HAFS framework improves video quality by 1.5x and reduces response time by 31%.

Goodsol Lee, Juheon Yi, Jinglu Wang et al.

IDEAgent employs a multi-agent Quality-Diversity search framework to generate diverse, high-quality research ideas, outperforming baselines by 3.89× on Yield across 32 topics.

Varun Gumma, Navonil Majumder, Soumitra Sinhahajari et al.

StateAct leverages program state for long-horizon tasks, achieving 26.9% success, ninefold cost reduction, outperforming pixel-based methods.

Yan Yang, Xiangru Jian, Ziyang Luo et al.

Proposes deployment-feedback-driven continual learning using external memory, boosting τ-bench success rate by 1.6× single-trial, 2.6× with corrections.

Valentin Tablan, Scott Taylor, Kristoffer Bernhem

BrainAgent enhances brain network analysis accuracy through iterative reasoning and reflection.

Jiaxing Li, Rui Dong, Muyao Tang et al.

This paper derives power-law scaling laws for from-scratch vision-language pretraining, revealing distinct behaviors for language and multimodal objectives under fixed compute.

Haoyuan Wu, Aoqi Wu, Hai Wang et al.

Introduces MissionBench, a benchmark for zero-shot evaluation of 22 MLLMs on 120 aerial long-horizon tasks, with success rates below 35%.

Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt et al.

VIGOR uses reward variance to adaptively allocate rollouts, reducing sampling by up to 2.3× while maintaining performance.

Heyang Jiang, Henry Liu, Baharan Mirzasoleiman

MetaEvolve uses reinforcement learning to cultivate self-evolution meta-skills, boosting code task performance by over 10%.

Shujin Wu, Cheng Qian, Xiusi Chen et al.

DomainPilot employs token-level domain loss monitoring and a two-stage optimization to improve language model fine-tuning by +1.8% to +3.8%.

He Zhang

SCALE uses self-supervised layout text generation and rule-guided sampling to boost sub-2nm local DRV fixing accuracy up to 97%.

Chia-Tung Ho, Haoyu Yang, Guanglei Zhou et al.

Proposes Trans-Unet, combining 3D-to-2D mapping, CNN, and self-attention, achieving high-fidelity brain folding prediction from point clouds.

Geran Zhao, Xiaotian Li, Poorya Chavoshnejad et al.

ADSD introduces a prompt suffix attack exploiting Soft-Collapse to collapse verifier acceptance, reducing speculative decoding speed by over 60%.

Run Wang, Chaoyi Zhou, Xi Liu et al.

Introduces ConVBench for complex visual reasoning; uses GRPO reinforcement learning with consistency rewards to improve LVLM logical stability, achieving 73.36% consistency.

Liqiang Jing, Xiong Zhou, Siddharth Varia et al.

Proposes Zero-Flow Two-Sample Test (ZF2ST) with strong performance on synthetic and image datasets.

Yakun Wang, Leyang Wang, Song Liu et al.

DLMRec enhances recommender systems using discrete diffusion language models, significantly improving Recall and NDCG.

Chengyi Liu, Yongqi Zhou, Junwei Pan et al.

Exact excluded-pool audits certify missed mass; in the zero-miss regime, the optimal label complexity is Ω(N₀/m).

Martin Anthony, Kaveh Salehzadeh Nobari

DAPM model achieves UAV monocular depth estimation from any height, pitch, roll, and FOV, reaching state-of-the-art performance.

Tong Ling, Wenhui Diao, Yingchao Feng et al.

Proposes context-weighted discrete flow matching, reducing perplexity by 63% via local context-aware sampling and loss.

Daniil Cherniavskii, Daniel Severo, Karen Ullrich

M2S method predicts posterior mean and maps it to scores, improving generative PPL to 143.3.

Jingyuan Li, Xiaoyi Jiang, Yixuan Jiang et al.