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Received — 2 April 2026 ⏭

Scaling the Long Video Understanding of Multimodal Large Language Models via Visual Memory Mechanism

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tao Chen · Kun Zhang · Qiong Wu · Xiao Chen · Chao Chang · Xiaoshuai Sun · Yiyi Zhou · Rongrong Ji
1 April 2026 at 12:00
arXiv:2603.29252v1 Announce Type: cross Abstract: Long video understanding is a key challenge that plagues the advancement of \emph{Multimodal Large language Models} (MLLMs). In this paper, we study this problem from the perspective of visual memory mechanism, and proposed a novel and training-free approach, termed \emph{Flexible Memory} (\textbf{FlexMem}). In principle, FlexMem aims to mimic human behavior of video watching, \emph{i.e.}, continually watching video content and recalling the most relevant memory fragments to answer the question. In this way, FlexMem can help MLLMs achieve video understanding of infinite lengths, unlike previous methods that process all video information at once and have input upper-limit. Concretely, FlexMem first consider the visual KV caches as the memory sources, and realize the effective memory transfer and writing via a dual-pathway compression design. Afterwards, FlexMem also explores different memory reading strategies for the diverse video understanding tasks, including the popular streaming one. To validate FlexMem, we apply it to two popular video-MLLMs, and conduct extensive experiments on five long video and one streaming video task. The experimental results show that on \textbf{a single 3090 GPU}, our FlexMem can achieve obvious improvements than existing efficient video understanding methods and process more than \textbf{1k frames}, which also helps the base MLLMs achieve comparable or even better performance than SOTA MLLMs on some benchmarks, \emph{e.g.} , GPT-4o and Gemini-1.5 Pro.

6GAgentGym: Tool Use, Data Synthesis, and Agentic Learning for Network Management

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiao Chen · Jianhua Tang · Xiaotong Yang · Zuohong Lv
1 April 2026 at 12:00
arXiv:2603.29656v1 Announce Type: cross Abstract: Autonomous 6G network management requires agents that can execute tools, observe the resulting state changes, and adapt their decisions accordingly. Existing benchmarks based on static questions or scripted episode replay, however, do not support such closed-loop interaction, limiting agents to passive evaluation without the ability to learn from environmental feedback. This paper presents 6GAgentGym to provide closed-loop capability. The framework provides an interactive environment with 42 typed tools whose effect classification distinguishes read-only observation from state-mutating configuration, backed by a learned Experiment Model calibrated on NS-3 simulation data. 6G-Forge bootstraps closed-loop training trajectories from NS-3 seeds via iterative Self-Instruct generation with execution verification against the Experiment Model. Supervised fine-tuning on the resulting corpus followed by reinforcement learning with online closed-loop interaction enables an 8B open-source model to achieve comparable overall success rate to GPT-5 on the accompanying 6GAgentBench, with stronger performance on long-horizon tasks. Together, these components provide a viable path toward autonomous, closed-loop network management.

The 1000 Chinese Pangenome empowers medical and population genetics

✇Nature - Issue - nature.com science feeds
By: Yifei Wang · Zhongqu Duan · Dan Chen · Dandan Shi · Yi Ding · Zhibin Wang · Baoqing Li · Zhiyi Wang · Minmin Guo · Wen Yang · Junren Hou · Wenhao Chen · Yazhou Guo · Wenjie Wei · Yujie Cao · Xiwei Sun · Weiyang Bai · Mingdong Lu · Ting Qi · Xian Shen · Jian Yang
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10315-y

Development of the pangenome-informed genome assembly (PIGA) workflow enabled the generation of 1,116 diploid genome assemblies (55 de novo and 1,061 pangenome-informed), representing an extensive resource of medically relevant genic variations.
Received — 26 March 2026 ⏭

PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuochen Liu · Junyi Zhu · Long Shu · Junda Lin · Yuhao Chen · Haotian Zhang · Chao Zhang · Derong Xu · Jia Li · Bo Tang · Zhiyu Li · Feiyu Xiong · Enhong Chen · Tong Xu
25 March 2026 at 12:00
arXiv:2603.23231v1 Announce Type: new Abstract: Empowering large language models with long-term memory is crucial for building agents that adapt to users' evolving needs. However, prior evaluations typically interleave preference-related dialogues with irrelevant conversations, reducing the task to needle-in-a-haystack retrieval while ignoring relationships between events that drive the evolution of user preferences. Such settings overlook a fundamental characteristic of real-world personalization: preferences emerge gradually and accumulate across interactions within noisy contexts. To bridge this gap, we introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall. Additionally, we incorporate (1) text variability and (2) linguistic alignment to simulate erratic user inputs and individual idiolects in real-world data. PERMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time. We design both multiple-choice and interactive tasks to probe the model's understanding of persona along the interaction timeline. Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption, outperforming traditional semantic retrieval of raw dialogues. Nevertheless, they still struggle to maintain a coherent persona across temporal depth and cross-domain interference, highlighting the need for more robust personalized memory management in agents. Our code and data are open-sourced at https://github.com/PolarisLiu1/PERMA.

ForestPrune: High-ratio Visual Token Compression for Video Multimodal Large Language Models via Spatial-Temporal Forest Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaobo Ju · Baiyang Song · Tao Chen · Jiapeng Zhang · Qiong Wu · Chao Chang · HuaiXi Wang · Yiyi Zhou · Rongrong Ji
25 March 2026 at 12:00
arXiv:2603.22911v1 Announce Type: cross Abstract: Due to the great saving of computation and memory overhead, token compression has become a research hot-spot for MLLMs and achieved remarkable progress in image-language tasks. However, for the video, existing methods still fall short of high-ratio token compression. We attribute this shortcoming to the insufficient modeling of temporal and continual video content, and propose a novel and training-free token pruning method for video MLLMs, termed ForestPrune, which achieves effective and high-ratio pruning via Spatial-temporal Forest Modeling. In practice, ForestPrune construct token forests across video frames based on the semantic, spatial and temporal constraints, making an overall comprehension of videos. Afterwards, ForestPrune evaluates the importance of token trees and nodes based on tree depth and node roles, thereby obtaining a globally optimal pruning decision. To validate ForestPrune, we apply it to two representative video MLLMs, namely LLaVA-Video and LLaVA-OneVision, and conduct extensive experiments on a bunch of video benchmarks. The experimental results not only show the great effectiveness for video MLLMs, e.g., retaining 95.8% average accuracy while reducing 90% tokens for LLaVA-OneVision, but also show its superior performance and efficiency than the compared token compression methods, e.g., +10.1% accuracy on MLVU and -81.4% pruning time than FrameFusion on LLaVA-Video.

ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Wang · Haocheng Yang · Licheng Pan · Lei Shen · Xiaoxi Li · Yinuo Wang · Zhichao Chen · Yuan Lu · Haoxuan Li · Zhouchen Lin
25 March 2026 at 12:00
arXiv:2603.23184v1 Announce Type: cross Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we study \textit{implicit reward modeling} -- learning reward models from implicit human feedback (e.g., clicks and copies) -- as a cost-effective alternative. We identify two fundamental challenges in implicit reward modeling: (1) Implicit preference data lacks definitive negative samples, which makes standard positive-negative classification methods inapplicable; (2) Implicit preference data suffers from user preference bias, where different responses have different propensities to elicit user feedback actions, which exacerbates the difficulty of distinguishing definitive negative samples. To address these challenges, we propose ImplicitRM, which aims to learn unbiased reward models from implicit preference data. ImplicitRM stratifies training samples into four latent groups via a stratification model. Building on this, it derives a learning objective through likelihood maximization, which we prove is theoretically unbiased, effectively resolving both challenges. Experiments demonstrate that ImplicitRM learns accurate reward models across implicit preference datasets. Code is available on our project website.

TRACE: A Multi-Agent System for Autonomous Physical Reasoning in Seismological

✇cs.AI, q-bio.NC updates on arXiv.org
By: Feng Liu · Jian Xu · Xin Cui · Xinghao Wang · Zijie Guo · Jiong Wang · S. Mostafa Mousavi · Xinyu Gu · Hao Chen · Ben Fei · Lihua Fang · Fenghua Ling · Zefeng Li · Lei Bai
25 March 2026 at 12:00
arXiv:2603.21152v2 Announce Type: replace-cross Abstract: Inferring the physical mechanisms that govern earthquake sequences from indirect geophysical observations remains difficult, particularly across tectonically distinct environments where similar seismic patterns can reflect different underlying processes. Current interpretations rely heavily on the expert synthesis of catalogs, spatiotemporal statistics, and candidate physical models, limiting reproducibility and the systematic transfer of insight across settings. Here we present TRACE (Trans-perspective Reasoning and Automated Comprehensive Evaluator), a multi-agent system that combines large language model planning with formal seismological constraints to derive auditable, physically grounded mechanistic inference from raw observations. Applied to the 2019 Ridgecrest sequence, TRACE autonomously identifies stress-perturbation-induced delayed triggering, resolving the cascading interaction between the Mw 6.4 and Mw 7.1 mainshocks; in the Santorini-Kolumbo case, the system identifies a structurally guided intrusion model, distinguishing fault-channeled episodic migration from the continuous propagation expected in homogeneous crustal failure. By providing a generalizable logical infrastructure for interpreting heterogeneous seismic phenomena, TRACE advances the field from expert-dependent analysis toward knowledge-guided autonomous discovery in Earth sciences.

Deciphering lung adenocarcinoma heterogeneity: a multi-omics approach reveals nuclear division fibroblasts as prognosticators and therapeutic targets

✇Omics In Lung
By: Peng Cao · Yongxia Qin · Dingtao Hu · Tengfei Ge · Peng Zhang · Chao Cheng · Shun Wang · Shuhui Hu · Zhen Zhang · Ling Xu
21 March 2026 at 18:00

J Transl Med. 2026 Mar 20. doi: 10.1186/s12967-026-08022-3. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant contributor to cancer‑related mortality globally. Lung‑associated fibroblasts (LAFs) are intricately linked to tumorigenesis and the tumor microenvironment (TME), but their heterogeneity and prognostic relevance in LUAD remain incompletely understood. This study aimed to systematically characterize LAF subsets across the spectrum of pulmonary disease, identify LAF subpopulations associated with LUAD prognosis, and construct a robust LAF‑based prognostic signature.

METHODS: We employed a multi-omics approach, leveraging bulk RNA data of 2719 patients from 19 LUAD cohorts, single-cell RNA (scRNA) sequencing data of 368,904 cells from 93 samples, and spatial transcriptomics data of 15,673 spots from 6 samples to characterize the landscape of LAFs across various stages of pulmonary disease. We employed multiple advanced machine learning algorithms to construct and validate a robust nuclear division LAFs (nLAFs) risk score (nLRS) prediction model.

RESULTS: We observed a dynamic and gradual increase in the proportion of LAFs during the progression of LUAD. Throughout this process, we identified nine LAFs subtypes and found nLAFs are significantly associated with the prognosis of LUAD. Utilizing 100 machine learning algorithm combinations and integrating nLAFs marker genes, we developed a five gene based nLRS model, which demonstrated superior performance than other 49 published models in predicting clinical outcomes for LUAD. Additionally, we observed distinct biological functions and immune cell infiltration in the TME between high and low nLRS groups. Exploratory analysis of pan-cancer immunotherapy cohorts suggested that patients with high nLRS scores may exhibit resistance to immunotherapy in some cancer types, but prospective validation in LUAD-specific cohorts is required. Conversely, high nLRS patients displayed increased sensitivity to chemotherapeutic and targeted therapies in preclinical models.

CONCLUSION: Our study introduces a candidate five-gene signature derived from nLAFs that may serve as a robust prognostic biomarker pending prospective validation, offering insights into personalized therapeutic strategies for LUAD patients.

PMID:41862916 | DOI:10.1186/s12967-026-08022-3

Received — 17 March 2026 ⏭

One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaolong Li · Lichao Sun · Yongchao Chen
16 March 2026 at 12:00
arXiv:2603.12480v1 Announce Type: cross Abstract: Generative flow and diffusion models provide the continuous, multimodal action distributions needed for high-precision robotic policies. However, their reliance on iterative sampling introduces severe inference latency, degrading control frequency and harming performance in time-sensitive manipulation. To address this problem, we propose the One-Step Flow Policy (OFP), a from-scratch self-distillation framework for high-fidelity, single-step action generation without a pre-trained teacher. OFP unifies a self-consistency loss to enforce coherent transport across time intervals, and a self-guided regularization to sharpen predictions toward high-density expert modes. In addition, a warm-start mechanism leverages temporal action correlations to minimize the generative transport distance. Evaluations across 56 diverse simulated manipulation tasks demonstrate that a one-step OFP achieves state-of-the-art results, outperforming 100-step diffusion and flow policies while accelerating action generation by over $100\times$. We further integrate OFP into the $\pi_{0.5}$ model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solution for highly accurate and low-latency robot control.

Neural-Quantum-States Impurity Solver for Quantum Embedding Problems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinzhanghao Zhou · Tsung-Han Lee · Ao Chen · Nicola Lanat\`a · Hong Guo
16 March 2026 at 12:00
arXiv:2509.12431v2 Announce Type: replace-cross Abstract: Neural quantum states (NQS) have emerged as a promising approach to solve second-quantized Hamiltonians, because of their scalability and flexibility. In this work, we design and benchmark an NQS impurity solver for the quantum embedding (QE) methods, focusing on the ghost Gutzwiller Approximation (gGA) framework. We introduce a graph transformer-based NQS framework able to represent arbitrarily connected impurity orbitals of the embedding Hamiltonian (EH) and develop an error control mechanism to stabilize iterative updates throughout the QE loops. We validate the accuracy of our approach with benchmark gGA calculations of the Anderson Lattice Model, yielding results in excellent agreement with the exact diagonalisation impurity solver. Finally, our analysis of the computational budget reveals the method's principal bottleneck to be the high-accuracy sampling of physical observables required by the embedding loop, rather than the NQS variational optimization, directly highlighting the critical need for more efficient inference techniques.

FCMBench: The First Large-scale Financial Credit Multimodal Benchmark for Real-world Applications

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yehui Yang · Dalu Yang · Fangxin Shang · Wenshuo Zhou · Jie Ren · Yifan Liu · Haojun Fei · Qing Yang · Yanwu Xu · Tao Chen
16 March 2026 at 12:00
arXiv:2601.00150v3 Announce Type: replace-cross Abstract: FCMBench is the first large-scale and privacy-compliant multimodal benchmark for real-world financial credit applications, covering tasks and robustness challenges from domain specific workflows and constraints. The current version of FCMBench covers 26 certificate types, with 5198 privacy-compliant images and 13806 paired VQA samples. It evaluates models on Perception and Reasoning tasks under real-world Robustness interferences, including 3 foundational perception tasks, 4 credit-specific reasoning tasks demanding decision-oriented visual evidence interpretation, and 10 real-world challenges for rigorous robustness stress testing. Moreover, FCMBench offers privacy-compliant realism with minimal leakage risk through in-house scenario-aware captures of manually synthesized templates, without any publicly released images. We conduct extensive evaluations of 28 state-of-the-art vision-language models spanning 14 AI companies and research institutes. Among them, Gemini 3 Pro achieves the best F1 score as a commercial model (65.16), Kimi-K2.5 achieves the best score as an open-source baseline (60.58). The mean and the std. of all tested models is 44.8 and 10.3 respectively, indicating that FCMBench is non-trivial and provides strong resolution for separating modern vision-language model capabilities. Robustness evaluations reveal that even top-performing models experience notable performance degradation under the designed challenges. We have open-sourced this benchmark to advance AI research in the credit domain and provide a domain-specific task for real-world AI applications.

BitDance: Scaling Autoregressive Generative Models with Binary Tokens

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuang Ai · Jiaming Han · Shaobin Zhuang · Weijia Mao · Xuefeng Hu · Ziyan Yang · Zhenheng Yang · Yali Wang · Huaibo Huang · Xiangyu Yue · Hao Chen
16 March 2026 at 12:00
arXiv:2602.14041v2 Announce Type: replace-cross Abstract: We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $2^{256}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256x256, BitDance achieves an FID of 1.24, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using 5.4x fewer parameters (260M) and achieving 8.7x speedup. For text-to-image generation, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024x1024 images, BitDance achieves a speedup of over 30x compared to prior AR models. We release code and models to facilitate further research on AR foundation models. Code and models are available at: https://github.com/shallowdream204/BitDance.
Received — 14 March 2026 ⏭

FNDC1 Competitively Binds Gbeta2 to Suppress the beta-Catenin-Destruction Complex and Promote Gastric Cancer Malignancy

✇Omics in Gastric
By: Wenyu Gao · Hao Chen · Fangyu Lin · Jialin Liu · Tingxuan Huang · Han Wang · Xiaojiao Weng · Xinli Wang · Xiaoyan Lin · Tao Jiang
11 March 2026 at 18:00

FASEB J. 2026 Mar 31;40(6):e71634. doi: 10.1096/fj.202503587R.

ABSTRACT

Gastric cancer (GC) is a leading cause of cancer-related deaths and has high recurrence rate. Although fibronectin domain-containing protein 1 (FNDC1) is implicated in GC progression, its molecular mechanisms remain unclear. Multi-omics analyses (TCGA, GEO datasets) were used to assess FNDC1 expression and clinical correlation. In vitro (cell proliferation, invasion, EMT markers) and in vivo (xenograft) experiments, combined with molecular assays (Co-IP, WB, ChIP), explored FNDC1's function and mechanism. FNDC1 was significantly upregulated in GC, correlating with advanced clinicopathological features and poor prognosis. Knockdown of FNDC1 suppressed GC cell proliferation, invasion, and metastasis by inhibiting EMT and Wnt/β-catenin signaling. Mechanistically, FNDC1 competitively bound the WD5 domain (residues 224-254) of Gβ2, disrupting Gβγ-Dvl1 interaction. This prevented Dvl1 degradation, promoted Axin1 ubiquitination, and destabilized the β-catenin-destruction complex (GSK3 β-APC-Axin1), leading to β-catenin accumulation and Wnt pathway activation. FNDC1 drives GC malignancy by targeting the Gβ2-Dvl1 axis to activate Wnt/β-catenin signaling, suggesting FNDC1 as a novel prognostic biomarker and therapeutic target.

PMID:41808415 | PMC:PMC12976582 | DOI:10.1096/fj.202503587R

Received — 11 March 2026 ⏭

Deconstructing Multimodal Mathematical Reasoning: Towards a Unified Perception-Alignment-Reasoning Paradigm

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyu Yang · Sihong Wu · Yilun Zhao · Zhenwen Liang · Lisen Dai · Chen Zhao · Minhao Cheng · Arman Cohan · Xiangliang Zhang
10 March 2026 at 12:00
arXiv:2603.08291v1 Announce Type: new Abstract: Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems that involve both textual and visual modalities. However, current models still face significant challenges in real-world visual math tasks. They often misinterpret diagrams, fail to align mathematical symbols with visual evidence, and produce inconsistent reasoning steps. Moreover, existing evaluations mainly focus on checking final answers rather than verifying the correctness or executability of each intermediate step. To address these limitations, a growing body of recent research addresses these issues by integrating structured perception, explicit alignment, and verifiable reasoning within unified frameworks. To establish a clear roadmap for understanding and comparing different MMR approaches, we systematically study them around four fundamental questions: (1) What to extract from multimodal inputs, (2) How to represent and align textual and visual information, (3) How to perform the reasoning, and (4) How to evaluate the correctness of the overall reasoning process. Finally, we discuss open challenges and offer perspectives on promising directions for future research.

"Better Ask for Forgiveness than Permission": Practices and Policies of AI Disclosure in Freelance Work

✇cs.AI, q-bio.NC updates on arXiv.org
By: Angel Hsing-Chi Hwang · Senya Wong · Baixiao Chen · Jessica He · Hyo Jin Do
10 March 2026 at 12:00
arXiv:2603.07459v1 Announce Type: cross Abstract: The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients' expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.

Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments

✇cs.AI, q-bio.NC updates on arXiv.org
By: Longbiao Cheng · Shih-Chii Liu
10 March 2026 at 12:00
arXiv:2603.07471v1 Announce Type: cross Abstract: Recent studies have shown that post-deployment adaptation can improve the robustness of speech enhancement models in unseen noise conditions. However, existing methods often incur prohibitive computational and memory costs, limiting their suitability for on-device deployment. In this work, we investigate model adaptation in realistic settings with dynamic acoustic scene changes and propose a lightweight framework that augments a frozen backbone with low-rank adapters updated via self-supervised training. Experiments on sequential scene evaluations spanning 111 environments across 37 noise types and three signal-to-noise ratio ranges, including the challenging [-8, 0] dB range, show that our method updates fewer than 1% of the base model's parameters while achieving an average 1.51 dB SI-SDR improvement within only 20 updates per scene. Compared to state-of-the-art approaches, our framework achieves competitive or superior perceptual quality with smoother and more stable convergence, demonstrating its practicality for lightweight on-device adaptation of speech enhancement models under real-world acoustic conditions.

\$OneMillion-Bench: How Far are Language Agents from Human Experts?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qianyu Yang · Yang Liu · Jiaqi Li · Jun Bai · Hao Chen · Kaiyuan Chen · Tiliang Duan · Jiayun Dong · Xiaobo Hu · Zixia Jia · Yang Liu · Tao Peng · Yixin Ren · Ran Tian · Zaiyuan Wang · Yanglihong Xiao · Gang Yao · Lingyue Yin · Ge Zhang · Chun Zhang · Jianpeng Jiao · Zilong Zheng · Yuan Gong
10 March 2026 at 12:00
arXiv:2603.07980v1 Announce Type: cross Abstract: As language models (LMs) evolve from chat assistants to long-horizon agents capable of multi-step reasoning and tool use, existing benchmarks remain largely confined to structured or exam-style tasks that fall short of real-world professional demands. To this end, we introduce \$OneMillion-Bench \$OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios. Unlike prior work, the benchmark requires retrieving authoritative sources, resolving conflicting evidence, applying domain-specific rules, and making constraint decisions, where correctness depends as much on the reasoning process as the final answer. We adopt a rubric-based evaluation protocol scoring factual accuracy, logical coherence, practical feasibility, and professional compliance, focused on expert-level problems to ensure meaningful differentiation across agents. Together, \$OneMillion-Bench provides a unified testbed for assessing agentic reliability, professional depth, and practical readiness in domain-intensive scenarios.

MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junjie Xing · Yeye He · Mengyu Zhou · Haoyu Dong · Shi Han · Lingjiao Chen · Dongmei Zhang · Surajit Chaudhuri · H. V. Jagadish
10 March 2026 at 12:00
arXiv:2506.05587v4 Announce Type: replace Abstract: Tables and table-based use cases play a crucial role in many important real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally require expert-level users like data engineers, data analysts, and database administrators to operate. Although LLMs have shown remarkable progress in working with tables (e.g., in spreadsheet and database copilot scenarios), comprehensive benchmarking of such capabilities remains limited. In contrast to an extensive and growing list of NLP benchmarks, evaluations of table-related tasks are scarce, and narrowly focus on tasks like NL-to-SQL and Table-QA, overlooking the broader spectrum of real-world tasks that professional users face. This gap limits our understanding and model progress in this important area. In this work, we introduce MMTU, a large-scale benchmark with over 28K questions across 25 real-world table tasks, designed to comprehensively evaluate models ability to understand, reason, and manipulate real tables at the expert-level. These tasks are drawn from decades' worth of computer science research on tabular data, with a focus on complex table tasks faced by professional users. We show that MMTU require a combination of skills -- including table understanding, reasoning, and coding -- that remain challenging for today's frontier models, where even frontier reasoning models like OpenAI GPT-5 and DeepSeek R1 score only around 69\% and 57\% respectively, suggesting significant room for improvement. We highlight key findings in our evaluation using MMTU and hope that this benchmark drives further advances in understanding and developing foundation models for structured data processing and analysis. Our code and data are available at https://github.com/MMTU-Benchmark/MMTU and https://huggingface.co/datasets/MMTU-benchmark/MMTU.

Oscillatory shear stress-driven endothelial-to-mesenchymal transition: a critical mechanical signal transduction mechanism in atherosclerosis progression

✇Cell Death Discovery nature.com science feeds
By: Jie Li · Wenchao Xu · Jie Ju · Min Liu · Wenxu Wang · Min Cheng · Xiaoyun Zhang · Xiaodong Cui · Hao Chen
10 March 2026 at 08:00

Cell Death Discovery, Published online: 10 March 2026; doi:10.1038/s41420-026-03000-6

Oscillatory shear stress-driven endothelial-to-mesenchymal transition: a critical mechanical signal transduction mechanism in atherosclerosis progression
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