❌

Normal view

Received — 10 September 2026 ⏭

Synchronized latency reversal and immune clearance by a multifunctional fusion protein enables HIV-1 reservoir reduction

✇Molecular Therapy
By: Fei Luo · Yinghui Cao · Na Liu · Xiaowei Wang · Wenjia Hu · Yajun Yan · Xiaoqing Xie · Dongmei Jiang · Yangyang Xu · Jun Wang · Hongyu Wang · Zhenyang Yu · Peng Qian · Shicheng Wan · Zhida Liu · Yan Gong · Tielong Chen · Shihui Song · Yong Xiong · Xiaoping Tang · Linghua Li · Li Wang · Haisheng Yu · Liang Cheng
29 July 2026 at 08:00
Latent HIV reservoirs evade both antiviral therapy and immune surveillance. Luo and colleagues develop a multifunctional fusion protein that couples reservoir reactivation with targeted immune engagement and clearance, offering a coordinated strategy to expose and eliminate persistent HIV-infected cells.

CDO1 as a prognostic biomarker and therapeutic target in gastric cancer: Mechanistic insights into the PI3K/AKT-THBS1 axis and epigenetic reactivation by decitabine

✇Omics in Gastric
By: Fazhi Wang · Runkai Zhou · Jinfeng Cai · Jiazhe Wen · Abudushalamu Yalikun · Yang Yu · Yugang Wen
31 August 2026 at 18:00

Clin Transl Med. 2026 Sep;16(9):e70784. doi: 10.1002/ctm2.70784.

ABSTRACT

BACKGROUND: As a pivotal metabolic enzyme, cysteine dioxygenase type 1 (CDO1) exerts tumour-suppressive effects across diverse tumour types, and its expression is strongly correlated with clinical prognosis. However, the molecular mechanisms underlying CDO1-mediated tumour suppression in gastric cancer (GC), its relationship with the tumour-associated immune microenvironment, and pharmacological strategies to restore its expression remain poorly understood.

METHODS: CDO1 expression and prognosis were evaluated by multi-omics and tissue microarray analyses. Tumour microenvironment and immune infiltration were analyzed using ESTIMATE and ssGSEA. Downstream pathways and interacting proteins were identified by transcriptomics, co-immunoprecipitation, and GST pull-down. CDO1 function was assessed by proliferation, apoptosis, and migration assays in gain- and loss-of-function models. In vivo tumorigenesis and CDO1-dependent decitabine efficacy were evaluated by subcutaneous xenografts. Patient-derived organoids were used to assess decitabine sensitivity and 5-FU synergy.

RESULTS: Compared with normal controls, CDO1 expression was notably decreased in GC tissues, and its low expression was strongly linked to unfavourable prognosis, supporting its utility as a biomarker for prognosis. Elevated CDO1 levels correlated with an immune-active tumour microenvironment and reduced metastatic signatures. Mechanistically, CDO1 directly bound to PI3K p85α, disrupting p85α-p110α dimerization, thereby attenuating PI3K/AKT phosphorylation and downregulating THBS1 expression. CDO1 overexpression led to reduced proliferation, invasiveness, and EMT, accompanied by increased apoptosis. These effects were reversed by PI3K activation or THBS1 co-overexpression. Decitabine was identified as an agent that epigenetically restores CDO1 expression. Critically, CDO1 knockdown significantly attenuated the anti-tumour efficacy of decitabine in vivo, confirming that decitabine acts primarily through CDO1 reactivation. Decitabine synergized with 5-FU in both organoids and xenografts.

CONCLUSIONS: Our data identify CDO1 as both a biomarker for prognosis and a tumour suppressor in gastric cancer. They reveal a CDO1-PI3K/AKT-THBS1 signalling axis and support the epigenetic reactivation of CDO1 by decitabine as a translatable therapeutic strategy.

KEY POINTS: CDO1 is frequently downregulated in gastric cancer and serves as an independent favourable prognostic biomarker. CDO1 directly binds PI3K p85α, disrupting p85α-p110α dimerization to suppress the PI3K/AKT-THBS1 signalling axis. Decitabine epigenetically restores CDO1 expression, and its anti-tumour activity is critically CDO1-dependent in vivo. Combining decitabine with 5-FU synergistically overcomes gastric cancer growth in patient-derived organoids and subcutaneous xenograft models.

PMID:42670236 | PMC:PMC13527532 | DOI:10.1002/ctm2.70784

Received — 27 May 2026 ⏭

TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liying Han · Kang Yang · Oliver Wang · Jason Wu · Pengrui Quan · Gaofeng Dong · Ozan Baris Mulayim · Sizhe Ma · Yuyang Yuan · Dezhi Hong · Mario Berges · Mani Srivastava
26 May 2026 at 12:00
arXiv:2605.24703v1 Announce Type: cross Abstract: Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-only QA, TSQA requires models to ground answers in temporal signals whose patterns may occur at different scales, specific time locations, or across separated intervals. However, existing benchmarks are typically organized by task types or high-level reasoning categories, making it difficult to diagnose the underlying signal-level capabilities driving model performance. We introduce TS-Skill, a controlled benchmark for evaluating three composable analytical skills in TSQA: temporal scale selection (SK1), temporal localization (SK2), and cross-interval integration (SK3). TS-Skill provides timestamp-aware questions, broad domain coverage, and human-validated QA quality. To construct the benchmark at scale, we develop SKEvol, a skill-guided agentic framework that combines domain-aware time-series seed generation, skill-controlled question generation, metadata- and code-assisted answer construction, multi-phase signal-grounded verification, and human-in-the-loop curation. Experiments on ten state-of-the-art LLMs and TSLMs reveal substantial and uneven capability gaps across SK1-SK3. In particular, SK3 remains consistently challenging for non-agent models, whereas tool-augmented agents show a selective advantage on standalone SK3. These findings demonstrate that skill-level evaluation can uncover temporal reasoning failures that are obscured by aggregate TSQA scores.

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuqiao Meng · Luoxi Tang · Feiyang Yu · Jinyuan Jia · Guanhua Yan · Ping Yang · Zhaohan Xi
26 May 2026 at 12:00
arXiv:2509.23573v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to help security analysts manage the surge of cyber threats, automating tasks from vulnerability assessment to incident response. Yet in operational CTI workflows, reliability gaps remain substantial. Existing explanations often point to generic model issues (e.g., hallucination), but we argue the dominant bottleneck is the threat landscape itself: CTI is heterogeneous, volatile, and fragmented. Under these conditions, evidence is intertwined, crowdsourced, and temporally unstable, which are properties that standard LLM-based studies rarely capture. In this paper, we present a comprehensive empirical study of LLM vulnerabilities in CTI reasoning. We introduce a human-in-the-loop categorization framework that robustly labels failure modes across the CTI lifecycle, avoiding the brittleness of automated "LLM-as-a-judge" pipelines. We identify three domain-specific cognitive failures: spurious correlations from superficial metadata, contradictory knowledge from conflicting sources, and constrained generalization to emerging threats. We validate these mechanisms via causal interventions and show that targeted defenses reduce failure rates significantly. Together, these results offer a concrete roadmap for building resilient, domain-aware CTI agents.
Received — 4 April 2026 ⏭

Group Representational Position Encoding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Zhang · Zixiang Chen · Yifeng Liu · Zhen Qin · Huizhuo Yuan · Kangping Xu · Yang Yuan · Quanquan Gu · Andrew Chi-Chih Yao
3 April 2026 at 12:00
arXiv:2512.07805v5 Announce Type: replace-cross Abstract: We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions. GRAPE unifies two families of mechanisms: (i) multiplicative rotations (Multiplicative GRAPE) in $\operatorname{SO}(d)$ and (ii) additive logit biases (Additive GRAPE) arising from unipotent actions in the general linear group $\mathrm{GL}$. In Multiplicative GRAPE, a position $n \in \mathbb{Z}$ (or $t \in \mathbb{R}$) acts as $\mathbf{G}(n) = \exp(n \, \omega \, \mathbf{L})$ with a rank-2 skew-symmetric generator $\mathbf{L} \in \mathbb{R}^{d \times d}$, yielding a relative, compositional, norm-preserving map with a closed-form matrix exponential. RoPE is recovered exactly when the $d/2$ planes correspond to canonical coordinate pairs with a log-uniform spectrum. Learned commuting subspaces and compact non-commuting mixtures strictly extend this geometry to capture cross-subspace feature coupling at $O(d)$ and $O(r d)$ cost per head, respectively. In Additive GRAPE, additive logits arise from rank-1 (or low-rank) unipotent actions, recovering ALiBi and the Forgetting Transformer (FoX) as exact special cases while preserving an exact relative law and streaming cacheability. Overall, GRAPE provides a principled design space for positional geometry in long-context models, subsuming RoPE and ALiBi as special cases. Project page: https://github.com/model-architectures/GRAPE.
Received — 2 April 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Distilling LLM Reasoning into Graph of Concept Predictors Ziyang Yu · Liang Zhao
    arXiv:2602.03006v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces these costs by querying an LLM oracle to train compact discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise. We propose Graph of Conce
     
  • 🏷️
  • ↗️

Distilling LLM Reasoning into Graph of Concept Predictors

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyang Yu · Liang Zhao
1 April 2026 at 12:00
arXiv:2602.03006v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) for discriminative workloads is often limited by inference latency, compute, and API costs at scale. Active distillation reduces these costs by querying an LLM oracle to train compact discriminative students, but most pipelines distill only final labels, discarding intermediate reasoning signals and offering limited diagnostics of what reasoning is missing and where errors arise. We propose Graph of Concept Predictors (GCP), a reasoning-aware active distillation framework that externalizes the teacher's decision process as a directed acyclic graph and mirrors it with modular concept predictors in the student. GCP enhances sample efficiency through a graph-aware acquisition strategy that targets uncertainty and disagreement at critical reasoning nodes. Additionally, it improves training stability and efficiency by performing targeted sub-module retraining, which attributes downstream loss to specific concept predictors and updates only the most influential modules. Experiments on eight NLP classification benchmarks demonstrate that GCP enhances performance under limited annotation budgets while yielding more interpretable and controllable training dynamics. Code is available at: https://github.com/Ziyang-Yu/GCP.
Received — 5 March 2026 ⏭

Adaptive Social Learning via Mode Policy Optimization for Language Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minzheng Wang · Yongbin Li · Haobo Wang · Xinghua Zhang · Nan Xu · Bingli Wu · Fei Huang · Haiyang Yu · Wenji Mao
4 March 2026 at 13:00
arXiv:2505.02156v5 Announce Type: replace-cross Abstract: Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current studies. Existing methods either lack explicit reasoning or employ lengthy Chain-of-Thought reasoning uniformly across all scenarios, resulting in excessive token usage and inflexible social behaviors in tasks such as negotiation or collaboration. To address this, we propose an $\textbf{A}$daptive $\textbf{S}$ocial $\textbf{L}$earning ($\textbf{ASL}$) framework in this paper, aiming to improve the adaptive reasoning ability of language agents in dynamic social interactions. To this end, we first identify the hierarchical reasoning modes under such context, ranging from intuitive response to deep deliberation based on the cognitive control theory. We then develop the $\textbf{A}$daptive $\textbf{M}$ode $\textbf{P}$olicy $\textbf{O}$ptimization ($\textbf{AMPO}$) algorithm to learn the context-aware mode adaptation and reasoning. Our framework advances existing research in three key aspects: (1) Multi-granular reasoning mode design, (2) Context-aware mode switching in rich social interaction, and (3) Token-efficient reasoning with depth adaptation. Extensive experiments on the benchmark social intelligence environment verify that ASL achieves 15.6% higher task performance than GPT-4o. Notably, our AMPO outperforms GRPO by 7.0% with 32.8% shorter thinking chains, demonstrating the advantages of our AMPO and the learned adaptive reasoning ability over GRPO's solution.
Received — 25 February 2026 ⏭

ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haibo Tong · Feifei Zhao · Linghao Feng · Ruoyu Wu · Ruolin Chen · Lu Jia · Zhou Zhao · Jindong Li · Tenglong Li · Erliang Lin · Shuai Yang · Enmeng Lu · Yinqian Sun · Qian Zhang · Zizhe Ruan · Jinyu Fan · Zeyang Yue · Ping Wu · Huangrui Li · Chengyi Sun · Yi Zeng
24 February 2026 at 13:00
arXiv:2602.14135v3 Announce Type: replace Abstract: Rapidly evolving AI exhibits increasingly strong autonomy and goal-directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control, and potentially irreversible. However, current AI safety evaluation systems suffer from critical limitations such as restricted risk dimensions and failed frontier risk detection. The lagging safety benchmarks and alignment technologies can hardly address the complex challenges posed by cutting-edge AI models. To bridge this gap, we propose the "ForesightSafety Bench" AI Safety Evaluation Framework, beginning with 7 major Fundamental Safety pillars and progressively extends to advanced Embodied AI Safety, AI4Science Safety, Social and Environmental AI risks, Catastrophic and Existential Risks, as well as 8 critical industrial safety domains, forming a total of 94 refined risk dimensions. To date, the benchmark has accumulated tens of thousands of structured risk data points and assessment results, establishing a widely encompassing, hierarchically clear, and dynamically evolving AI safety evaluation framework. Based on this benchmark, we conduct systematic evaluation and in-depth analysis of over twenty mainstream advanced large models, identifying key risk patterns and their capability boundaries. The safety capability evaluation results reveals the widespread safety vulnerabilities of frontier AI across multiple pillars, particularly focusing on Risky Agentic Autonomy, AI4Science Safety, Embodied AI Safety, Social AI Safety and Catastrophic and Existential Risks. Our benchmark is released at https://github.com/Beijing-AISI/ForesightSafety-Bench. The project website is available at https://foresightsafety-bench.beijing-aisi.ac.cn/.

Group Representational Position Encoding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Zhang · Zixiang Chen · Yifeng Liu · Zhen Qin · Huizhuo Yuan · Kangping Xu · Yang Yuan · Quanquan Gu · Andrew Chi-Chih Yao
24 February 2026 at 13:00
arXiv:2512.07805v4 Announce Type: replace-cross Abstract: We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions. GRAPE unifies two families of mechanisms: (i) multiplicative rotations (Multiplicative GRAPE) in $\operatorname{SO}(d)$ and (ii) additive logit biases (Additive GRAPE) arising from unipotent actions in the general linear group $\mathrm{GL}$. In Multiplicative GRAPE, a position $n \in \mathbb{Z}$ (or $t \in \mathbb{R}$) acts as $\mathbf{G}(n) = \exp(n \, \omega \, \mathbf{L})$ with a rank-2 skew-symmetric generator $\mathbf{L} \in \mathbb{R}^{d \times d}$, yielding a relative, compositional, norm-preserving map with a closed-form matrix exponential. RoPE is recovered exactly when the $d/2$ planes correspond to canonical coordinate pairs with a log-uniform spectrum. Learned commuting subspaces and compact non-commuting mixtures strictly extend this geometry to capture cross-subspace feature coupling at $O(d)$ and $O(r d)$ cost per head, respectively. In Additive GRAPE, additive logits arise from rank-1 (or low-rank) unipotent actions, recovering ALiBi and the Forgetting Transformer (FoX) as exact special cases while preserving an exact relative law and streaming cacheability. Overall, GRAPE provides a principled design space for positional geometry in long-context models, subsuming RoPE and ALiBi as special cases. Project page: https://github.com/model-architectures/GRAPE.
There are no more articles
❌