SSRLive combines static and dynamic semantic IDs, raising online watch time by 3.38% in live-streaming recommendation.

Teng Shi, Zhaoheng Li, Yuanhang Qu et al.

FinEvolveBench tests delayed-feedback self-evolution on 31 Chinese A-share industries and 177,324 news articles; generic memory rarely improves IC.

Zihao Deng, Yining Zhu, Leiming Wang et al.

LIMMT uses physics feasibility, diversity, and complexity to select high-quality motion data, outperforming full datasets with only 3%.

Yu Guan, Zekun Qi, Chenghuai Lin et al.

This study compares declarative skill files and imperative state machines in AI tool use, showing that skills improve accuracy under high-quality knowledge retrieval.

M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen et al.

Proposes FDM framework using WCCS to optimize ML configurations for malware detection across diverse deployment scenarios.

Tadiwa Vhito, Jakapan Suaboot, Warodom Werapun et al.

Skill creation via RWSA decomposition; W2S improves replay consistency by 10.5%.

Yuyang Zhang, Xinyuan Han, Xudong Jiang et al.

Proposed a cross-view fusion framework significantly enhancing 6-DoF grasp pose estimation robustness, excelling on GraspNet-1Billion.

Kangjian Zhu, Haobo Jiang, Jianjun Qian et al.

CRAFT introduces bidirectional counterfactual reasoning, achieving 82.4% accuracy on WikiTQ and 94.6% on TabFact, outperforming baselines.

Chenshuo Pan, Yu Zhao, Jie Zhang et al.

AdMem framework integrates semantic, episodic, and procedural memory, enhancing LLM performance in long tasks.

Runzhe Wang, Huilin Lu, Shengjie Liu et al.

CaRE framework standardizes compute, metrics, and stochasticity in MDLM evaluation, revealing temperature and compute effects on strategy rankings.

Yash Shah, Abhijit Chakraborty, Vivek Gupta

OpenSkill framework enhances LLM agents' skill transfer in open-world settings without supervision, achieving top automated pass rates.

Zhiling Yan, Dingjie Song, Hanrong Zhang et al.

Introduces ShallowBench, a benchmark for evaluating generative drug models on low-concavity targets using Alpha Shape volume differences.

Saket Reddy, Shiwei Liu

Study co-trains robot manipulation policies using everyday human videos, achieving a 29.7% success rate improvement.

Richard Li, Aditya Prakash, Andrew Wen et al.

PoLar algorithm dynamically skips or repeats layers to enhance LLM inference efficiency and accuracy.

Ziyue Li, Yang Li, Tianyi Zhou

Proposes Code2LoRA, a hypernetwork-based method generating repository-specific adapters, achieving 63.8% cross-repo and 66.2% in-repo exact match on static tasks, and 60.3% on evolving codebases.

Liliana Hotsko, Yinxi Li, Yuntian Deng et al.

Introduces RP-Regret for adaptive opponents, with algorithms achieving sublinear regret and better equilibria in repeated games.

Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu et al.

PAR3D introduces part-aware 3D multimodal large language models, significantly enhancing fine-grained scene understanding via the ScenePart dataset.

Shaohui Dai, Yansong Qu, You Shen et al.

Proposes Complexity-Balanced Diffusion Splitting (CBS), using Dirichlet energy and trajectory acceleration to estimate local complexity, improving synthesis quality by ~35%.

Noam Issachar, Dani Lischinski, Raanan Fattal

Proposes Astra framework combining RL-trained VLM policy with Bagel-based world simulator for imagination-driven spatial reasoning, improving MMSI-Bench accuracy from 45.1% to 49.5%.

Chenming Zhu, Jingli Lin, Yilin Long et al.

MLEvolve is a self-evolving multi-agent framework using LLMs for end-to-end machine learning algorithm discovery, achieving 65.3% medal rate within 12 hours.

Shangheng Du, Xiangchao Yan, Jinxin Shi et al.