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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.

ContextBudget: Budget-Aware Context Management for Long-Horizon Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yong Wu · YanZhao Zheng · TianZe Xu · ZhenTao Zhang · YuanQiang Yu · JiHuai Zhu · Chao Ma · BinBin Lin · BaoHua Dong · HangCheng Zhu · RuoHui Huang · Gang Yu
3 April 2026 at 12:00
arXiv:2604.01664v1 Announce Type: new Abstract: LLM-based agents show strong potential for long-horizon reasoning, yet their context size is limited by deployment factors (e.g., memory, latency, and cost), yielding a constrained context budget. As interaction histories grow, this induces a trade-off between retaining past information and staying within the context limit. To address this challenge, we propose Budget-Aware Context Management (BACM), which formulates context management as a sequential decision problem with a context budget constraint. It enables agents to assess the available budget before incorporating new observations and decide when and how much of the interaction history to compress. We further develop BACM-RL, an end-to-end curriculum-based reinforcement learning approach that learns compression strategies under varying context budgets. Experiments on compositional multi-objective QA and long-horizon web browsing benchmarks show that BACM-RL consistently outperforms prior methods across model scales and task complexities, achieving over $1.6\times$ gains over strong baselines in high-complexity settings, while maintaining strong advantages as budgets shrink, where most methods exhibit a downward performance trend.
Received — 2 April 2026 ⏭

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4

✇Oncogene - Issue - nature.com science feeds
By: Shengjie Wang · Xin Tong · Runfeng Sun · Xinyi Xia · Donglai Chen · Zhao Wang · Hao Shi · Chenzhuo Wu · Xiaoxiao Guo · Die Hu · Ersuo Jin · Hong-Tao Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03752-6

STAT3-mediated transactivation of NOVA2 promotes lung adenocarcinoma metastasis by splicing SMAD4

Consensus statement on ctDNA minimal residual disease (MRD) testing in early-stage NSCLC - A Delphi study by the Asian Thoracic Oncology Research Group (ATORG)

✇MRD
By: Aaron C Tan · Bin-Chi Liao · Molly Li · David Lee · Yuji Uehara · Lucksamon Thamlikitkul · Jia-Tao Zhang · Meimei Zheng · Chee Khoon Lee · Nick Pavlakis · Thomas John · Ross A Soo · Anders Skanderup · Pei Jye Voon · Beung-Chul Ahn · Sehhoon Park · Hidetoshi Hayashi · Yasushi Goto · Hidehito Horinouchi · Yasushi Yatabe · Thanyanan Reungwetwattana · James Chih-Hsin Yang · Dong-Wan Kim · Tony Mok · Daniel S W Tan · Yi-Long Wu · Myung-Ju Ahn
28 March 2026 at 18:00

J Thorac Oncol. 2026 Mar 26:103696. doi: 10.1016/j.jtho.2026.103696. Online ahead of print.

ABSTRACT

INTRODUCTION: Minimal residual disease (MRD) detection using liquid biopsy is an emerging tool for risk stratification and monitoring for recurrence in resected early-stage NSCLC. There is increasing need for clear guidance on its optimal clinical implementation.

METHODS: The Asian Thoracic Oncology Research Group (ATORG) convened a multi-disciplinary panel of 27 experts to develop a consensus statement on the clinical application of ctDNA-based MRD testing in early-stage resected NSCLC, using a structured Delphi methodology. Statements were organized into broad thematic domains: Assay validity and standardization; Harmonization in research and trials; Clinical application; Challenges in implementation; Consensus recommendations; Infrastructure for regional MRD adoption; and Roadmap for pragmatic trials.

RESULTS: A total of 23 position statements were developed, of which all except one achieved strong consensus. The consensus highlighted the need to define minimum analytical performance thresholds for MRD assays, improve standardization of reporting metrics, and clear guidelines for pre-analytical handling. Harmonization of blood sampling timepoints and terminology across clinical trials is also essential to confirm the prognostic value of MRD assays. While current MRD assays demonstrate high specificity and positive predictive value, variable sensitivity precludes routine use for adjuvant therapy de-escalation outside clinical trials. Broader access, sustainable funding, ongoing consensus building and collaborative real-world data generation are also critical to support clinical implementation and adoption. Future clinical trials must account for the distinct biology and changing standards of care associated with different driver genes.

CONCLUSION: These consensus recommendations provide a pragmatic framework to guide the responsible integration of MRD testing into clinical research and practice.

PMID:41903701 | DOI:10.1016/j.jtho.2026.103696

Received — 11 March 2026 ⏭

Annealed Co-Generation: Disentangling Variables via Progressive Pairwise Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hantao Zhang · Jieke Wu · Mingda Xu · Xiao Hu · Yingxuan You · Pascal Fua
10 March 2026 at 12:00
arXiv:2603.06615v1 Announce Type: cross Abstract: For multivariate co-generation in scientific applications, we advocate pairwise block rather than joint modeling of all variables. This design mitigates the computational burden and data imbalance. To this end, we propose an Annealed Co-Generation (ACG) framework that replaces high-dimensional diffusion modeling with a low-dimensional diffusion model, which enables multivariate co-generation by composing pairwise variable generations. We first train an unconditional diffusion model over causal variables that are disentangled into pairs. At inference time, we recover the joint distribution by coupling these pairwise models through shared common variables, enabling coherent multivariate generation without any additional training. By employing a three-stage annealing process-Consensus, Heating, and Cooling-our method enforces consistency across shared common variables and progressively constrains each pairwise data distribution to lie on a learnable manifold, while maintaining high likelihood within each pair. We demonstrate the framework's flexibility and efficacy on two distinct scientific tasks: flow-field completion and antibody generation. All datasets and code will be made publicly available upon publication.

HybridStitch: Pixel and Timestep Level Model Stitching for Diffusion Acceleration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Desen Sun · Jason Hon · Jintao Zhang · Sihang Liu
10 March 2026 at 12:00
arXiv:2603.07815v1 Announce Type: cross Abstract: Diffusion models have demonstrated a remarkable ability in Text-to-Image (T2I) generation applications. Despite the advanced generation output, they suffer from heavy computation overhead, especially for large models that contain tens of billions of parameters. Prior work has illustrated that replacing part of the denoising steps with a smaller model still maintains the generation quality. However, these methods only focus on saving computation for some timesteps, ignoring the difference in compute demand within one timestep. In this work, we propose HybridStitch, a new T2I generation paradigm that treats generation like editing. Specifically, we introduce a hybrid stage that jointly incorporates both the large model and the small model. HybridStitch separates the entire image into two regions: one that is relatively easy to render, enabling an early transition to the smaller model, and another that is more complex and therefore requires refinement by the large model. HybridStitch employs the small model to construct a coarse sketch while exploiting the large model to edit and refine the complex regions. According to our evaluation, HybridStitch achieves 1.83$\times$ speedup on Stable Diffusion 3, which is faster than all existing mixture of model methods.

Taming Modality Entanglement in Continual Audio-Visual Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyang Hong · Qi Yang · Tao Zhang · Zili Wang · Zhaojin Fu · Kun Ding · Bin Fan · Shiming Xiang
10 March 2026 at 12:00
arXiv:2510.17234v2 Announce Type: replace-cross Abstract: Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previously learned ones. However, existing methods mainly focus on coarse-grained tasks, with limitations in addressing modality entanglement in fine-grained continual learning settings. To bridge this gap, we introduce a novel Continual Audio-Visual Segmentation (CAVS) task, aiming to continuously segment new classes guided by audio. Through comprehensive analysis, two critical challenges are identified: 1) multi-modal semantic drift, where a sounding objects is labeled as background in sequential tasks; 2) co-occurrence confusion, where frequent co-occurring classes tend to be confused. In this work, a Collision-based Multi-modal Rehearsal (CMR) framework is designed to address these challenges. Specifically, for multi-modal semantic drift, a Multi-modal Sample Selection (MSS) strategy is proposed to select samples with high modal consistency for rehearsal. Meanwhile, for co-occurence confusion, a Collision-based Sample Rehearsal (CSR) mechanism is designed, allowing for the increase of rehearsal sample frequency of those confusable classes during training process. Moreover, we construct three audio-visual incremental scenarios to verify effectiveness of our method. Comprehensive experiments demonstrate that our method significantly outperforms single-modal continual learning methods.

Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kai Zeng · Zhanqian Wu · Kaixin Xiong · Xiaobao Wei · Xiangyu Guo · Zhenxin Zhu · Kalok Ho · Lijun Zhou · Bohan Zeng · Ming Lu · Haiyang Sun · Bing Wang · Guang Chen · Hangjun Ye · Wentao Zhang
10 March 2026 at 12:00
arXiv:2510.19195v4 Announce Type: replace-cross Abstract: Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos. Existing methods primarily focus on metrics related to generation quality and controllability. However, they often overlook the evaluation of downstream perception tasks, which are $\mathbf{really\ crucial}$ for the performance of autonomous driving. Existing methods usually leverage a training strategy that first pretrains on synthetic data and finetunes on real data, resulting in twice the epochs compared to the baseline (real data only). When we double the epochs in the baseline, the benefit of synthetic data becomes negligible. To thoroughly demonstrate the benefit of synthetic data, we introduce Dream4Drive, a novel synthetic data generation framework designed for enhancing the downstream perception tasks. Dream4Drive first decomposes the input video into several 3D-aware guidance maps and subsequently renders the 3D assets onto these guidance maps. Finally, the driving world model is fine-tuned to produce the edited, multi-view photorealistic videos, which can be used to train the downstream perception models. Dream4Drive enables unprecedented flexibility in generating multi-view corner cases at scale, significantly boosting corner case perception in autonomous driving. To facilitate future research, we also contribute a large-scale 3D asset dataset named DriveObj3D, covering the typical categories in driving scenarios and enabling diverse 3D-aware video editing. We conduct comprehensive experiments to show that Dream4Drive can effectively boost the performance of downstream perception models under various training epochs. Page: https://wm-research.github.io/Dream4Drive/ GitHub Link: https://github.com/wm-research/Dream4Drive
Received — 5 March 2026 ⏭

CoFL: Continuous Flow Fields for Language-Conditioned Navigation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haokun Liu · Zhaoqi Ma · Yicheng Chen · Masaki Kitagawa · Wentao Zhang · Jinjie Li · Moju Zhao
4 March 2026 at 13:00
arXiv:2603.02854v1 Announce Type: cross Abstract: Language-conditioned navigation pipelines often rely on brittle modular components or costly action-sequence generation. To address these limitations, we present CoFL, an end-to-end policy that directly maps a bird's-eye view (BEV) observation and a language instruction to a continuous flow field for navigation. Instead of predicting discrete action tokens or sampling action chunks via iterative denoising, CoFL outputs instantaneous velocities that can be queried at arbitrary 2D projected locations. Trajectories are obtained by numerical integration of the predicted field, producing smooth motion that remains reactive under closed-loop execution. To enable large-scale training, we build a dataset of over 500k BEV image-instruction pairs, each procedurally annotated with a flow field and a trajectory derived from BEV semantic maps built on Matterport3D and ScanNet. By training on a mixed distribution, CoFL significantly outperforms modular Vision-Language Model (VLM)-based planners and generative policy baselines on strictly unseen scenes. Finally, we deploy CoFL zero-shot in real-world experiments with overhead BEV observations across multiple layouts, maintaining reliable closed-loop control and a high success rate.
Received — 25 February 2026 ⏭

AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wentao Zhang · Mingxuan Zhao · Jincheng Gao · Jieshun You · Huaiyu Jia · Yilei Zhao · Bo An · Shuo Sun
24 February 2026 at 13:00
arXiv:2602.18481v1 Announce Type: cross Abstract: The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge tests to interactive trading simulations. However, current evaluations of real-time trading performance overlook a critical failure mode: severe behavioral instability in sequential decision-making under uncertainty. We empirically show that LLM-based trading agents exhibit extreme run-to-run variance, inconsistent action sequences even under deterministic decoding, and irrational action flipping across adjacent time steps. These issues stem from stateless autoregressive architectures lacking persistent action memory, as well as sensitivity to continuous-to-discrete action mappings in portfolio allocation. As a result, many existing financial trading benchmarks produce unreliable, non-reproducible, and uninformative evaluations. To address these limitations, we propose AlphaForgeBench, a principled framework that reframes LLMs as quantitative researchers rather than execution agents. Instead of emitting trading actions, LLMs generate executable alpha factors and factor-based strategies grounded in financial reasoning. This design decouples reasoning from execution, enabling fully deterministic and reproducible evaluation while aligning with real-world quantitative research workflows. Experiments across multiple state-of-the-art LLMs show that AlphaForgeBench eliminates execution-induced instability and provides a rigorous benchmark for assessing financial reasoning, strategy formulation, and alpha discovery.

Synthesizing Multimodal Geometry Datasets from Scratch and Enabling Visual Alignment via Plotting Code

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haobo Lin · Tianyi Bai · Chen Chen · Jiajun Zhang · Bohan Zeng · Wentao Zhang · Binhang Yuan
24 February 2026 at 13:00
arXiv:2602.18745v1 Announce Type: cross Abstract: Multimodal geometry reasoning requires models to jointly understand visual diagrams and perform structured symbolic inference, yet current vision--language models struggle with complex geometric constructions due to limited training data and weak visual--symbolic alignment. We propose a pipeline for synthesizing complex multimodal geometry problems from scratch and construct a dataset named \textbf{GeoCode}, which decouples problem generation into symbolic seed construction, grounded instantiation with verification, and code-based diagram rendering, ensuring consistency across structure, text, reasoning, and images. Leveraging the plotting code provided in GeoCode, we further introduce code prediction as an explicit alignment objective, transforming visual understanding into a supervised structured prediction task. GeoCode exhibits substantially higher structural complexity and reasoning difficulty than existing benchmarks, while maintaining mathematical correctness through multi-stage validation. Extensive experiments show that models trained on GeoCode achieve consistent improvements on multiple geometry benchmarks, demonstrating both the effectiveness of the dataset and the proposed alignment strategy. The code will be available at https://github.com/would1920/GeoCode.

TAG: Thinking with Action Unit Grounding for Facial Expression Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haobo Lin · Tianyi Bai · Jiajun Zhang · Xuanhao Chang · Sheng Lu · Fangming Gu · Zengjie Hu · Wentao Zhang
24 February 2026 at 13:00
arXiv:2602.18763v1 Announce Type: cross Abstract: Facial Expression Recognition (FER) is a fine-grained visual understanding task where reliable predictions require reasoning over localized and meaningful facial cues. Recent vision--language models (VLMs) enable natural language explanations for FER, but their reasoning is often ungrounded, producing fluent yet unverifiable rationales that are weakly tied to visual evidence and prone to hallucination, leading to poor robustness across different datasets. We propose TAG (Thinking with Action Unit Grounding), a vision--language framework that explicitly constrains multimodal reasoning to be supported by facial Action Units (AUs). TAG requires intermediate reasoning steps to be grounded in AU-related facial regions, yielding predictions accompanied by verifiable visual evidence. The model is trained via supervised fine-tuning on AU-grounded reasoning traces followed by reinforcement learning with an AU-aware reward that aligns predicted regions with external AU detectors. Evaluated on RAF-DB, FERPlus, and AffectNet, TAG consistently outperforms strong open-source and closed-source VLM baselines while simultaneously improving visual faithfulness. Ablation and preference studies further show that AU-grounded rewards stabilize reasoning and mitigate hallucination, demonstrating the importance of structured grounded intermediate representations for trustworthy multimodal reasoning in FER. The code will be available at https://github.com/would1920/FER_TAG .
Received — 17 February 2026 ⏭

DAIAN: Deep Adaptive Intent-Aware Network for CTR Prediction in Trigger-Induced Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhihao Lv · Longtao Zhang · Ailong He · Shuzhi Cao · Shuguang Han · Jufeng Chen
17 February 2026 at 13:00
arXiv:2602.13971v1 Announce Type: cross Abstract: Recommendation systems are essential for personalizing e-commerce shopping experiences. Among these, Trigger-Induced Recommendation (TIR) has emerged as a key scenario, which utilizes a trigger item (explicitly represents a user's instantaneous interest), enabling precise, real-time recommendations. Although several trigger-based techniques have been proposed, most of them struggle to address the intent myopia issue, that is, a recommendation system overemphasizes the role of trigger items and narrowly focuses on suggesting commodities that are highly relevant to trigger items. Meanwhile, existing methods rely on collaborative behavior patterns between trigger and recommended items to identify the user's preferences, yet the sparsity of ID-based interaction restricts their effectiveness. To this end, we propose the Deep Adaptive Intent-Aware Network (DAIAN) that dynamically adapts to users' intent preferences. In general, we first extract the users' personalized intent representations by analyzing the correlation between a user's click and the trigger item, and accordingly retrieve the user's related historical behaviors to mine the user's diverse intent. Besides, sparse collaborative behaviors constrain the performance in capturing items associated with user intent. Hence, we reinforce similarity by leveraging a hybrid enhancer with ID and semantic information, followed by adaptive selection based on varying intents. Experimental results on public datasets and our industrial e-commerce datasets demonstrate the effectiveness of DAIAN.
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