Introducing RATS (Register Attention Transformers), which self-supervisedly discovers part-level structures with N learnable registers, achieving +12 mIoU on five segmentation benchmarks.

Timing Yang, Predrag Neskovic, Jansen Seheult et al.

Instruct-Particulate employs large-scale heterogeneous datasets and instruction-guided neural networks to efficiently predict 3D articulated structures, significantly improving generalization.

Ruining Li, Yuxin Yao, Matt Zhou et al.

WorkflowView leverages LLMs to abstract low-level action sequences into high-level activities with F1=0.90, demonstrating cross-domain generalization.

Gaurav Verma, Scott Counts

This study reveals that self-verification in vision-language models can cause regression when verifier quality is task-specific, supported by variance theorem analysis.

Jianzhe Lin

Proposes a method combining lexical token analysis and multi-word similarity to quantify cultural localization in AI-generated stories, revealing only 9-17% of vocabulary accounts for cultural differences.

Shaily Bhatt, Supriti Vijay, Jeremiah Milbauer et al.

Proposes MoE-RM-SRL, integrating reward machines, safe distance, and sparse gating experts, achieving safe and efficient highway autonomous driving.

Chufei Yan, Zhihao Cui, Yiyan Lv et al.

MeEvo combines natural evolution and metacognitive reflection through cyclic alternation, significantly improving search stability and solution quality on complex optimization tasks.

Zishang Qiu, Xinan Chen, Rong Qu et al.

Proposes a co-evolutionary SNN ensemble framework based on marginal contribution fitness, significantly improving multi-task performance.

Catherine Rodriquez, James Ghawaly

InterleaveThinker employs a multi-agent framework with a planner and critic, achieving high-quality interleaved text-image generation with step-wise reinforcement learning, improving performance on benchmarks by over 50%.

Dian Zheng, Harry Lee, Manyuan Zhang et al.

Proposes Modality Forcing, a post-training method enabling a single DiT model to jointly generate image and sparse depth data, achieving 57% reduction in AbsRel and scaling with model size.

Bardienus Pieter Duisterhof, Deva Ramanan, Jeffrey Ichnowski et al.

Flow Reversal Steering (FRS) leverages reverse flow models to map coarse actions into high-quality behaviors, boosting zero-shot control and rapid learning in robotic policies.

Andy Tang, William Chen, Andrew Wagenmaker et al.

SpatialClaw employs code as an action interface, achieving 59.9% average accuracy across 20 spatial reasoning benchmarks, outperforming recent models by 11.2%.

Seokju Cho, Ryo Hachiuma, Abhishek Badki et al.

Using large language models (e.g., Claude 4.7) for automated reproducibility assessment in social sciences, matching effect sizes within ±0.05 and supporting conclusions with high accuracy.

Tobias Holtdirk, Pietro Marcolongo, Anna Steinberg Schulten et al.

This paper analyzes the sparsity and geometric structure of on-policy distillation (OPD), revealing small, coordinate-sparse updates that are spectrally concentrated and deviate from source principal directions.

Guo Yu, Wenlin Liu, Yulan Hu et al.

Flex4DHuman employs relative camera-pose encoding within a diffusion framework to synthesize synchronized multi-view videos from monocular or sparse inputs, surpassing prior methods without explicit geometry priors.

Jen-Hao Cheng, Yipeng Wang, Hao Zhang et al.

Introduces operads as a formal framework for question decomposition, with operadic consistency correlating strongly with model accuracy across multiple datasets.

Nathaniel Bottman, Kyle Richardson

Proposed MCR-Bionic hand integrates anatomical structural priors with hydraulic artificial muscles, achieving dexterous manipulation via wrist-finger coupling and intrinsic muscle pathways.

Haosen Yang, Guowu Wei

EWSegNet combines spatial and spectral features for efficient waste segmentation in cluttered backgrounds, achieving high accuracy with low computational cost.

Mamoona Javaid, Mubashir Noman, Abdul Hannan et al.

CQC-RAG introduces cross-query consistency to enhance robustness in retrieval-augmented generation, outperforming baselines by +4.76 EM on TriviaQA and +9.12 EM on MuSiQue.

Yanjia Sun, Sifan Liu, Jie Shao

SupraSNN employs a superscalar-inspired architecture with synapse-level parallelism, achieving 149μs latency and 0.025mJ/image on FPGA for MNIST, outperforming prior accelerators.

Seyed Sadra Ghavami, Mohammad Hossein Nikkhah, Mohammad Rasoul Roshanshah et al.