Proposes Spoken Function Calling (SFC), leveraging structured rules to enhance large audio language models' semantic understanding beyond traditional closed-set SLU.

Yuezhang Peng, Yuxin Liu, Changfeng Gao et al.

Introduces LLM-guided symbolic regression (SR) framework for automated kinetic model discovery, reducing iteration count by up to 79.3%.

Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona

Single-shot structured light depth reconstruction using VQ-VAE and U-Net achieves 3.70mm MAE at 26Hz, enabling real-time laparoscopic guidance.

Wayne Wonseok Rodgers, Xiangyi Le, Seonghoon Jang et al.

This paper applies multidimensional Item Response Theory (IRT) to analyze 192 language models across 8 safety benchmarks, revealing three core latent abilities and enabling cost-effective evaluation and auditing.

Joshua Fonseca Rivera, Neil Shah, David Demitri Africa et al.

BridgeVLA++ integrates multi-view projection, explicit spatio-temporal memory, and pre-trained VLMs for efficient, robust 3D robot manipulation.

Peiyan Li, Yuze Zhu, Yixiang Chen et al.

Leveraging foundation vision models for cross-species animal pose tracking, achieving high accuracy with limited labels.

Le Li, Daniela Ivanova, Nicolas Pugeault

Marginal matching does not ensure factorized sampling; model leakage persists.

Duong Bach, Hai Nguyen Hong, Cuong Do

Introduces variational bounds for the finite-temperature perceptron trained on Gaussian mixtures, deriving upper and lower bounds via interpolation and log-concavity, with fixed point equations for energy and error estimates.

Francesco Camilli, Pierluigi Contucci, Federica Gerace et al.

MemoryCPT employs an end-to-end trainable memory framework combining QAD and QAR, achieving a QPC of 0.138 with significant cost reduction and answer quality improvement.

Songxin Lei, Kun Ouyang, Weilin Ruan et al.

OCSD compares matched replay views to calibrate environment feedback, significantly improving multi-task reinforcement learning performance.

Yi Yang, Cong Qin, Xiaodan Liu et al.

Proposes TriQua framework, combining base triples with qualifiers for fine-grained factuality evaluation, achieving high correlation (r=0.89) with human scores.

Jin Liu, Steffen Thoma, Achim Rettinger

MAGIC combines graph label propagation and geometric alignment to stabilize feature space in semi-supervised class-incremental learning, reducing drift and improving accuracy.

Yousef Abdi, Mohammad Asadpour, Yousef Seyfari

Masked Diffusion model improves beat tracking by modeling multiple plausible outputs, reducing incoherence and enhancing stability.

Francesco Foscarin, Filip Korzeniowski, Richard Vogl

ABD method achieves 59.43% accuracy on LiveCodeBench-v6, outperforming Single9 and HAC.

Ruitong Li, Binjie Guo, Aisheng Mo et al.

Sun develops the ISMIE framework, combining crowdsourcing surveys, neurophysiological signals, and preference modeling to analyze GenAI's impact on modern information seeking, revealing preference shifts and cognitive load dynamics.

Shuoqi Sun

EuroExec benchmark reveals frontier LLMs achieve only 56.9% solve rate on European executive tasks, far below expert performance.

Pau Arnal, Khaled Denfir, Danylo Smahliuk et al.

Introduces DiVers dataset with over 1.1 million music versions, enhancing robustness in real-world scenarios, significantly improving music version identification (VI) performance.

Simon Hachmeier, R. Oguz Araz, Dmitry Bogdanov et al.

Proposes a multi-objective ranking framework combining immediate and delayed signals with Segment-Aware targeting, boosting Twitch DAU by 0.09% and ARPU by 0.56%, with 41.9% fewer parameters.

Xiaoyi Gu, Julia Tavares, Eder Santana et al.

CommBench benchmarks LLMs for GPU communication code correctness and efficiency; GPT-5.5 achieves only 30.7% success on tasks.

Shuang Ma, Yuyi Li, Yihan Zhang et al.

ToolArtist employs post-trained UMM with RL and RAD-GRPO to dynamically coordinate reasoning, tool use, and image generation, outperforming fixed pipeline methods.

Jiahao Zhao, Xiaomin Yu, Zhongxiang Sun et al.