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Received today — 7 October 2026 ⏭

Multi-Omics and Computational Pharmacology Approach With Experimental Validation Reveals the Antiproliferative Activity of Sophoricoside Against Pancreatic Cancer

✇Omics in Hepatocellular
By: Peng Lin · Wei Cheng · Xin Qi · Jing Li
30 September 2026 at 18:00

Chem Biodivers. 2026 Oct;23(10):e71778. doi: 10.1002/cbdv.71778.

ABSTRACT

Pancreatic cancer has a dismal prognosis and limited therapeutic options, highlighting an urgent need for effective treatments. Sophoricoside (SOP), a natural isoflavone glycoside, has exhibited anticancer activities in multiple malignancies, including lung cancer, glioblastoma, and hepatocellular carcinoma. We combined cellular assays, network pharmacology, machine learning, and multi-omics to investigate SOP's effects. SOP-inhibited proliferation of MIA PaCa-2, SW1990, and PANC-1 cells dose-dependently. Network pharmacology revealed 85 overlapping targets enriched in MAPK, apoptosis, and PD-L1/PD-1 pathways. Machine learning and differential expression identified PTPN1 as the core target. PTPN1 was markedly upregulated in pancreatic adenocarcinoma, and its high expression correlated with poor survival and immune infiltration. Functional enrichment linked PTPN1 to TGF-β, VEGF, and metabolic reprogramming. Molecular docking suggested a possible binding mode between SOP and PTPN1, involving four predicted hydrogen bonds. SOP reduced PTPN1 mRNA, and PTPN1 knockdown phenocopied SOP's antiproliferative effect with no additivity upon combination. Collectively, this first report demonstrates that SOP restrains pancreatic cancer cell proliferation, with PTPN1 identified as a key functionally required downstream mediator based on integrative computational and functional evidence. This work offers an integrated strategy for mechanistic exploration and highlights PTPN1 as a promising therapeutic biomarker and target for pancreatic cancer.

PMID:42814531 | PMC:PMC13626263 | DOI:10.1002/cbdv.71778

Multi-Omics and Computational Pharmacology Approach With Experimental Validation Reveals the Antiproliferative Activity of Sophoricoside Against Pancreatic Cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Peng Lin · Wei Cheng · Xin Qi · Jing Li
30 September 2026 at 18:00

Chem Biodivers. 2026 Oct;23(10):e71778. doi: 10.1002/cbdv.71778.

ABSTRACT

Pancreatic cancer has a dismal prognosis and limited therapeutic options, highlighting an urgent need for effective treatments. Sophoricoside (SOP), a natural isoflavone glycoside, has exhibited anticancer activities in multiple malignancies, including lung cancer, glioblastoma, and hepatocellular carcinoma. We combined cellular assays, network pharmacology, machine learning, and multi-omics to investigate SOP's effects. SOP-inhibited proliferation of MIA PaCa-2, SW1990, and PANC-1 cells dose-dependently. Network pharmacology revealed 85 overlapping targets enriched in MAPK, apoptosis, and PD-L1/PD-1 pathways. Machine learning and differential expression identified PTPN1 as the core target. PTPN1 was markedly upregulated in pancreatic adenocarcinoma, and its high expression correlated with poor survival and immune infiltration. Functional enrichment linked PTPN1 to TGF-β, VEGF, and metabolic reprogramming. Molecular docking suggested a possible binding mode between SOP and PTPN1, involving four predicted hydrogen bonds. SOP reduced PTPN1 mRNA, and PTPN1 knockdown phenocopied SOP's antiproliferative effect with no additivity upon combination. Collectively, this first report demonstrates that SOP restrains pancreatic cancer cell proliferation, with PTPN1 identified as a key functionally required downstream mediator based on integrative computational and functional evidence. This work offers an integrated strategy for mechanistic exploration and highlights PTPN1 as a promising therapeutic biomarker and target for pancreatic cancer.

PMID:42814531 | PMC:PMC13626263 | DOI:10.1002/cbdv.71778

Received — 27 May 2026 ⏭

Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity

✇Nature - Issue - nature.com science feeds
By: Pantelis A. Nicola · Andrew R. J. Lawson · Alexandra Tidd · Juliette Imbert · Yoshihiro Ishida · Luke A. Wylie · Paul A. Scott · Kenny Roberts · Luke M. R. Harvey · Stefanie V. Lensing · Wei Cheng · Federico Abascal · Daniel Leongamornlert · Yvette Hooks · Matthew Mayho · Nicole Müller-Sienerth · Sara Widaa · Laura Mincarelli · James Illing · Flavia Peci · Bee Ling Ng · Georgeina L. Jarman · Andrew J. C. Russell · Krishnaa T. A. Mahbubani · Kourosh Saeb-Parsy · Anna L. Paterson · Krishna Chatterjee · Raheleh Rahbari · Omer Ali Bayraktar · Michael R. Stratton · Peter J. Campbell · John A. Tadross · Nadia Schoenmakers · Iñigo Martincorena
14 April 2026 at 08:00

Nature, Published online: 14 April 2026; doi:10.1038/s41586-026-10493-9

Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity
Received — 2 April 2026 ⏭

Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus

✇cs.AI, q-bio.NC updates on arXiv.org
By: Huan Zhang · Wei Cheng · Wei Hu
1 April 2026 at 12:00
arXiv:2603.29292v1 Announce Type: cross Abstract: Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly external resources such as powerful teacher models or reliable test units. However, in real-world scenarios, it is much harder to obtain reference solutions and test oracles than problem descriptions and test inputs. In this paper, we tackle a challenging yet realistic question: Can a code language model improve itself without access to a superior teacher and a test oracle? To answer this, we propose ConSelf, a self-improving approach built upon two key ideas. First, we introduce code semantic entropy, a novel metric that measures problem-level uncertainty by assessing the functional diversity of program behaviors, enabling a curriculum construction with the most learnable problems. Second, we present consensus-driven direct preference optimization (Con-DPO), a preference-based fine-tuning method that weights each preference pair by its behavioral consensus, thereby mitigating the impact of noisy self-generated supervision. Experiments on various benchmarks and backbone LLMs demonstrate that ConSelf significantly outperforms baselines, validating the effectiveness of semantic entropy-based curriculum construction and consensus-driven optimization in improving code generation without external supervision.
Received — 5 March 2026 ⏭

Farther the Shift, Sparser the Representation: Analyzing OOD Mechanisms in LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingyu Jin · Yutong Yin · Jingcheng Niu · Qingcheng Zeng · Wujiang Xu · Mengnan Du · Wei Cheng · Zhaoran Wang · Tianlong Chen · Dimitris N. Metaxas
5 March 2026 at 13:00
arXiv:2603.03415v1 Announce Type: cross Abstract: In this work, we investigate how Large Language Models (LLMs) adapt their internal representations when encountering inputs of increasing difficulty, quantified as the degree of out-of-distribution (OOD) shift. We reveal a consistent and quantifiable phenomenon: as task difficulty increases, whether through harder reasoning questions, longer contexts, or adding answer choices, the last hidden states of LLMs become substantially sparser. In short, \textbf{\textit{the farther the shift, the sparser the representations}}. This sparsity--difficulty relation is observable across diverse models and domains, suggesting that language models respond to unfamiliar or complex inputs by concentrating computation into specialized subspaces in the last hidden state. Through a series of controlled analyses with a learning dynamic explanation, we demonstrate that this sparsity is not incidental but an adaptive mechanism for stabilizing reasoning under OOD. Leveraging this insight, we design \textit{Sparsity-Guided Curriculum In-Context Learning (SG-ICL)}, a strategy that explicitly uses representation sparsity to schedule few-shot demonstrations, leading to considerable performance enhancements. Our study provides new mechanistic insights into how LLMs internalize OOD challenges. The source code is available at the URL: https://github.com/MingyuJ666/sparsityLLM.

DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiawei Cheng · Min Gao · Zongwei Wang · Xiaofei Zhu · Zhiyi Liu · Wentao Li · Wei Li · Huan Wu
5 March 2026 at 13:00
arXiv:2603.03782v1 Announce Type: cross Abstract: Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one account. Existing shared-account sequential recommendation (SSR) methods often assume a fixed number of latent users per account, limiting their ability to adapt to diverse sharing patterns and reducing recommendation accuracy. Recent latent reasoning technique applied in sequential recommendation (SR) generate intermediate embeddings from the user embedding (e.g, last item embedding) to uncover users' potential interests, which inspires us to treat the problem of inferring the number of latent users as generating a series of intermediate embeddings, shifting from inferring preferences behind user to inferring the users behind account. However, the last item cannot be directly used for reasoning in SSR, as it can only represent the behavior of the most recent latent user, rather than the collective behavior of the entire account. To address this, we propose DisenReason, a two-stage reasoning method tailored to SSR. DisenReason combines behavior disentanglement stage from frequency-domain perspective to create a collective and unified account behavior representation, which serves as a pivot for latent user reasoning stage to infer the number of users behind the account. Experiments on four benchmark datasets show that DisenReason consistently outperforms all state-of-the-art baselines across four benchmark datasets, achieving relative improvements of up to 12.56\% in MRR@5 and 6.06\% in Recall@20.

ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiayi Zhu · Jianing Zhang · Yiying Yang · Wei Cheng · Xiaoyun Yuan
4 March 2026 at 13:00
arXiv:2603.02697v1 Announce Type: cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction. ShareVerse leverages the generation capability of large video models and integrates three key innovations: 1) A dataset for large-scale multi-agent interactive world modeling is built on the CARLA simulation platform, featuring diverse scenes, weather conditions, and interactive trajectories with paired multi-view videos (front/ rear/ left/ right views per agent) and camera data. 2) We propose a spatial concatenation strategy for four-view videos of independent agents to model a broader environment and to ensure internal multi-view geometric consistency. 3) We integrate cross-agent attention blocks into the pretrained video model, which enable interactive transmission of spatial-temporal information across agents, guaranteeing shared world consistency in overlapping regions and reasonable generation in non-overlapping regions. ShareVerse, which supports 49-frame large-scale video generation, accurately perceives the position of dynamic agents and achieves consistent shared world modeling.
Received — 25 February 2026 ⏭

Agentic AI for Scalable and Robust Optical Systems Control

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zehao Wang · Mingzhe Han · Wei Cheng · Yue-Kai Huang · Philip Ji · Denton Wu · Mahdi Safari · Flemming Holtorf · Kenaish AlQubaisi · Norbert M. Linke · Danyang Zhuo · Yiran Chen · Ting Wang · Dirk Englund · Tingjun Chen
24 February 2026 at 13:00
arXiv:2602.20144v1 Announce Type: cross Abstract: We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410-task benchmark to evaluate request understanding, role-aware responses, multi-step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations--commercial online LLMs and locally hosted open-source LLMs--and compare them with LLM-based code generation baselines. AgentOptics achieves 87.7%--99.0% average task success rates, significantly outperforming code-generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device-level control to system orchestration, monitoring, and closed-loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio-over-fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi-span channel provisioning with launch power optimization; closed-loop fiber polarization stabilization; and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems.
Received — 17 February 2026 ⏭

Interpreting Emergent Extreme Events in Multi-Agent Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ling Tang · Jilin Mei · Dongrui Liu · Chen Qian · Dawei Cheng · Jing Shao · Xia Hu
17 February 2026 at 13:00
arXiv:2601.20538v2 Announce Type: replace-cross Abstract: Large language model-powered multi-agent systems have emerged as powerful tools for simulating complex human-like systems. The interactions within these systems often lead to extreme events whose origins remain obscured by the black box of emergence. Interpreting these events is critical for system safety. This paper proposes the first framework for explaining emergent extreme events in multi-agent systems, aiming to answer three fundamental questions: When does the event originate? Who drives it? And what behaviors contribute to it? Specifically, we adapt the Shapley value to faithfully attribute the occurrence of extreme events to each action taken by agents at different time steps, i.e., assigning an attribution score to the action to measure its influence on the event. We then aggregate the attribution scores along the dimensions of time, agent, and behavior to quantify the risk contribution of each dimension. Finally, we design a set of metrics based on these contribution scores to characterize the features of extreme events. Experiments across diverse multi-agent system scenarios (economic, financial, and social) demonstrate the effectiveness of our framework and provide general insights into the emergence of extreme phenomena.
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