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Received today — 7 October 2026 ⏭

USP4-Dependent CHAF1B Stabilization Regulates Distinct SETDB1 Ubiquitin States Linked to AKT T308 Signaling and Lipogenic Remodeling in HCC

✇Omics in Hepatocellular
By: Saiyan Bian · Yun Tong · Wenkai Ni · Zhangzhi Tang · Weiting Chen · Kexin Ma · Lihan Jiang · Jiayu Shao · Xuyang He · Wenjie Zheng
30 September 2026 at 18:00

Adv Sci (Weinh). 2026 Sep 29:e78039. doi: 10.1002/advs.78039. Online ahead of print.

ABSTRACT

Durable responses to current therapies remain limited in hepatocellular carcinoma (HCC), highlighting the need to identify regulators of malignant progression. By integrating multi-omics analyses, spatial transcriptomics, clinical specimens, and multiple models, we identified chromatin assembly factor 1B (CHAF1B) as a functional regulator of HCC phenotypes. Gain- and loss-of-function of CHAF1B altered proliferative, migratory, clonogenic, and tumorigenic phenotypes. LC-MS/MS, DIA proteomics, and cell-based assays revealed CHAF1B-associated lipogenic remodeling characterized by SREBP1C nuclear localization, lipogenic gene/protein induction, and lipid-droplet accumulation. Mechanistically, the WD40 repeat-containing region of CHAF1B contributed to its association with UHRF1 and SETDB1, supporting UHRF1-associated K63-linked ubiquitination and CRM1/exportin-1-dependent cytoplasmic redistribution of SETDB1. Conversely, CHAF1B depletion enhanced SETDB1 association with VHL and favored a predominantly K11-associated degradative ubiquitin state linked to proteasomal SETDB1 loss. SETDB1 redistribution and catalytic activity were associated with AKT T308-linked signaling. A focused CRISPR-based screen of deubiquitinases identified USP4 as an upstream regulator of CHAF1B protein homeostasis. USP4 depletion or Akebia saponin D (ASD) increased K48-linked ubiquitination of CHAF1B, reduced CHAF1B protein abundance, attenuated AKT T308-linked signaling, and suppressed malignant and lipogenic phenotypes. These findings reveal distinct ubiquitin-dependent states governing SETDB1 stability and identify USP4-dependent CHAF1B stabilization as an upstream regulatory node in HCC.

PMID:42811544 | PMC:PMC13624420 | DOI:10.1002/advs.78039

Received — 10 September 2026 ⏭

UnitBoost: Managing Compound LLM Systems with a Merge Operator, Not a Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xing Zhang · Guanghui Wang · Yanwei Cui · Mengdie Flora Wang · Peiyang He
10 September 2026 at 12:00
arXiv:2609.09815v1 Announce Type: new Abstract: Compound LLM systems often solve a coordination problem by adding a higher-level LLM. The resulting meta-agent reads workers' outputs, writes the final answer, allocates later calls, and decides when to stop. It is expressive, but it also concentrates three control decisions in an opaque, order-sensitive model call. We ask whether the manager needs to be generative at all. UnitBoost replaces that model with a defined meta-level operator: a task-given unit map turns worker outputs into slot-value proposals, a constrained argmax assembles the output, and the slots left unfilled or unsupported become an explicit residual for the next round. The operator is order-free, records unit provenance, and gives a simple guarantee: without coupling constraints, unit-wise maximization under the same admission score dominates selection of any complete candidate. On three held-out benchmarks, it exceeds the best single candidate chosen with gold labels by 0.060-0.195 absolute task-score points and input-matched generative managers by 0.048-0.076. Replacing only the management step improves six compound-system configurations by 0.013-0.182. Residual-directed rounds raise FanOutQA cell F1 from 0.4778 to 0.5524; matched controls show that the true residual outperforms random targets and ordinary rereading, while a label-free supply signal flags exhaustion after one unproductive round. The same analysis measures three conditions in which no such gain is available (one indivisible unit, unavailable unit identity, and an endpoint that charges for every emitted unit) and quantifies cross-unit coupling as a repair cost. The manager gives up semantic freedom and gains order invariance, unit provenance, and testable failure conditions.
Received — 27 May 2026 ⏭

Stromal ACTA2 Counteracts TCDD-Induced Hepatocarcinogenesis via Suppression of the PI3K-AKT-mTOR Pathway

✇Omics in Hepatocellular
By: Ruoyang He · Ziqing Yang · Wenge Zhang · Yuehao Ren · Shuzi Li · Simin Zhan · Chuntian Wang · Zhen Zhang
18 May 2026 at 18:00

J Hepatocell Carcinoma. 2026 May 10;13:586916. doi: 10.2147/JHC.S586916. eCollection 2026.

ABSTRACT

PURPOSE: 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental pollutant that promotes hepatocellular carcinoma (HCC) through non-genotoxic mechanisms. However, stromal regulatory factors that counteract its tumor-promoting effects remain poorly defined. This study aimed to elucidate the role of actin alpha-2 (ACTA2) in TCDD-associated hepatocarcinogenesis.

METHODS: An integrative strategy combining network toxicology, Mendelian randomization, multi-omics and single-cell analyses, molecular docking and molecular dynamics simulations, along with in vitro experiments, was employed to investigate the functional role of ACTA2.

RESULTS: ACTA2 was identified as a stromal-associated factor linked to reduced HCC risk and improved patient survival. Single-cell and multi-omics analyses revealed that ACTA2 is predominantly expressed in hepatic stellate cells and fibroblast-like populations, reflecting tumor microenvironment composition rather than tumor cell-intrinsic expression. Functional enrichment analyses indicated that ACTA2 is associated with extracellular matrix remodeling and PI3K-AKT signaling. Molecular simulations demonstrated stable binding of TCDD to ACTA2 (ΔG_bind ≈ -7.05 kcal/mol), suggesting potential structural perturbation. In vitro experiments showed that TCDD downregulated ACTA2 expression, promoted proliferation of LX-2 and cancer-associated fibroblasts (CAFs), and activated PI3K-AKT-mTOR signaling, whereas ACTA2 overexpression attenuated these effects.

CONCLUSION: ACTA2 acts as a context-dependent stromal regulator that modulates PI3K-AKT-mTOR signaling in TCDD-induced hepatocarcinogenesis. These findings highlight the importance of stromal remodeling in environmental carcinogenesis and suggest ACTA2 as a potential biomarker and therapeutic target in dioxin-associated HCC.

PMID:42148320 | PMC:PMC13175077 | DOI:10.2147/JHC.S586916

Received — 4 April 2026 ⏭

LiveMathematicianBench: A Live Benchmark for Mathematician-Level Reasoning with Proof Sketches

✇cs.AI, q-bio.NC updates on arXiv.org
By: Linyang He · Qiyao Yu · Hanze Dong · Baohao Liao · Xinxing Xu · Micah Goldblum · Jiang Bian · Nima Mesgarani
3 April 2026 at 12:00
arXiv:2604.01754v1 Announce Type: cross Abstract: Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a central question in artificial intelligence and cognitive science. As LLMs are increasingly integrated into scientific workflows, rigorous evaluation of their mathematical capabilities becomes a practical necessity. Existing benchmarks are limited by synthetic settings and data contamination. We present LiveMathematicianBench, a dynamic multiple-choice benchmark for research-level mathematical reasoning built from recent arXiv papers published after model training cutoffs. By grounding evaluation in newly published theorems, it provides a realistic testbed beyond memorized patterns. The benchmark introduces a thirteen-category logical taxonomy of theorem types (e.g., implication, equivalence, existence, uniqueness), enabling fine-grained evaluation across reasoning forms. It employs a proof-sketch-guided distractor pipeline that uses high-level proof strategies to construct plausible but invalid answer choices reflecting misleading proof directions, increasing sensitivity to genuine understanding over surface-level matching. We also introduce a substitution-resistant mechanism to distinguish answer recognition from substantive reasoning. Evaluation shows the benchmark is far from saturated: Gemini-3.1-pro-preview, the best model, achieves only 43.5%. Under substitution-resistant evaluation, accuracy drops sharply: GPT-5.4 scores highest at 30.6%, while Gemini-3.1-pro-preview falls to 17.6%, below the 20% random baseline. A dual-mode protocol reveals that proof-sketch access yields consistent accuracy gains, suggesting models can leverage high-level proof strategies for reasoning. Overall, LiveMathematicianBench offers a scalable, contamination-resistant testbed for studying research-level mathematical reasoning in LLMs.
Received — 5 March 2026 ⏭

TTSR: Test-Time Self-Reflection for Continual Reasoning Improvement

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyang He · Zihua Rong · Liangjie Zhao · Yunjia Zhao · Lan Yang · Honggang Zhang
5 March 2026 at 13:00
arXiv:2603.03297v1 Announce Type: cross Abstract: Test-time Training enables model adaptation using only test questions and offers a promising paradigm for improving the reasoning ability of large language models (LLMs). However, it faces two major challenges: test questions are often highly difficult, making self-generated pseudo-labels unreliable, and existing methods lack effective mechanisms to adapt to a model's specific reasoning weaknesses, leading to inefficient learning. To address these issues, we propose \textbf{TTSR}, a self-reflective test-time self-evolving training framework. TTSR employs a single pretrained language model that alternates between the roles of a \textit{Student} and a \textit{Teacher} at test time. The Student focuses on solving problems and learning from synthesized variant questions, while the Teacher analyzes the Student's failed reasoning trajectories, summarizes recurring reasoning weaknesses, and synthesizes targeted variant questions accordingly. This process guides the model to improve within a learnable regime through a continual self-evolving loop. Experimental results on multiple challenging mathematical reasoning benchmarks show that TTSR consistently improves reasoning performance and generalizes well across different model backbones and general-domain reasoning tasks. These findings suggest that teacher-mediated self-reflection provides an effective pathway for stable and continual reasoning improvement at test time.

SpotIt+: Verification-based Text-to-SQL Evaluation with Database Constraints

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rocky Klopfenstein · Yang He · Andrew Tremante · Yuepeng Wang · Nina Narodytska · Haoze Wu
5 March 2026 at 13:00
arXiv:2603.04334v1 Announce Type: cross Abstract: We present SpotIt+, an open-source tool for evaluating Text-to-SQL systems via bounded equivalence verification. Given a generated SQL query and the ground truth, SpotIt+ actively searches for database instances that differentiate the two queries. To ensure that the generated counterexamples reflect practically relevant discrepancies, we introduce a constraint-mining pipeline that combines rule-based specification mining over example databases with LLM-based validation. Experimental results on the BIRD dataset show that the mined constraints enable SpotIt+ to generate more realistic differentiating databases, while preserving its ability to efficiently uncover numerous discrepancies between generated and gold SQL queries that are missed by standard test-based evaluation.

SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rocky Klopfenstein · Yang He · Andrew Tremante · Yuepeng Wang · Nina Narodytska · Haoze Wu
5 March 2026 at 13:00
arXiv:2510.26840v2 Announce Type: replace-cross Abstract: Community-driven Text-to-SQL evaluation platforms play a pivotal role in tracking the state of the art of Text-to-SQL performance. The reliability of the evaluation process is critical for driving progress in the field. Current evaluation methods are largely test-based, which involves comparing the execution results of a generated SQL query and a human-labeled ground-truth on a static test database. Such an evaluation is optimistic, as two queries can coincidentally produce the same output on the test database while actually being different. In this work, we propose a new alternative evaluation pipeline, called SpotIt, where a formal bounded equivalence verification engine actively searches for a database that differentiates the generated and ground-truth SQL queries. We develop techniques to extend existing verifiers to support a richer SQL subset relevant to Text-to-SQL. A performance evaluation of ten Text-to-SQL methods on the high-profile BIRD dataset suggests that test-based methods can often overlook differences between the generated query and the ground-truth. Further analysis of the verification results reveals a more complex picture of the current Text-to-SQL evaluation.
Received — 25 February 2026 ⏭

A Very Big Video Reasoning Suite

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thadd\"aus Wiedemer, Qingying Gao, Dezhi Luo, Yaoyao Qian, Lianyu Huang, Zelong Hong, Jiahui Ge, Qianli Ma, Hang He, Yifan Zhou, Lingzi Guo, Lantao Mei, Jiachen Li, Hanwen Xing, Tianqi Zhao, Fengyuan Yu, Weihang Xiao, Yizheng Jiao, Jianheng Hou, Danyang Zhang, Pengcheng Xu, Boyang Zhong, Zehong Zhao, Gaoyun Fang, John Kitaoka, Yile Xu, Hua Xu, Kenton Blacutt, Tin Nguyen, Siyuan Song, Haoran Sun, Shaoyue Wen, Linyang He, Runming Wang, Yanzhi Wang, Mengyue Yang, Ziqiao Ma, Rapha\"el Milli\`ere, Freda Shi, Nuno Vasconcelos, Daniel Khashabi, Alan Yuille, Yilun Du, Ziming Liu, Bo Li, Dahua Lin, Ziwei Liu, Vikash Kumar, Yijiang Li, Lei Yang, Zhongang Cai, Hokin Deng
24 February 2026 at 13:00
arXiv:2602.20159v1 Announce Type: cross Abstract: Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over spatiotemporal structure such as continuity, interaction, and causality. However, systematically studying video reasoning and its scaling behavior is hindered by the lack of large-scale training data. To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities. Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization to unseen reasoning tasks. Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning. The data, benchmark toolkit, and models are publicly available at https://video-reason.com/ .

Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zuhong Lin · Daoyuan Ren · Kai Ran · Jing Sun · Songlin Yu · Xuefeng Bai · Xiaotian Huang · Haiyang He · Pengxu Pan · Ying Fang · Zhanglin Li · Haipu Li · Jingjing Yao
24 February 2026 at 13:00
arXiv:2504.18880v4 Announce Type: replace Abstract: Accurately identifying the synthesis conditions of metal-organic frameworks (MOFs) is essential for guiding experimental design, yet remains challenging because relevant information in the literature is often scattered, inconsistent, and difficult to interpret. We present MOFh6, a large language model driven system that reads raw articles or crystal codes and converts them into standardized synthesis tables. It links related descriptions across paragraphs, unifies ligand abbreviations with full names, and outputs structured parameters ready for use. MOFh6 achieved 99% extraction accuracy, resolved 94.1% of abbreviation cases across five major publishers, and maintained a precision of 0.93 +/- 0.01. Processing a full text takes 9.6 s, locating synthesis descriptions 36 s, with 100 papers processed for USD 4.24. By replacing static database lookups with real-time extraction, MOFh6 reshapes MOF synthesis research, accelerating the conversion of literature knowledge into practical synthesis protocols and enabling scalable, data-driven materials discovery.
Received — 17 February 2026 ⏭

Anthropomorphism on Risk Perception: The Role of Trust and Domain Knowledge in Decision-Support AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Manuele Reani · Xiangyang He · Zuolan Bao
17 February 2026 at 13:00
arXiv:2602.13625v1 Announce Type: cross Abstract: Anthropomorphic design is routinely used to make conversational agents more approachable and engaging. Yet its influence on users' perceptions remains poorly understood. Drawing on psychological theories, we propose that anthropomorphism influences risk perception via two complementary forms of trust, and that domain knowledge moderates these relationships. To test our model, we conducted a large-scale online experiment (N = 1,256) on a financial decision-support system implementing different anthropomorphic designs. We found that anthropomorphism indirectly reduces risk perception by increasing both cognitive and affective trust. Domain knowledge moderates these paths: participants with low financial knowledge experience a negative indirect effect of perceived anthropomorphism on risk perception via cognitive trust, whereas those with high financial knowledge exhibit a positive direct and indirect effect. We discuss theoretical contributions to human-AI interaction and design implications for calibrating trust in anthropomorphic decision-support systems for responsible AI.
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