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Received — 27 May 2026 ⏭
Received — 8 April 2026 ⏭

From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junhao Ye · Yuchen Hu · Ke Xu · Dingrong Pan · Qichun Chen · Jie Zhou · Shuai Zhao · Xinwei Fang · Xi Wang · Nan Guan · Zhe Jiang
7 April 2026 at 12:00
arXiv:2504.19959v4 Announce Type: cross Abstract: Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise from the considerable manual coding effort required, repetitive manual execution of multiple EDA tools, and the need for in-depth domain expertise to navigate complex designs.Here, we present UVM^2, an automated verification framework that leverages Large Language Models (LLMs) to generate UVM testbenches and iteratively refine them using coverage feedback, significantly reducing manual effort while maintaining rigorous verification standards.To evaluate UVM^2, we introduce a benchmark suite comprising Register Transfer Level (RTL) designs of up to 1.6K lines of code.The results show that UVM^2 reduces testbench setup time by up to UVM^2 compared to experienced engineers, and achieve average code and function coverage of 87.44% and 89.58%, outperforming state-of-the-art solutions by 20.96% and 23.51%, respectively.

MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi Feng · Chen Huang · Zhibo Man · Ryner Tan · Long P. Hoang · Shaoyang Xu · Wenxuan Zhang
7 April 2026 at 12:00
arXiv:2602.13458v2 Announce Type: replace-cross Abstract: Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent-agent social interaction. Prior work has examined multi-agent behavior primarily in controlled or small-scale settings, limiting our understanding of emergent social dynamics at scale. The recent emergence of MoltBook, a social networking platform designed explicitly for AI agents, presents a unique opportunity to study whether and how these interactions reproduce core human social mechanisms. We present MoltNet, a dataset tracking the full one-month activity trajectories of 148K AI agents on MoltBook (Jan.-Feb., 2026), and analyze their social interaction along four theory-grounded dimensions: \textit{intent and motivation}, \textit{norms and templates}, \textit{incentives and drift}, \textit{emotion and contagion}. Our analysis reveals that agents respond strongly to social rewards, converge on community-specific norms, and actively enforce them across community boundaries -- resembling human incentive sensitivity and normative conformity. However, they exhibit weak alignment with declared personas and display limited emotional reciprocity and dialogic engagement, diverging systematically from human online communities. These findings establish a first empirical portrait of agent social behavior at scale, with direct implications for the design and governance of AI-populated communities.
Received — 2 April 2026 ⏭

Targeting sialic acid metabolism: a therapeutic strategy against gastric cancer driven by WZ35

✇Omics in Gastric
By: Boxiang Hong · Ying Wang · Xinran Feng · Lihan Wei · Yichen Huang · Xiawei Zhang · Longyuchen Xu · Pinyan Jin · Liyuan Huang · Rui Li · Xinyu Wu · Zhi Li · Hangyu Ye · Tongke Chen · Xiaoyan Wang · Hao Zhang
23 March 2026 at 18:00

Cell Oncol (Dordr). 2026 Mar 23;49(2):60. doi: 10.1007/s13402-026-01194-6.

ABSTRACT

Glycolytic reprogramming is closely associated with the occurrence and progression of gastric cancer. Specifically, the energy derived from glucose metabolism and the cellular proteins by its intermediate products influence gastric cancer development. However, as an important branch of glucose metabolism, sialic acid metabolism and its mediated sialylation modifications remain insufficiently studied in gastric cancer, and their specific relationship with malignant tumor progression requires further exploration. This study employed a multi‑omics approach, integrating metabolomics, single‑cell RNA sequencing, and bulk RNA sequencing analyses, to investigate the metabolic landscape of gastric cancer and its associated alterations. The results indicated that sialic acid is a characteristic metabolite in malignant gastric cancer tissues. It modulates biological functions such as immune response, proliferative activity, and metabolic remodeling within gastric cancer tissues by influencing sialylation modifications. Furthermore, we identified the drug WZ35, which can inhibit the malignant proliferation of gastric cancer by targeting both sialic acid metabolism and sialylated protein modifications. We put forward a conjecture that the metabolism and modification of sialic acid promote the malignant development of gastric cancer, and we discovered that the drug WZ35 has an inhibitory effect on the sialic acid metabolism of gastric cancer.

GRAPHICAL ABSTRACT:

PMID:41870836 | PMC:PMC13009457 | DOI:10.1007/s13402-026-01194-6

Received — 26 March 2026 ⏭
Received — 14 March 2026 ⏭

B cell imprinting in children impairs antibodies to the haemagglutinin stalk

✇Nature - Issue - nature.com science feeds
By: Jiayi Sun · Gyunghee Jo · Chloe A. Troxell · Yanbin Fu · Robert Hoezl · Huibin Lv · Hassanein H. Abozeid · Qi Wen Teo · Tossapol Pholcharee · Joshua J. C. McGrath · Siriruk Changrob · Sean A. Nelson · Atsuhiro Yasuhara · Min Huang · Nai-Ying Zheng · Jordan C. Chervin · Lei Li · Monica L. Fernández-Quintero · Johannes R. Loeffler · Alesandra J. Rodriguez · Jiachen Huang · Olivia M. Swanson · Angel Balmaseda · Guillermina Kuan · Lora Campredon · E. Kaitlynn Allen · Gabriele Neumann · Nicholas C. Wu · Yoshihiro Kawaoka · Florian Krammer · Asuncion Mejias · Octavio Ramilo · Paul G. Thomas · Aubree Gordon · Andrew B. Ward · Julianna Han · Patrick C. Wilson
11 March 2026 at 08:00

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10248-6

Imprinting by influenza viruses can cause a deleterious shift of nearly the entire memory recall response against key, conserved epitopes.
Received — 5 March 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Perfect score on IPhO 2025 theory by Gemini agent Yichen Huang
    arXiv:2603.03352v1 Announce Type: cross Abstract: The International Physics Olympiad (IPhO) is the world's most prestigious and renowned physics competition for pre-university students. IPhO problems require complex reasoning based on deep understanding of physical principles in a standard general physics curriculum. On IPhO 2025 theory problems, while gold medal performance by AI models was reported previously, it falls behind the best human contestant. Here we build a simple agent with Gemini
     
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Perfect score on IPhO 2025 theory by Gemini agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yichen Huang
5 March 2026 at 13:00
arXiv:2603.03352v1 Announce Type: cross Abstract: The International Physics Olympiad (IPhO) is the world's most prestigious and renowned physics competition for pre-university students. IPhO problems require complex reasoning based on deep understanding of physical principles in a standard general physics curriculum. On IPhO 2025 theory problems, while gold medal performance by AI models was reported previously, it falls behind the best human contestant. Here we build a simple agent with Gemini 3.1 Pro Preview. We run it five times and it achieved a perfect score every time. However, data contamination could occur because Gemini 3.1 Pro Preview was released after the competition.
Received — 25 February 2026 ⏭

Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ailin Huang · Ang Li · Aobo Kong · Bin Wang · Binxing Jiao · Bo Dong · Bojun Wang · Boyu Chen · Brian Li · Buyun Ma · Chang Su · Changxin Miao · Changyi Wan · Chao Lou · Chen Hu · Chen Xu · Chenfeng Yu · Chengting Feng · Chengyuan Yao · Chunrui Han · Dan Ma · Dapeng Shi · Daxin Jiang · Dehua Ma · Deshan Sun · Di Qi · Enle Liu · Fajie Zhang · Fanqi Wan · Guanzhe Huang · Gulin Yan · Guoliang Cao · Guopeng Li · Han Cheng · Hangyu Guo · Hanshan Zhang · Hao Nie · Haonan Jia · Haoran Lv · Hebin Zhou · Hekun Lv · Heng Wang · Heung-Yeung Shum · Hongbo Huang · Hongbo Peng · Hongyu Zhou · Hongyuan Wang · Houyong Chen · Huangxi Zhu · Huimin Wu · Huiyong Guo · Jia Wang · Jian Zhou · Jianjian Sun · Jiaoren Wu · Jiaran Zhang · Jiashu Lv · Jiashuo Liu · Jiayi Fu · Jiayu Liu · Jie Cheng · Jie Luo · Jie Yang · Jie Zhou · Jieyi Hou · Jing Bai · Jingcheng Hu · Jingjing Xie · Jingwei Wu · Jingyang Zhang · Jishi Zhou · Junfeng Liu · Junzhe Lin · Ka Man Lo · Kai Liang · Kaibo Liu · Kaijun Tan · Kaiwen Yan · Kaixiang Li · Kang An · Kangheng Lin · Lei Yang · Liang Lv · Liang Zhao · Liangyu Chen · Lieyu Shi · Liguo T
24 February 2026 at 13:00
arXiv:2602.10604v2 Announce Type: replace-cross Abstract: We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.
Received — 17 February 2026 ⏭

MoltNet: Understanding Social Behavior of AI Agents in the Agent-Native MoltBook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yi Feng · Chen Huang · Zhibo Man · Ryner Tan · Long P. Hoang · Shaoyang Xu · Wenxuan Zhang
17 February 2026 at 13:00
arXiv:2602.13458v1 Announce Type: cross Abstract: Large-scale communities of AI agents are becoming increasingly prevalent, creating new environments for agent-agent social interaction. Prior work has examined multi-agent behavior primarily in controlled or small-scale settings, limiting our understanding of emergent social dynamics at scale. The recent emergence of MoltBook, a social networking platform designed explicitly for AI agents, presents a unique opportunity to study whether and how these interactions reproduce core human social mechanisms. We present MoltNet, a large-scale empirical analysis of agent interaction on MoltBook using data collected in early 2026. Grounded in sociological and social-psychological theory, we examine behavior along four dimensions: intent and motivation, norms and templates, incentives and behavioral drift, emotion and contagion. Our analysis revealed that agents strongly respond to social rewards and rapidly converge on community-specific interaction templates, resembling human patterns of incentive sensitivity and normative conformity. However, they are predominantly knowledge-driven rather than persona-aligned, and display limited emotional reciprocity along with weak dialogic engagement, which diverges systematically from human online communities. Together, these results reveal both similarities and differences between artificial and human social systems and provide an empirical foundation for understanding, designing, and governing large-scale agent communities.
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