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Received today — 7 October 2026 ⏭

Immunotherapy for Digestive System Cancers: Progress, Challenges, and Future Directions

✇Omics in Hepatocellular
By: Keran Sun · Hongru Li · Hao Chi · Yuxuan Song · Yunze Niu · Jingyuan Ning · Hengrui Liu
26 September 2026 at 18:00

Biomedicines. 2026 Aug 27;14(9):1919. doi: 10.3390/biomedicines14091919.

ABSTRACT

Immune checkpoint blockade has changed the management of several digestive system cancers, but its impact is highly context dependent. This review evaluates evidence for esophageal, gastric and gastroesophageal junction, colorectal, hepatocellular, biliary tract and gallbladder, and pancreatic cancers. Randomized phase III trials have established chemoimmunotherapy or dual-checkpoint strategies in advanced esophageal cancer, biomarker- and regimen-dependent first-line therapy in gastric cancer, PD-1-based therapy for MSI-H/dMMR colorectal cancer, atezolizumab-bevacizumab and STRIDE for unresectable hepatocellular carcinoma, and chemoimmunotherapy for advanced biliary tract cancer. Recent results also expand perioperative treatment: neoadjuvant checkpoint blockade produces high pathological response rates in dMMR colon cancer, adjuvant atezolizumab plus mFOLFOX6 improves disease-free survival in stage III dMMR colon cancer, and perioperative serplulimab improves event-free survival in PD-L1-positive resectable gastric cancer. These advances coexist with important negative findings. Pembrolizumab-containing therapy did not meet superiority end points in KEYNOTE-062, the initial adjuvant signal in IMbrave050 was not sustained, and unselected pancreatic ductal adenocarcinoma remains largely resistant to checkpoint blockade. Early vaccine, cellular, TIGIT, radiomics, spatial, and multi-omics studies remain hypothesis-generating and require external or randomized validation. Clinical interpretation should integrate evidence maturity, biomarker validity, immune-related toxicity, patient-reported outcomes, cost, access, and manufacturing demands rather than response rate alone.

PMID:42792661 | PMC:PMC13604390 | DOI:10.3390/biomedicines14091919

Immunotherapy for Digestive System Cancers: Progress, Challenges, and Future Directions

✇(Multiomics OR Omics) AND (Pancreatic)
By: Keran Sun · Hongru Li · Hao Chi · Yuxuan Song · Yunze Niu · Jingyuan Ning · Hengrui Liu
26 September 2026 at 18:00

Biomedicines. 2026 Aug 27;14(9):1919. doi: 10.3390/biomedicines14091919.

ABSTRACT

Immune checkpoint blockade has changed the management of several digestive system cancers, but its impact is highly context dependent. This review evaluates evidence for esophageal, gastric and gastroesophageal junction, colorectal, hepatocellular, biliary tract and gallbladder, and pancreatic cancers. Randomized phase III trials have established chemoimmunotherapy or dual-checkpoint strategies in advanced esophageal cancer, biomarker- and regimen-dependent first-line therapy in gastric cancer, PD-1-based therapy for MSI-H/dMMR colorectal cancer, atezolizumab-bevacizumab and STRIDE for unresectable hepatocellular carcinoma, and chemoimmunotherapy for advanced biliary tract cancer. Recent results also expand perioperative treatment: neoadjuvant checkpoint blockade produces high pathological response rates in dMMR colon cancer, adjuvant atezolizumab plus mFOLFOX6 improves disease-free survival in stage III dMMR colon cancer, and perioperative serplulimab improves event-free survival in PD-L1-positive resectable gastric cancer. These advances coexist with important negative findings. Pembrolizumab-containing therapy did not meet superiority end points in KEYNOTE-062, the initial adjuvant signal in IMbrave050 was not sustained, and unselected pancreatic ductal adenocarcinoma remains largely resistant to checkpoint blockade. Early vaccine, cellular, TIGIT, radiomics, spatial, and multi-omics studies remain hypothesis-generating and require external or randomized validation. Clinical interpretation should integrate evidence maturity, biomarker validity, immune-related toxicity, patient-reported outcomes, cost, access, and manufacturing demands rather than response rate alone.

PMID:42792661 | PMC:PMC13604390 | DOI:10.3390/biomedicines14091919

Received — 10 September 2026 ⏭

No Free Checker: A Survey of Verifiers for Robot Policies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Wan · Xihang Yue · Zhirui Liu · Ziyuan Chu · Shuxun Wang · Yuhan Chen · Xiaonan Jiang · Xukun Zhu · Yubo Dong · Linchao Zhu
10 September 2026 at 12:00
arXiv:2609.09250v1 Announce Type: cross Abstract: A verifier for robot policies reads a candidate behavior and returns a score for how well it did, used both to evaluate vision-language-action policies and to train them. Verifiers range from success detectors and reward models to runtime monitors, safety filters, and temporal-logic specifications. We survey roughly 150 verifiers and compare them along two properties. Availability is how much a verdict costs, how early in a rollout the verdict arrives, and how often a verdict can be asked for. Availability rises as verdicts get cheaper, earlier, and denser. Credibility is how much a high score tells us about the task. Credibility falls as the judgment becomes gameable and self-serving. We group the verifiers by who supplies the judgment: human verifiers, rule-based and formal verifiers, learned and pretrained verifiers, and model-intrinsic verifiers. Across the four families, we find that credibility falls as availability rises. Regardless of who supplies the judgment, there is no free checker. We then examine what validates a verifier itself, and how much a high score tells us. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. We close with nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built.

Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Liu · Tao Zhe · Yanyong Huang · Sankha Narayan Guria · Xiao Luo · Wei Fan · Yanjie Fu · Dongjie Wang
10 September 2026 at 12:00
arXiv:2609.10225v1 Announce Type: cross Abstract: Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformation knowledge into continuous embedding spaces for efficient exploration of candidate strategies, but face three key limitations: (1) overlooking hierarchical relationships between low-level features, operations, and high-level abstractions; (2) enforcing order-sensitive embeddings on inherently permutation-invariant transformation sequences, thereby introducing systematic bias; and (3) relying on gradient-based search, which is ill-suited to non-convex transformation spaces. We propose a framework with two complementary components. First, a permutation-invariant hierarchical module captures interactions across features, operations, and abstraction levels, with a self-attention pooling mechanism that maps semantically equivalent structures to consistent embeddings aligned with downstream performance. Second, a policy-guided multi-objective reinforcement learning strategy initializes the search from empirically strong seeds and jointly optimizes predictive accuracy and transformation efficiency. Extensive experiments on diverse tabular benchmarks demonstrate the effectiveness and robustness of our framework against strong baselines. Our code and data are publicly available at: https://github.com/RayLiu1103/PHER.

City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Liu · Steven Jige Quan · Zhong-Ren Peng · Zijun Yao · Han Wang · Pengyang Wang · Kunpeng Liu · Yanjie Fu · Dongjie Wang
10 September 2026 at 12:00
arXiv:2602.19326v3 Announce Type: replace-cross Abstract: Urban renewal requires incremental modifications to existing geospatial plans, yet manually updating complex layouts under spatial constraints is labor-intensive and error-prone. To tackle this, we propose CEAE, a hierarchical agentic framework that formulates urban renewal as machine-executable GeoJSON editing from natural-language instructions. CEAE decomposes instructions into hierarchical geometric intents, executing edits from coarse to fine while preserving spatial consistency through a self-reflective execution-validation loop. Experimental results show that CEAE outperforms baselines in execution validity, robustness, and geometric accuracy.
Received — 27 May 2026 ⏭

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xirui Liu · Sihang Zhou · Yanning Hou · Rong Zhou · Haoyuan Chen · Maolin He · Siwei Wang · Hao Chen · Jian Huang
26 May 2026 at 12:00
arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: abstract representations lose executability on concrete pages, while concrete representations fail to generalize across domains. This entanglement limits capability accumulation: on new websites, agents either fail to recognize reusable task logic due to surface-level differences or attempt infeasible actions from outdated page structures. To disentangle them, we propose DRIVE, a dual-level skill modeling framework separating historical experience into natural language reasoning skills, which capture transferable task logic, and programmatic interaction skills, grounding abstract actions to executable operations. A scene-aware coordination mechanism adaptively retrieves and invokes these dual-level skills based on task semantics. DRIVE also uses skill-level reflection to identify hierarchy-specific failure modes, enabling targeted skill library expansion and refinement. Experiments across five WebArena domains show DRIVE attains an average task success rate of 52.8%, exceeding the skill-free baseline by 7.3 percentage points. Further ablations show reasoning and interaction skills provide distinct, complementary benefits, supporting separation of transferable task logic from executable page-level operations.

GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junjie Zhao · Jingyi Liang · Zhenyang Cai · Jiaming Zhang · Zhenwei Wen · Shuzhi Deng · Wenjing Yi · Chunfeng Luo · Hexian Zhang · Junying Chen · Tianrui Liu · Zhuhui Bai · Zixu Zhang · Pradeep Singh · Xiang Liu · Jianquan Li · Nhan L Tran · Falk Schwendicke · Zuolin Jin · Lijian Jin · Liangyi Chen · Wei-fa Yang · Benyou Wang · Junwen Wang · Shan Jiang
26 May 2026 at 12:00
arXiv:2605.24636v2 Announce Type: new Abstract: While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplored, particularly in dentistry. Here we introduce GlobalDentBench, the first multinational dental benchmark, featuring a taxonomy that encompasses 14 dental specialties across 88 countries and regions spanning six continents. The benchmark comprises 8,978 expert-validated questions across three formats (multiple-choice, short-answer, and case-based questions) and assesses three progressive reasoning levels: knowledge recall (L1), routine reasoning (L2), and individualized reasoning (L3). To ensure data quality, the automated construction framework was calibrated by six senior dentists, achieving expert agreement rates of 99.98% for multiple-choice and short-answer questions and 96.78% for the more complex case-based questions. Evaluation of 12 frontier LLMs on GlobalDentBench revealed a sharp, stepwise performance degradation with increasing reasoning complexity. Specifically, accuracy plummeted from 81.34% on multiple-choice to 64.53% on short-answer and 22.34% on case-based questions, while declining markedly from 74.01% at L1 to 55.64% at L2 and 35.71% at L3. More critically, risk analysis of real-world dental cases demonstrated an alarming overall unsafe rate of 31.01% in LLM-generated clinical recommendations, with 4.51% posing risks of irreversible patient harm and risks particularly pronounced in specialties such as orthodontics. These findings expose fundamental limitations in the medical reasoning and safety of current LLMs. Consequently, GlobalDentBench provides a scalable foundation for trustworthy clinical AI evaluation, underscoring the urgent need for rigorous validation before the safe deployment of these models in healthcare.

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

✇Nature - Issue - nature.com science feeds
By: Min He · Jia Su · Xiaogang Zhou · Tuo Qi · Jiazheng Wang · Tianxin Zhang · Jinhua Chen · Mawsheng Chern · Youpin Xu · Xiang Lu · Qingqing Hou · Hongrui Liu · Wenfang Huang · Jiawei Liu · Shiying Li · Yunfei Zhu · Xue Chen · Senyu Ran · Han Li · Renju Liu · Mingliang Lei · Guosong Shu · Haicheng Liao · Junjie Yin · Xiaobo Zhu · Yongyan Tang · Li Song · Long Wang · Qing Xiong · Jiali Liu · Yu Bi · Yihua Yang · Hai Qing · Bingtian Ma · Yan Li · Jing Fan · Peng Qin · Yuping Wang · Jianming Wu · Wenming Wang · Shigui Li · Weitao Li · Jing Wang · Xuewei Chen
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

Pixelated quantum-dot superlattice LEDs

✇Nature - Issue - nature.com science feeds
By: Chengxi Zhang · Qingsen Zeng · Hui Li · Renjun Guo · Yue Yu · Linjie Dai · Lyudmila Turyanska · Zirui Liu · Jun Dai · Yingguo Yang · Yue Zhao · Jun Lu · Lin Wang · Lingmei Kong · Tze Chien Sum · Yuchen Wu · Tae-Woo Lee · Xuyong Yang
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10392-z

Scalable fabrication of ordered perovskite quantum dot superlattices enables high-efficiency, ultrahigh-resolution LEDs and active-matrix displays with greatly improved brightness, stability and device lifetime.
Received — 4 April 2026 ⏭

ATBench: A Diverse and Realistic Trajectory Benchmark for Long-Horizon Agent Safety

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu Li · Haoyu Luo · Yuejin Xie · Yuqian Fu · Zhonghao Yang · Shuai Shao · Qihan Ren · Wanying Qu · Yanwei Fu · Yujiu Yang · Jing Shao · Xia Hu · Dongrui Liu
3 April 2026 at 12:00
arXiv:2604.02022v1 Announce Type: new Abstract: Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step interactions rather than isolated prompts or final responses. Existing trajectory-level benchmarks remain limited by insufficient interaction diversity, coarse observability of safety failures, and weak long-horizon realism. We introduce ATBench, a trajectory-level benchmark for structured, diverse, and realistic evaluation of agent safety. ATBench organizes agentic risk along three dimensions: risk source, failure mode, and real-world harm. Based on this taxonomy, we construct trajectories with heterogeneous tool pools and a long-context delayed-trigger protocol that captures realistic risk emergence across multiple stages. The benchmark contains 1,000 trajectories (503 safe and 497 unsafe), averaging 9.01 turns and 3.95k tokens, with 1,954 invoked tools drawn from pools spanning 2,084 available tools. Data quality is supported by rule-based and LLM-based filtering plus full human audit. Experiments on frontier LLMs, open-source models, and specialized guard systems show that ATBench is challenging even for strong evaluators, while enabling taxonomy-stratified analysis, cross-benchmark comparison, and diagnosis of long-horizon failure patterns.
Received — 2 April 2026 ⏭

Accelerating Diffusion Large Language Models with SlowFast Sampling: The Three Golden Principles

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qingyan Wei · Yaojie Zhang · Zhiyuan Liu · Puyu Zeng · Yuxuan Wang · Biqing Qi · Dongrui Liu · Linfeng Zhang
1 April 2026 at 12:00
arXiv:2506.10848v3 Announce Type: replace-cross Abstract: Diffusion-based language models (dLLMs) have emerged as a promising alternative to traditional autoregressive LLMs by enabling parallel token generation and significantly reducing inference latency. However, existing sampling strategies for dLLMs, such as confidence-based or semi-autoregressive decoding, often suffer from static behavior, leading to suboptimal efficiency and limited flexibility. In this paper, we propose SlowFast Sampling, a novel dynamic sampling strategy that adaptively alternates between exploratory and accelerated decoding stages. Our method is guided by three golden principles: certainty principle, convergence principle, and positional principle, which govern when and where tokens can be confidently and efficiently decoded. We further integrate our strategy with dLLM-Cache to reduce redundant computation. Extensive experiments across benchmarks and models show that SlowFast Sampling achieves up to 15.63$\times$ speedup on LLaDA with minimal accuracy drop, and up to 34.22$\times$ when combined with caching. Notably, our approach outperforms strong autoregressive baselines like LLaMA3 8B in throughput, demonstrating that well-designed sampling can unlock the full potential of dLLMs for fast and high-quality generation.
Received — 26 March 2026 ⏭

Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dadi Guo · Tianyi Zhou · Dongrui Liu · Chen Qian · Qihan Ren · Shuai Shao · Zhiyuan Fan · Yi R. Fung · Kun Wang · Linfeng Zhang · Jing Shao
25 March 2026 at 12:00
arXiv:2510.00415v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) and agent system designs have empowered agents with unprecedented levels of capability. However, existing agent benchmarks are showing a trend of rapid ceiling-hitting by newly developed agents, making it difficult to meet the demands for evaluating agent abilities. To address this problem, we propose the Trajectory-based Validated-by-Reproducing Agent-benchmark Complexity Evolution (TRACE) framework. This framework takes an original task from an existing benchmark and encourages agents to freely explore and evolve it into a new task with higher difficulty while recording validatable agent trajectories. The framework proceeds in three stages: (1) evolutionary proposal mining, which provides task evolution proposals through preliminary exploration and divergent thinking; (2) problem formation and free exploration, where proposals are conceptualized into feasible problem candidates and the agents then explore them freely while recording their execution trajectories; and (3) multi-level validation, which ensures that the evolved tasks are accompanied by validatable and reproducible trajectories. Experiments on the GAIA benchmark demonstrate that the TRACE framework consistently enhances task complexity while improving the reliability of correctness through validatable execution trajectories. In addition, our framework can successfully adapt to and improve reasoning datasets represented by AIME-2024. This work marks a paradigm shift from static, manually curated benchmarks to dynamic, self-evolving evaluation systems, providing a sustainable and challenging runway for agent development

Hijacking ERAD for targeted degradation of transmembrane proteins

✇Cell
By: Haikun Song · Wei Wang · Tingfang Mei · Huiwen Zheng · Keni Ning · Xiaofan Liu · Siulam Cheung · Zhiyuan Cao · Danqi Sheng · Xiaohan Mai · Haoran Zhu · Guankai Guo · Shuaimeng Liu · Rongkai Wei · Qian Wang · Yu Cao · Yu Ding · Yiyan Fei · Rui Liu · Motoyuki Hattori · Chunquan Sheng · Boxun Lu
19 March 2026 at 08:00
Development of an ERAD-hijacking technology overcomes the challenges of current targeted protein degradation approaches to achieve degradation of transmembrane proteins.
Received — 14 March 2026 ⏭

PIRA-Bench: A Transition from Reactive GUI Agents to GUI-based Proactive Intent Recommendation Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuxiang Chai · Shunye Tang · Han Xiao · Rui Liu · Hongsheng Li
10 March 2026 at 12:00
arXiv:2603.08013v1 Announce Type: new Abstract: Current Graphical User Interface (GUI) agents operate primarily under a reactive paradigm: a user must provide an explicit instruction for the agent to execute a task. However, an intelligent AI assistant should be proactive, which is capable of anticipating user intentions directly from continuous visual inputs, such as mobile or desktop screenshots, and offering timely recommendations without explicit user prompting. Transitioning to this proactive paradigm presents significant challenges. Real-world screen activity is rarely linear; it consists of long-horizon trajectories fraught with noisy browsing, meaningless actions, and multithreaded task-switching. To address this gap, we introduce PIRA-Bench (Proactive Intent Recommendation Agent Benchmark), a novel benchmark for evaluating multimodal large language models (MLLMs) on continuous, weakly-supervised visual inputs. Unlike reactive datasets, PIRA-Bench features complex trajectories with multiple interleaved intents and noisy segments with various user profile contexts, challenging agents to detect actionable events while fitting to user preferences. Furthermore, we propose the PIRF baseline, a memory-aware, state-tracking framework that empowers general MLLMs to manage multiple task threads and handle misleading visual inputs. PIRA-Bench serves as an initial step toward robust and proactive GUI-based personal assistants.
Received — 11 March 2026 ⏭

CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaona Xue · Yiqiao Huang · Jiacheng Li · Yuanhang Zheng · Huiqi Miao · Yunfei Ma · Rui Liu · Xinbao Sun · Minglu Liu · Fanyu Meng · Chao Deng · Junlan Feng
10 March 2026 at 12:00
arXiv:2603.07886v1 Announce Type: cross Abstract: Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs' adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications.

Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuai Shao · Qihan Ren · Chen Qian · Boyi Wei · Dadi Guo · Jingyi Yang · Xinhao Song · Linfeng Zhang · Weinan Zhang · Dongrui Liu · Jing Shao
10 March 2026 at 12:00
arXiv:2509.26354v2 Announce Type: replace Abstract: Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as Misevolution. To provide a systematic investigation, we evaluate misevolution along four key evolutionary pathways: model, memory, tool, and workflow. Our empirical findings reveal that misevolution is a widespread risk, affecting agents built even on top-tier LLMs (e.g., Gemini-2.5-Pro). Different emergent risks are observed in the self-evolutionary process, such as the degradation of safety alignment after memory accumulation, or the unintended introduction of vulnerabilities in tool creation and reuse. To our knowledge, this is the first study to systematically conceptualize misevolution and provide empirical evidence of its occurrence, highlighting an urgent need for new safety paradigms for self-evolving agents. Finally, we discuss potential mitigation strategies to inspire further research on building safer and more trustworthy self-evolving agents. Our code and data are available at https://github.com/ShaoShuai0605/Misevolution . Warning: this paper includes examples that may be offensive or harmful in nature.

Survey of Computerized Adaptive Testing: A Machine Learning Perspective

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yan Zhuang · Qi Liu · Haoyang Bi · Zhenya Huang · Weizhe Huang · Jiatong Li · Junhao Yu · Zirui Liu · Zirui Hu · Yuting Hong · Zachary A. Pardos · Haiping Ma · Mengxiao Zhu · Shijin Wang · Enhong Chen
10 March 2026 at 12:00
arXiv:2404.00712v3 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports, sociology, and the evaluation of AI models. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing paradigm. We delve into measurement models, question selection algorithm, bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.
Received — 5 March 2026 ⏭

xLLM Technical Report

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tongxuan Liu · Tao Peng · Peijun Yang · Xiaoyang Zhao · Xiusheng Lu · Weizhe Huang · Zirui Liu · Xiaoyu Chen · Zhiwei Liang · Jun Xiong · Donghe Jin · Minchao Zhang · Jinrong Guo · Yingxu Deng · Xu Zhang · Xianzhe Dong · Siqi Wang · Siyu Wu · Yu Wu · Zihan Tang · Yuting Zeng · Yanshu Wang · Jinguang Liu · Meng Kang · Menxin Li · Yunlong Wang · Yiming Liu · Xiaolong Ma · Yifan Wang · Yichen Zhang · Jinrun Yin · Keyang Zheng · Jiawei Yin · Jun Zhang · Ziyue Wang · Xiaobo Lin · Liangyu Liu · Liwei Lan · Yang Liu · Chunhua Peng · Han Liu · Songcheng Ren · Xuezhu Wang · Yunheng Shen · Yi Wang · Guyue Liu · Yitao Hu · Hui Chen · Tong Yang · Hailong Yang · Jing Li · Guiguang Ding · Ke Zhang
4 March 2026 at 13:00
arXiv:2510.14686v2 Announce Type: replace-cross Abstract: We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intelligent scheduling module that efficiently processes multimodal requests and co-locates online and offline tasks through unified elastic scheduling to maximize cluster utilization. This module also relies on a workload-adaptive dynamic Prefill-Decode (PD) disaggregation policy and a novel Encode-Prefill-Decode (EPD) disaggregation policy designed for multimodal inputs. Furthermore, it incorporates a distributed architecture to provide global KV Cache management and robust fault-tolerant capabilities for high availability. At the engine layer, xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources. This is achieved through comprehensive multi-layer execution pipeline optimizations, an adaptive graph mode and an xTensor memory management. xLLM-Engine also further integrates algorithmic enhancements such as optimized speculative decoding and dynamic EPLB, collectively serving to substantially boost throughput and inference efficiency. Extensive evaluations demonstrate that xLLM delivers significantly superior performance and resource efficiency. Under identical TPOT constraints, xLLM achieves throughput up to 1.7x that of MindIE and 2.2x that of vLLM-Ascend with Qwen-series models, while maintaining an average throughput of 1.7x that of MindIE with Deepseek-series models. xLLM framework is publicly available at https://github.com/jd-opensource/xllm and https://github.com/jd-opensource/xllm-service.
Received — 25 February 2026 ⏭

City Editing: Hierarchical Agentic Execution for Dependency-Aware Urban Geospatial Modification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Liu · Steven Jige Quan · Zhong-Ren Peng · Zijun Yao · Han Wang · Zhengzhang Chen · Kunpeng Liu · Yanjie Fu · Dongjie Wang
24 February 2026 at 13:00
arXiv:2602.19326v1 Announce Type: cross Abstract: As cities evolve over time, challenges such as traffic congestion and functional imbalance increasingly necessitate urban renewal through efficient modification of existing plans, rather than complete re-planning. In practice, even minor urban changes require substantial manual effort to redraw geospatial layouts, slowing the iterative planning and decision-making procedure. Motivated by recent advances in agentic systems and multimodal reasoning, we formulate urban renewal as a machine-executable task that iteratively modifies existing urban plans represented in structured geospatial formats. More specifically, we represent urban layouts using GeoJSON and decompose natural-language editing instructions into hierarchical geometric intents spanning polygon-, line-, and point-level operations. To coordinate interdependent edits across spatial elements and abstraction levels, we propose a hierarchical agentic framework that jointly performs multi-level planning and execution with explicit propagation of intermediate spatial constraints. We further introduce an iterative execution-validation mechanism that mitigates error accumulation and enforces global spatial consistency during multi-step editing. Extensive experiments across diverse urban editing scenarios demonstrate significant improvements in efficiency, robustness, correctness, and spatial validity over existing baselines.

CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures

✇cs.AI, q-bio.NC updates on arXiv.org
By: Punya Syon Pandey · Yongjin Yang · Jiarui Liu · Zhijing Jin
24 February 2026 at 13:00
arXiv:2508.11915v2 Announce Type: replace-cross Abstract: Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been sufficiently quantified. In this paper, we present the Conversational Robustness Evaluation Score: CORE, a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions. CORE integrates measures of cluster entropy, lexical repetition, and semantic similarity, providing a direct lens of dialog quality. We apply CORE to pairwise LLM dialogs across competitive, cooperative, and neutral settings, further grounding our analysis in Zipf's and Heaps' Laws to characterize word frequency distributions and vocabulary growth. Our findings show that cooperative settings exhibit both steeper Zipf distributions and higher Heap exponents, indicating more repetition alongside greater vocabulary expansion. In contrast, competitive interactions display lower Zipf and Heaps exponents, reflecting less repetition and more constrained vocabularies. These results provide new insights into how social incentives influence language adaptation, and highlight CORE as a robust diagnostic for measuring linguistic robustness in multi-agent LLM systems. Our code is available at https://github.com/psyonp/core.
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