❌

Normal view

Received — 15 September 2026 ⏭

MAIA: Multi-Agent Intent Articulation for Requirement Discovery in Art Commissions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu-Chao Wang · Yanhong Lu · Yingjie Victor Chen · Tim McGraw
14 September 2026 at 12:00
arXiv:2609.12097v1 Announce Type: cross Abstract: In bespoke art commissions, laypeople know what they feel but lack the words to specify it: one participant wanted a laid-off truck driver depicted as "a ghost in his own machine" but left the medium, scale, and palette unsaid. We frame this as an articulation bottleneck at an under-served upstream stage: requirement discovery, which precedes any artist or image generator and forces the commissioner to constitute intent in the first place. We present MAIA (Multi-Agent Intent Articulation), a multi-agent system that scaffolds this stage through Socratic inquiry under a "Verification over Invention" rule, turning vague affect into a text-only brief of visual terms the user verifies. In a within-subjects study (N = 16), the full configuration produced a large, significant gain in Cognitive Support over a minimal baseline (r = 0.96, p_FDR = 0.015; LMM p_FDR
Received — 13 September 2026 ⏭

NBR1-Mediated Autophagic Degradation of YTHDF1 Curtails <em>FDX1</em> Translation to Drive Concurrent Multikinase Inhibitor Resistance and Cuproptosis Tolerance

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Weikai Wang · Xin Jiang · Xiaona Chen · Kaiyu Liang · Miaoqin Chen · Kaizhong Lu · Qi Wei · Yuchen Wu · Liyuan Zhu · Yuan Chen · Tianlun Hou · Jingfeng Luo · Chentao Li · Xian Wang · Hongchuan Jin · Lifeng Feng
12 September 2026 at 18:00

Cancer Commun (Lond). 2026 Sep 11;46:0048. doi: 10.34133/cancomm.0048. eCollection 2026.

ABSTRACT

Background: Cancer cells frequently acquire adaptive resistance to targeted therapies; however, strategies capable of concurrently overcoming treatment tolerance and reactivating cell death pathways are currently lacking. Here, we investigated the dual role of ferredoxin 1 (FDX1) in modulating both multikinase inhibitor (MKI) sensitivity and cuproptosis susceptibility in hepatocellular carcinoma (HCC), and sought to develop a therapeutic approach for reversing resistance. Methods: HCC models, both in vitro and in vivo, were employed to investigate the role of FDX1 in MKI resistance and cuproptosis evasion. Polysome profiling, SunTag translation reporters, CRISPR-Cas9 mutagenesis, and mass spectrometry were employed to delineate the underlying mechanisms. A codelivery nanoliposome system was engineered and tested in orthotopic HCC models. Results: Prolonged exposure to MKIs led to the down-regulation of FDX1 protein levels, resulting in MKI resistance and cuproptosis tolerance in HCC both in vitro and in vivo. Mechanistically, we found that MKIs inactivated protein kinase B (PKB, also known as AKT)-mechanistic target of rapamycin (mTOR) signaling, thereby suppressing the SET and MYND domain-containing protein 2 (SMYD2)-mediated methylation of YTH domain family protein 1 (YTHDF1) at lysine 515 (K515). Hypomethylated YTHDF1 was degraded via next to BRCA1 gene 1 protein (NBR1)-dependent autophagy, leading to the repression of N6-methyladenosine modification-dependent translation of FDX1 mRNA. FDX1 deficiency drove MKI resistance by reactivating AKT survival signaling while impairing cuproptosis through reduced divalent copper ions (Cu2+) to monovalent copper ions (Cu+) conversion and the loss of protein lipoylation. Additionally, restoring FDX1 expression through NBR1 knockdown or YTHDF1 overexpression overcame MKI resistance and resensitized HCC cells to cuproptosis. Finally, a nanoliposomal system, super cuproptosis detonator liposome, designed for the codelivery of NBR1 small interfering RNA, a copper ionophore, and sorafenib restored FDX1-dependent cuproptosis and exhibited marked anti-HCC efficacy, suppressing HCC growth in vivo. Conclusions: MKIs suppressed SMYD2-mediated YTHDF1 methylation at K515 via the inactivation of AKT-mTOR signaling. This led to the inhibition of FDX1 translation, resulting in AKT signaling reactivation and protein lipoylation impairment, effects that contributed to both MKI resistance and cuproptosis tolerance in HCC. Overcoming MKI resistance and resensitizing cells to cuproptosis by targeting NBR1-mediated YTHDF1 degradation using a nanoliposomal codelivery system represents a promising strategy for HCC treatment.

PMID:42729649 | PMC:PMC13562797 | DOI:10.34133/cancomm.0048

Received — 27 May 2026 ⏭

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Yang · Ziyang Gong · Weiquan Huang · Qihao Yang · Ziwei Zhou · Zisu Huang · Yan Li · Xuemei Gao · Qi Dai · Bei Liu · Kai Qiu · Yuqing Yang · Dongdong Chen · Xue Yang · Chong Luo
26 May 2026 at 12:00
arXiv:2605.23904v2 Announce Type: replace Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization. Code: https://aka.ms/skillopt

SREBP2 regulates CCDC25 expression and promotes tumor metastasis in Triple-Negative Breast Cancer

✇Oncogenesis - nature.com science feeds
By: Xingxian Lv · Hao Wang · Beibei Liang · Liyun Yang · Zhuozhuo Han · Wei Xie · Xiuhong Lu · Hao Yang · Gang Huang · Jian Zhao
13 April 2026 at 08:00

Oncogenesis, Published online: 13 April 2026; doi:10.1038/s41389-026-00614-4

SREBP2 regulates CCDC25 expression and promotes tumor metastasis in Triple-Negative Breast Cancer
Received — 2 April 2026 ⏭

QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiazheng Li · Hongzhou Lin · Hong Lu · Kaiyue Wen · Zaiwen Yang · Jiaxuan Gao · Yi Wu · Jingzhao Zhang
1 April 2026 at 12:00
arXiv:2507.13266v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a central paradigm for training large language models (LLMs) in reasoning tasks. Yet recent studies question RL's ability to incentivize reasoning capacity beyond the base model. This raises a key challenge: how can RL be adapted to solve harder reasoning problems more effectively? To address this challenge, we propose a simple yet effective strategy via Question Augmentation: introduce partial solutions during training to reduce problem difficulty and provide more informative learning signals. Our method, QuestA, when applied during RL training on math reasoning tasks, not only improves pass@1 but also pass@k-particularly on problems where standard RL struggles to make progress. This enables continual improvement over strong open-source models such as DeepScaleR and OpenMath Nemotron, further enhancing their reasoning capabilities. We achieve new state-of-the-art results on math benchmarks using 1.5B-parameter models: 72.50% (+10.73%) on AIME24, 62.29% (+12.79%) on AIME25, and 41.67% (+10.11%) on HMMT25. Code, data and model are available at https://github.com/foreverlasting1202/QuestA.
Received — 11 March 2026 ⏭

AutoFigure-Edit: Generating Editable Scientific Illustration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhen Lin · Qiujie Xie · Minjun Zhu · Shichen Li · Qiyao Sun · Enhao Gu · Yiran Ding · Ke Sun · Fang Guo · Panzhong Lu · Zhiyuan Ning · Yixuan Weng · Yue Zhang
10 March 2026 at 12:00
arXiv:2603.06674v1 Announce Type: cross Abstract: High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a website for easy access and interactive use at https://deepscientist.cc/.

TDM-R1: Reinforcing Few-Step Diffusion Models with Non-Differentiable Reward

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihong Luo · Tianyang Hu · Weijian Luo · Jing Tang
10 March 2026 at 12:00
arXiv:2603.07700v1 Announce Type: cross Abstract: While few-step generative models have enabled powerful image and video generation at significantly lower cost, generic reinforcement learning (RL) paradigms for few-step models remain an unsolved problem. Existing RL approaches for few-step diffusion models strongly rely on back-propagating through differentiable reward models, thereby excluding the majority of important real-world reward signals, e.g., non-differentiable rewards such as humans' binary likeness, object counts, etc. To properly incorporate non-differentiable rewards to improve few-step generative models, we introduce TDM-R1, a novel reinforcement learning paradigm built upon a leading few-step model, Trajectory Distribution Matching (TDM). TDM-R1 decouples the learning process into surrogate reward learning and generator learning. Furthermore, we developed practical methods to obtain per-step reward signals along the deterministic generation trajectory of TDM, resulting in a unified RL post-training method that significantly improves few-step models' ability with generic rewards. We conduct extensive experiments ranging from text-rendering, visual quality, and preference alignment. All results demonstrate that TDM-R1 is a powerful reinforcement learning paradigm for few-step text-to-image models, achieving state-of-the-art reinforcement learning performances on both in-domain and out-of-domain metrics. Furthermore, TDM-R1 also scales effectively to the recent strong Z-Image model, consistently outperforming both its 100-NFE and few-step variants with only 4 NFEs. Project page: https://github.com/Luo-Yihong/TDM-R1
There are no more articles
❌