Proposes EmbedFilter, a linear transformation that filters the latent subspace encoding high-frequency, uninformative tokens, improving zero-shot text embedding performance by up to 14%.

Songhao Wu, Zhongxin Chen, Yuxuan Liu et al.

Using a task-based framework, real-world data from Perplexity shows AI agents significantly boost automation, efficiency, and task scope, with productivity gains of up to 87%.

Jeremy Yang, Kate Zyskowski, Noah Yonack et al.

Proposes CoMetaPNS, integrating continual Bayesian GMM with set-conditioned generative models for personalized cardiac electrophysiology simulation, achieving superior accuracy and anti-forgetting.

Ryan Missel, Xiajun Jiang, Linwei Wang

Proposes a physiologically constrained musculoskeletal neural network (MSK-NN) for multi-DoF joint kinematics estimation from partial sEMG, outperforming baseline models.

Wending Heng, Mingming Zhang, Glen Cooper et al.

This study introduces representation steering via Sparse AutoEncoders (SAE) and activation space manipulation to reduce Whisper's hallucination rate from 72.63% to 14.11%, without fine-tuning.

Georgii Aparin, Vadim Popov, Tasnima Sadekova et al.

TEVI leverages sparse autoencoders with text conditioning to refine image embeddings, significantly improving vision-language alignment and retrieval accuracy.

Sweta Mahajan, Sukrut Rao, Jiahao Xie et al.

Proposes Transferability and Predictability as extensions to ISO 26262, enhancing autonomous vehicle controllability and predictability with measurable metrics.

Chaitanya Shinde, Hadi Hajieghrary, Paul Schmitt et al.

This paper introduces a unified framework based on watching, remembering, and reasoning, significantly advancing long video understanding with multimodal LLMs.

Jiahao Meng, Yue Tan, Qi Xu et al.

Proposes a simulation-based imitation learning framework that automatically generates diverse reach-to-grasp demonstrations, achieving over 90% grasp success in real-world tests.

Kaijie Shi, Wanglong Lu, Huiling Chen et al.

This paper introduces LLM-guided MAP-Elites evolution for optimizing medical decision pipelines, improving accuracy and safety metrics significantly across tasks.

Ivan Sviridov, Artem Oskin, Ivan Panin et al.

AnchorWorld combines 3D human motion and view-based anchors for customizable, self-evolving egocentric scene simulation.

Yu Li, Menghan Xia, Gongye Liu et al.

MatMind unifies structure-activity reasoning in crystal materials science, surpassing specialized models.

Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu et al.

Rosetta Memory optimizes cross-LLM tasks with adaptive memory, showing significant performance improvements in experiments.

Hao Yang, Shiqi Shen, Haoxuan Li et al.

Proposes a Constrained Dominant Set (CDS) method for multimodal long-document QA, achieving 66.99 on VisDoMBench, surpassing previous SOTA by 37.1 points.

Ambuj Mehrish, Sebatiano Vascon

Improving LLM behavioral consistency using the Cognitive Kernel Model (CKM), reducing decision flip rate by 82%.

Gi-Hun Lee, Joong Yull Park

TOAD applies test-time trajectory search using learned reward functions, boosting six planners; NAVSIM-v2 reaches 56.3 EPDMS.

Yihong Xu, Eloi Zablocki, Yuan Yin et al.

SES framework optimizes symbolic solutions directly from equations without training data, successfully recovering algebraic and differential equations' explicit expressions.

Sergei Garmaev, Vinay Sharma, Olga Fink

MalSkillBench is a behavior-verified benchmark with 3944 malicious skills, covering attack vectors, behaviors, and strategies.

Wenbo Guo, Wei Zeng, Chengwei Liu et al.

SlimSearcher enhances training efficiency via adaptive reward gating, reducing tool calls by 17%-58%.

Zequn Xie, Junjie Wang, Dan Yang et al.

GuideCAD employs prefix embeddings to efficiently fuse visual-textual data, enabling lightweight 3D CAD model generation with only a quarter of the parameters of fine-tuning methods.

Minseong Kim, Jinyeong Park, Sungho Park et al.