LifeVal is a new benchmark that evaluates AI model performance on personalized agentic consumer tasks, covering over 1.7 trillion US dollars in annual willingness-to-pay across 148 task categories such as managing finances and finding childcare. The benchmark was built using a survey of more than 3,800 consumers and cross‑referenced with national time‑use studies. Task creators, who met strict expertise thresholds, designed AI evaluation tasks personalized with real ChatGPT conversations, memories, reference files, and personal context, and all tasks underwent at least three rounds of human quality control before inclusion.
By Jenny Yang, Elizabeth Proehl, Anjan Bharadwaj, Emily Fan, Charlotte Cole, James Kim, Noam Brown, Phoebe Thacker, Amelia Glaese, Tejal Patwardhan
Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments.
arXiv:2608. 10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants.
By Xiaohongshu Inc
The paper presents a systematic study of token consumption in AI agents performing coding tasks. It finds that agentic tasks are far more expensive—about 1000 times more tokens than code reasoning or chat—primarily due to input tokens, and that token usage varies wildly, with accuracy peaking at moderate costs. Models differ significantly in efficiency, and current frontier models cannot reliably predict their own token usage, often underestimating it.
By Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei
The paper introduces Active Preference Acquisition (APA) for long‑term personalized web agents, defining the Prospective Personalization Value (PPV) of a missing preference as its expected cumulative utility over future tasks minus the cost of asking. APA selects questions within a per‑interaction budget, prioritizing preferences that may not benefit the current task but can be reused across many future tasks. Experiments on the ActiveWebPref benchmark show APA acquiring 80.3% of amortization‑dependent preferences and achieving higher net utility than baseline methods, while limiting the number of questions asked.
The paper introduces Active Preference Acquisition (APA), a method for web agents to decide which unknown user preferences to ask about by evaluating the Prospective Personalization Value (PPV)—the expected cumulative benefit across future tasks minus the cost of asking. APA calculates counterfactual gains for each candidate preference using task prototypes and aggregates them by expected reuse, enabling agents to prioritize questions that may not help immediately but improve long‑term performance. Experiments on the ActiveWebPref benchmark show APA can acquire a large share of valuable preferences and achieve higher net utility than baseline strategies, demonstrating that considering future reuse is crucial for effective personalization.
By Yiqi Wang, Zizhi Zeng, Jiaqi Zhang, Yiqun Duan, Mingkai Zheng, Taotao Cai
The paper introduces Test-Time Adaptation through Human‑Agent Interaction (TAHI), a method that uses iterative human feedback to adapt AI agents to individual users’ criteria. By integrating cross‑session interaction data into agent context and weights, and building an evolving rubric module, the authors demonstrate that agents can improve task success by 4.5–20.9% after only a few interactions. The evolving rubric also serves as a scalable annotation tool, detecting 16.0–22.3% more failures than language models or humans alone, and personalized agents can even generalize improvements up to 8.8% across users.
By Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao, Aspen Chen, Jonas Mueller, Zhiqi Liang, Jett Chen, Michael Ryan, Qianou Ma, Luxi He, Zhoujun Cheng, Andre He, Seungone Kim, Jiayi Geng, Mingqian Zheng, Weiwei Sun, Zheyuan Zhang, Xinran Zhao, Yike Wang, Abe Hou, Liwei Jiang, Pang Wei Koh, Diyi Yang, Graham Neubig, Daniel Fried
arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.
By Simiao Zuo, Chenhui Xu, Yimeng Jia, Qiang Lou, Jian Jiao, Denis Charles
arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
By Jingxuan Han, Wei Liu, Mingyang Zhu, Youpeng Wang, Ziwen Wang, Lin Qiu, Xuezhi Cao, Xunliang Cai, Zheren Fu, Licheng Zhang, Zhendong Mao
PersonaMem-v3 is a benchmark and evaluation harness designed to assess omni-platform personal intelligence for AI agents. It is built from over one million anonymized real-world engagement histories, covering social media, chatbots, calendars, and AI companions, and tracks user preferences and habits over time. The benchmark tests agents on personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning, evaluating their ability to infer holistic user understanding, personalize responses, rerank recommendations, follow user steering, and avoid inappropriate personalization.
By Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell, Chris Callison-Burch, Lyle Ungar, Dan Roth, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu
xDailyBench is a new benchmark comprising 248 tasks across 51 real‑life scenarios, designed to evaluate large language models on everyday professional consultation. The tasks are based on actual user requests and assessed with detailed binary rubrics that capture both explicit instructions and implicit needs inferred from context. In tests of 11 leading models, the best achieved a 75.6% task‑level score, yet all models struggled more with implicit requirements, showing gaps of at least 9 percentage points.
By Yongchang Peng, Qingshui Gu, Liya Zhu, Ge Zhang, Duo Wang, Haodong Wang, Jingzhe Ding, Tianhao Yu, Letian Gao, Yongjie Zhong, Chaoxin Li, Zixin Su, Jinchao Tao, Xingyu Ma, Xin'ao Guo, Feng Tian, Shiyuan Dong, Xiaoyan He, Sen Liu, Xin Chen, Jiajun Li, Zejia Zhang, Xi Lin, Wen Zhang, Yi Zhu, Duju Zeng, Xiang Gao, Yunyang Wang, Jiahao Wang, Yujia Qin, Jiaheng Liu, Shen Yan, Xiaolong Chang, Wenhao Huang
arXiv:2608. 13940v1 Announce Type: new Abstract: AI research agents (AIRA) can now propose, implement, and evaluate their own machine learning experiments, but progress on frontier tasks is throttled by cost: a candidate solution can be written in minutes, whereas evaluating it can take hours to days of GPU time.
By Thomas Simon Foster, Bassel Al Omari, Tingchen Fu, Thomas Mann, Carl Domond, Lucia Cipolina-Kun, Bhavul Gauri, Muna Aghamelu, Alexander D. Goldie, Eryk Helenowski, Jean-Christophe Gagnon-Audet, Alberto Pepe, Saba Nazir, Daniel Izcovich, Noam Levi, Rishi Hazra, Karen Hambardzumyan, Nicolas Baldwin, Xian Li, Martin Josifoski, Paris Giampouras, Masoud Jalili Sabet, Anya Sims, Hela Momand, Tatiana Shavrina, Despoina Magka, Jason Weston, Yulin Wang, Anirudh Goyal, Jo\~ao Henriques, Yoram Bachrach, Emily McMilin, Jakob Nicolaus Foerster