jiacheng Ruan, Jun Tang, Wenzhen Yuan et al.

Self-SiMS uses video self-similarity for zero-shot proposals, reaching 42.2 [email protected] on QVHighlights test.

Jihyun Lee, Cheol-Ho Cho, Woojin Jun et al.

TARA employs type-aware repair allocation, boosting semantic accuracy by +5.6 points on DSG, +2.6 on TIFA, with no retraining needed.

Haoyue Liu, Xiaoyu Ma, Ye Chen et al.

Proposes Geo-DConv, integrating microphone geometry into dynamic convolution for array-invariant speech enhancement.

Zhenglong Liu, Wangyou Zhang, Chenda Li et al.

This paper introduces a perception-guided framework for single-view 3D reconstruction, achieving up to 32.7% reduction in Chamfer distance and significant metric improvements.

Y Huynh, Duc Thanh Nguyen, Mohamed Abdelrazek

Using XGB-C2ST to reveal dependency deficits in TabbyFlow and TabDiff models.

Jie Zhang

Proposes a new method for evaluating personal LLM agents under temporal interventions; no existing protocol meets all four conditions.

Pin Qian, Su Wang, Yihang Chen et al.