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Received — 10 September 2026 ⏭

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingjin Li · Yu Liu · Huayi Liu · Xiang Ye · Chao Jiang · Hongguang Zhang · Yu Ruan
10 September 2026 at 12:00
arXiv:2510.05124v3 Announce Type: replace-cross Abstract: We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.

Multi-Omics Biomarker Signatures for Precision Diagnosis and Prognosis in Primary Liver Cancer: A Literature Review

✇Omics in Hepatocellular
By: Ke Xu · Yu Liu · Yuan Peng · Yanli Zeng · Zhibin Luo
4 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70136. doi: 10.1002/biof.70136.

ABSTRACT

Primary liver cancer (PLC) is a biologically heterogeneous group of malignancies dominated by hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and a smaller subset of combined hepatocellular-cholangiocarcinoma (cHCC-CCA), and its clinical burden remains high because current diagnostic and prognostic tools do not adequately capture molecular diversity. Conventional imaging, serum markers, and histopathological assessment remain insufficient for precise early diagnosis, subtype-resolved classification, and outcome stratification, while tissue and liquid biopsy approaches have expanded the range of analytes available for clinical assessment. Recent studies have identified candidate biomarker signatures across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and circulating layers, suggesting that integrated multi-omics profiling may better represent tumor lineage, clonal evolution, immune context, and therapeutic vulnerability than isolated molecular readouts. However, these layers are not equally mature for clinical use: genomic testing is closest to routine therapeutic application in iCCA, plasma methylation assays are advancing for HCC surveillance augmentation, and many proteomic or metabolomic panels remain validation-stage tools. Their clinical value remains constrained by sampling bias, biospecimen-dependent signal loss, assay standardization, cost, and the need for prospective validation across clinically diverse populations. This narrative review critically synthesizes current evidence on multi-omics biomarker signatures for precision diagnosis and prognosis in primary liver cancer and argues that clinically useful signatures should be question-specific, stage-aware, and specimen-aware rather than universal multi-analyte panels.

PMID:42697859 | PMC:PMC13545153 | DOI:10.1002/biof.70136

CAFs shape the immunosuppressive microenvironment of pancreatic cancer through the Lin28b-STING Axis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Minghe Fan · Ziyang Zhang · Wei Xu · Yuwei Liu · Ruoxuan Wang · Wenqin Hao · Sihan Huo · Yuyao Guo · Lina Wang · Ang Lv · Xiangyu Liu · Wei Wang · Chuanhui Han · Ying Zhao
3 September 2026 at 18:00

Nat Commun. 2026 Aug 7;17(1):9491. doi: 10.1038/s41467-026-76495-3.

ABSTRACT

Cancer-associated fibroblasts comprise diverse functionally distinct cellular subsets, with certain subpopulations exerting pivotal influence in shaping the pancreatic cancer immune microenvironment. Here we show that Lin28b+ cancer-associated fibroblasts contribute to establishing an immunologically cold tumor microenvironment in pancreatic ductal adenocarcinoma. Mechanistically, Lin28b directly binds to STING mRNA and promotes its degradation, thereby suppressing STING expression and downstream type I interferon signaling. Loss of Lin28b in cancer-associated fibroblasts activates the cGAS-STING-interferon signaling cascade, enhancing dendritic cell antigen presentation and CD8+ T cell cytotoxic function. Importantly, genetic inhibition of Lin28b in cancer-associated fibroblasts enhances sensitivity to anti-PD-L1 immune checkpoint blockade therapy. These findings reveal that targeting the Lin28b-STING axis represents a promising therapeutic strategy for overcoming the intrinsic resistance of pancreatic ductal adenocarcinoma to immunotherapy.

PMID:42693143 | PMC:PMC13542369 | DOI:10.1038/s41467-026-76495-3

Received — 27 May 2026 ⏭

HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyu Liu · Haotian Xu · Yanan He · Sarang Rajendra Patil · Mengjia Xu · Tengfei Ma
26 May 2026 at 12:00
arXiv:2605.24140v1 Announce Type: new Abstract: Multi-step reasoning remains a central challenge for large language models: single-pass generation is efficient but lacks accuracy; tree-search methods explore multiple paths but are computation-heavy. We address this gap by distilling reasoning progress into a hyperbolic geometric signal that guides step-by-step generation. Our approach is motivated by a structural observation: in combinatorial reasoning trees, solution-bearing states are few while dead ends are exponentially numerous. The hyperbolic space matches this asymmetry, with compact volume near the origin and exponentially expanding capacity toward the boundary, so that distance-to-origin naturally encodes solution proximity while angular separation distinguishes branches requiring different next operations. We train a lightweight head to project LLM hidden states into this space, then fine-tune a low-rank adapter interactively on its own reasoning attempts to act on the injected signal. Across multiple benchmarks, the geometric signal yields consistent gains, with larger improvements on deeper reasoning chains. Our code is publicly available at https://github.com/yuyuliu11037/HyperGuide.

PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruiwen Gu · Yahao Liu · Zhenyu Liu · Qitai Tan · Xiao-Ping Zhang
26 May 2026 at 12:00
arXiv:2605.25554v1 Announce Type: new Abstract: As a core task in intelligent transportation systems, traffic forecasting plays a critical role in urban traffic management. Accurate traffic forecasting relies on modeling complex spatiotemporal dependencies, which is inherently challenging due to spatial heterogeneity in traffic systems.Despite significant progress, most existing methods are still limited to pairwise spatial dependency modeling, making it difficult to capture dynamic high-order interactions among nodes with similar traffic patterns. To address this issue, we propose PHGNet, a novel spatiotemporal forecasting framework based on prototype-guided hypergraph construction. At the core of PHGNet, a prototype learning mechanism is designed to adaptively assign pattern-similar nodes to hyperedges, thereby capturing high-order interactions with time-varying structures. To improve the reliability of dynamic hypergraph construction, we further develop a global-local node representation module to extract time-consistent features. For forecasting, iterative residual refinement and Temporal Query Attention are introduced to improve forecasting accuracy while supporting efficient parallel decoding. Extensive experiments on multiple real-world datasets demonstrate that PHGNet achieves superior predictive performance compared with state-of-the-art methods.

STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liang Xue · Haoyu Liu · Cheng Wang · Pengyu Chen · Haozhuo Zheng · Yang Liu
26 May 2026 at 12:00
arXiv:2605.25162v1 Announce Type: cross Abstract: Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisition pipelines face a persistent trilemma: expert annotation is expensive, real-world service conversations are constrained by privacy and commercial restrictions, and static corpora quickly become temporally stale. We propose Stream, a data-centric framework that leverages publicly available streaming media (live streams and short videos) to synthesize high-value service dialogues at scale. Stream mines authentic interaction signals from noisy streams and synthesizes conversations by integrating role-grounded persona construction with Conversational Blueprint construction; it further adopts retrieval-augmented generation (RAG) to support knowledge-aware responses. Based on Stream, we release StreamDial, a large-scale multi-domain dataset covering Automotive, Restaurant, and Hotel. StreamDial contains 87,498 dialogue sessions and 1,497,320 turns in total, with an average of 17.11 turns per session and a comparable scale across domains. Each session is organized as a structured quadruplet $\langle P_u, P_a, B, H \rangle$ that pairs dialogue history with explicit user/agent personas and a Conversational Blueprint, capturing realistic service behaviors such as requirement mining, constraint conflicts, negotiation, and recovery. Evaluations with automatic judges and downstream tasks show that StreamDial improves intrinsic dialogue quality over strong baselines, and models trained with StreamDial improve Dialogue State Tracking across backbones; we further report a completed human-evaluation set and encouraging multilingual transfer on Qwen3-8B under a controlled training budget. The data is released in https://github.com/hitxueliang/DialogDataSetBySTREAM.

Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hengyu Liu · Tianyi Li · Zhihong Cui · Yushuai Li · Zhangkai Wu · Torben Bach Pedersen · Kristian Torp · Christian S. Jensen
26 May 2026 at 12:00
arXiv:2605.02010v2 Announce Type: replace Abstract: This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, structured databases) and implicit knowledge (reasoning patterns, debugging processes, intermediate steps). Implicit knowledge remains unexternalized because documentation cost exceeds perceived value -- yet AI learns from it indiscriminately, acquiring both beneficial patterns and harmful biases. Current reliability methods can only verify explicit knowledge against sources, creating a fundamental gap: the most valuable AI capabilities (reasoning, judgment, intuition) are precisely those we cannot verify. We propose Knowledge Objects (KOs) -- structured artifacts that externalize implicit knowledge into forms humans can inspect, verify, and endorse. KOs transform verification economics: what was previously too costly to verify becomes feasible, enabling accumulated human validation to improve reliability over time.

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Huang · Boyu Liu · Linlin Yang · Yanjing Li · Yuguang Yang · Xuhui Liu · Canyu Chen · Zhongqian Fu · Baochang Zhang
26 May 2026 at 12:00
arXiv:2605.10989v3 Announce Type: replace-cross Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including the Straight-Through Estimator (STE) and its improved variants, rely on hand-crafted designs that suffer from gradient mismatch problem and information loss induced by fixed-range gradient clipping. To address this, we propose SURrogate GradiEnt Adaptation (SURGE), a novel learnable gradient compensation framework with theoretical grounding. SURGE mitigates gradient mismatch through auxiliary backpropagation. Specifically, we design a Dual-Path Gradient Compensator (DPGC) that constructs a parallel full-precision auxiliary branch for each binarized layer, decoupling gradient flow via output decomposition during backpropagation. DPGC enables bias-reduced gradient estimation by leveraging the full-precision branch to estimate components beyond STE's first-order approximation. To further enhance training stability, we introduce an Adaptive Gradient Scaler (AGS) based on an optimal scale factor to dynamically balance inter-branch gradient contributions via norm-based scaling. Experiments on image classification, object detection, and language understanding tasks demonstrate that SURGE performs best over state-of-the-art methods.

Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling

✇npj Digital Medicine
By: Xiangyu Liu · Yuanling Tang · Song Zhang · Haiyang Bian · Hanlin Shu · Leen Liao · Xiaolin Pang · Qianting Lv · Jia Chen · Peirong Ding · Ping Liu · Yu Shen · Ziqiang Wang · Shouping Zhu · Jie Tian · Zhenyu Liu · Xin Wang
26 May 2026 at 08:00

npj Digital Medicine, Published online: 26 May 2026; doi:10.1038/s41746-026-02798-w

Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling

Human pancreatic progenitor organoids define genetic and epigenetic barriers to early PDAC transformation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xian Zhang · Wilfred Wong · Hyein S Cho · Dapeng Yang · Gary Dixon · Renhe Luo · Dingyu Liu · Denis Torre · Shigeaki Umeda · Federico González · Gokce Askan · Aslihan Yavas · Jeyaram Ravichandran Damodaran · Robert W Rickert · Rajya Kappagantula · Fong Cheng Pan · Victoria G Aveson · Adrien Grimont · William B Fall · Xianming Wang · Julian Pulecio · Samuel J Kaplan · Heng Pan · Xianlu Laura Peng · Jen Jen Yeh · Steven D Leach · Christine A Iacobuzio-Donahue · Rohit Chandwani · Christina S Leslie · Danwei Huangfu
20 May 2026 at 18:00

Dev Cell. 2026 May 19:S1534-5807(26)00159-0. doi: 10.1016/j.devcel.2026.04.012. Online ahead of print.

ABSTRACT

The lack of accurate human models that recapitulate pancreatic ductal adenocarcinoma (PDAC) initiation has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectories of tumor initiation and progression, validated against clinical datasets and tumor histopathology. We demonstrate that CDKN2A loss, which is nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, whereas SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of the pancreatic lineage program during PDAC initiation, alongside AP-1-driven chromatin remodeling. We identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and hypermethylation of essential pancreatic transcription factors. This model captures genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a potential early intervention strategy.

PMID:42161274 | PMC:PMC13196429 | DOI:10.1016/j.devcel.2026.04.012

Advancing solar and wind penetration in China through energy complementarity

✇Nature - Issue - nature.com science feeds
By: Yuan Hu · Hou Jiang · Chuan Zhang · Jianlong Yuan · Mengting Zhang · Ling Yao · Qiang Chen · Jichao Wu · Hualong Zhang · Subin Ma · Xiang Li · Weiyu Zhang · Quanhua Dong · Congcong Wen · Gege Yin · Fan Zhang · Chaohui Yu · Zhijun Jin · Yu Liu
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10570-z

Using high-resolution satellite imagery combined with a deep-learning-based framework to build a national energy inventory enables a data-driven assessment of solar–wind complementarity strategies to reduce power variability and enhance renewable energy penetration across China.

GPNMB Drives Brain Metastasis by Sculpting a Pathological Endothelial-Immune Interactome

✇Omics In Lung
By: Xuefei Liu · Jun Tan · Chun Wu · Guanyin Huang · Yixin Cheng · Jianyang Hu · Binyu Zhang · Mao Zhao · Boxi Zhao · Jingru Lian · Shuqian Zheng · Lin Zeng · Meng Xu · Yang Xu · Shan Zeng · Hao Yu · Hui Yang · Zhixiang Zuo · Chuanyu Liu · Weineng Feng · Weinan Guo · Chunying Li · Sai-Lan Liu · Qing Liu · Feiqiu Wen · Xin Hong
13 April 2026 at 18:00

Cancer Discov. 2026 Apr 15. doi: 10.1158/2159-8290.CD-25-1663. Online ahead of print.

ABSTRACT

Brain metastases (BM) remain a devastating disease with dismal prognosis. How circulating tumor cells (CTCs) penetrate the blood brain barrier (BBB) and reprogram the brain microenvironment remain unclear. Using spatially resolved multi-omic profiling of CTCs and brain metastases, integrated with experimental and clinical analyses, we identified Glycoprotein Non-Metastatic Melanoma Protein B (GPNMB) as a CTC-secreted driver of vascular disruption and brain colonization. CBX3 upregulation induced GPNMB expression, which bound endothelial EGFR, triggering CBL-mediated ubiquitination and degradation. Attenuated EGFR signaling suppressed FTO and disrupted endothelial junctions via YTHDF2-dependent TJP1 m6A methylation. Remarkably, GPNMB-induced BBB remodeling promoted immune infiltration via CXCL12-CXCR4 axis, and induced time course-dependent T cell exhaustion within the brain microenvironment. Clinically, elevated CBX3⁺GPNMB⁺ CTCs and plasma CXCL12 were significantly associated with BM progression in lung cancer and melanoma. Therapeutically, dual blockade of GPNMB and PD1 enhanced anti-BM efficacy in mice, unveiling GPNMB as a promising target for precision immunotherapy.

PMID:41973996 | DOI:10.1158/2159-8290.CD-25-1663

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

✇Cell
By: Him K. Shrestha · Huan Sun · Jay M. Yarbro · DongGeun Lee · Danting Liu · Erming Wang · Meghan McReynolds · Nan Zhang · Boer Xie · Shu Yang · Kaiwen Yu · Suresh Poudel · Yuxin Li · Zuo-Fei Yuan · Dehui Kong · Minghui Wang · Zhen Wang · Mingming Niu · Hong Wang · Masihuz Zaman · Ju Wang · David R. Vanderwall · Yu Sun · Zhiping Wu · Ping-Chung Chen · Bing Bai · Anthony A. High · Júlia Faura · Chunyu Liu · David A. Bennett · Erik C.B. Johnson · Nicholas T. Seyfried · Allan I. Levey · Vahram Haroutunian · Geidy E. Serrano · Thomas G. Beach · Michael DeTure · Takahisa Kanekiyo · Ronald C. Petersen · Guojun Bu · Pamela J. McLean · Dennis W. Dickson · Rosa Rademakers · Gang Yu · Xusheng Wang · Bin Zhang · Junmin Peng
23 March 2026 at 08:00
A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.
Received — 13 April 2026 ⏭

A Genetically Engineered Human Organoid Model Reveals Distinct Genetic and Epigenetic Barriers of Lineage Plasticity in Early PDAC Transformation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xian Zhang · Wilfred Wong · Hyein S Cho · Dapeng Yang · Gary Dixon · Renhe Luo · Dingyu Liu · Denis Torre · Shigeaki Umeda · Federico González · Gokce Askan · Aslihan Yavas · Jeyaram Ravichandran Damodaran · Robert W Rickert · Rajya Kappagantula · Fong Cheng Pan · Victoria G Aveson · Adrien Grimont · Julian Pulecio · Samuel J Kaplan · Heng Pan · Steven D Leach · Christine A Iacobuzio-Donahue · Rohit Chandwani · Christina S Leslie · Danwei Huangfu
10 April 2026 at 18:00

bioRxiv [Preprint]. 2026 Mar 11:2026.03.09.710586. doi: 10.64898/2026.03.09.710586.

ABSTRACT

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro , validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

PMID:41959451 | PMC:PMC13060829 | DOI:10.64898/2026.03.09.710586

The importance of competition and facilitation for global tree diversity

✇Nature - Issue - nature.com science feeds
By: Han Xu · Matteo Detto · J. Aaron Hogan · Alfonso Alonso · Joseph D. Birch · Pulchérie Bissiengou · Chengjin Chu · Stuart J. Davies · Gunter A. Fischer · Billy C. H. Hau · David Kenfack · Buhang Li · Juyu Lian · Mingxian Lin · Wande Liu · Yu Liu · Zhifa Liu · James A. Lutz · Hervé Roland Memiaghe · Xiangcheng Mi · Vojtech Novotny · Haibao Ren · Jianrong Su · Jill Thompson · Maria Uriarte · Renato Valencia · Tze Leong Yao · Sandra L. Yap · Yicen Zhang · Jess K. Zimmerman · George D. Weiblen · Yide Li · Suqin Fang · Fangliang He
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10349-2

Across 17 forest plots (2.7 million trees, 5,400 species), competition dominated overall, but facilitation was relatively stronger near the equator and declined towards higher latitudes, partly linked to temperature, legumes, mycorrhizal associations and canopy nursing effect.
Received — 8 April 2026 ⏭

XAttnRes: Cross-Stage Attention Residuals for Medical Image Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyu Liu · Qing Xu · Zhen Chen
7 April 2026 at 12:00
arXiv:2604.03297v1 Announce Type: cross Abstract: In the field of Large Language Models (LLMs), Attention Residuals have recently demonstrated that learned, selective aggregation over all preceding layer outputs can outperform fixed residual connections. We propose Cross-Stage Attention Residuals (XAttnRes), a mechanism that maintains a global feature history pool accumulating both encoder and decoder stage outputs. Through lightweight pseudo-query attention, each stage selectively aggregates from all preceding representations. To bridge the gap between the same-dimensional Transformer layers in LLMs and the multi-scale encoder-decoder stages in segmentation networks, XAttnRes introduces spatial alignment and channel projection steps that handle cross-resolution features with negligible overhead. When added to existing segmentation networks, XAttnRes consistently improves performance across four datasets and three imaging modalities. We further observe that XAttnRes alone, even without skip connections, achieves performance on par with the baseline, suggesting that learned aggregation can recover the inter-stage information flow traditionally provided by predetermined connections.
Received — 4 April 2026 ⏭

Infeasibility Aware Large Language Models for Combinatorial Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yakun Wang · Min Chen · Zeguan Wu · Junyu Liu · Sitao Zhang · Zhenwen Shao
3 April 2026 at 12:00
arXiv:2604.01455v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation and do not explicitly address infeasibility detection. We propose an infeasibility-aware framework that combines certifiable dataset construction, supervised fine-tuning, and LLM-assisted downstream search. For the minor-embedding problem, we introduce a new mathematical programming formulation together with provable zero-phase infeasibility screening, which enables scalable construction of training instances labeled either as feasible with structured certificates or as certifiably infeasible. Using training data generated through this exact optimization pipeline, we show that an 8B-parameter LLM can be fine-tuned to jointly perform solution generation and infeasibility detection. We further utilize LLM outputs as warm starts for downstream local search, providing a practical way to accelerate optimization even when the LLM outputs are imperfect. Experiments show that our fine-tuned model improves overall accuracy by up to 30\% over GPT-5.2; meanwhile LLM-guided warm starts provide up to $2\times$ speedup compared with starting from scratch in downstream local search.

Four Generations of Quantum Biomedical Sensors

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xin Jin · Priyam Srivastava · Ronghe Wang · Yuqing Li · Jonathan Beaumariage · Tom Purdy · M. V. Gurudev Dutt · Kang Kim · Kaushik Seshadreesan · Junyu Liu
3 April 2026 at 12:00
arXiv:2603.29944v2 Announce Type: replace-cross Abstract: Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.
Received — 2 April 2026 ⏭

Four Generations of Quantum Biomedical Sensors

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xin Jin · Priyam Srivastava · Ronghe Wang · Yuqing Li · Jonathan Beaumariage · Tom Purdy · M. V. Gurudev Dutt · Kang Kim · Kaushik Seshadreesan · Junyu Liu
1 April 2026 at 12:00
arXiv:2603.29944v1 Announce Type: cross Abstract: Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.
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