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Received — 17 March 2026 ⏭

FC-Track: Overlap-Aware Post-Association Correction for Online Multi-Object Tracking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Cheng Ju · Zejing Zhao · Akio Namiki
16 March 2026 at 12:00
arXiv:2603.12758v1 Announce Type: cross Abstract: Reliable multi-object tracking (MOT) is essential for robotic systems operating in complex and dynamic environments. Despite recent advances in detection and association, online MOT methods remain vulnerable to identity switches caused by frequent occlusions and object overlap, where incorrect associations can propagate over time and degrade tracking reliability. We present a lightweight post-association correction framework (FC-Track) for online MOT that explicitly targets overlap-induced mismatches during inference. The proposed method suppresses unreliable appearance updates under high-overlap conditions using an Intersection over Area (IoA)-based filtering strategy, and locally corrects detection-to-tracklet mismatches through appearance similarity comparison within overlapped tracklet pairs. By preventing short-term mismatches from propagating, our framework effectively mitigates long-term identity switches without resorting to global optimization or re-identification. The framework operates online without global optimization or re-identification, making it suitable for real-time robotic applications. We achieve 81.73 MOTA, 82.81 IDF1, and 66.95 HOTA on the MOT17 test set with a running speed of 5.7 FPS, and 77.52 MOTA, 80.90 IDF1, and 65.67 HOTA on the MOT20 test set with a running speed of 0.6 FPS. Specifically, our framework FC-Track produces only 29.55% long-term identity switches, which is substantially lower than existing online trackers. Meanwhile, our framework maintains state-of-the-art performance on the MOT20 benchmark.

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

✇cs.AI, q-bio.NC updates on arXiv.org
By: Elena Ryumina (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia) · Maxim Markitantov (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia) · Alexandr Axyonov (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia) · Dmitry Ryumin (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia) · Mikhail Dolgushin (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia) · Denis Dresvyanskiy (ITMO University · St. Petersburg · Russia) · Alexey Karpov (St. Petersburg Federal Research Center of the Russian Academy of Sciences · St. Petersburg · Russia · ITMO University · St. Petersburg · Russia)
16 March 2026 at 12:00
arXiv:2603.13056v1 Announce Type: cross Abstract: Continuous emotion recognition in terms of valence and arousal under in-the-wild (ITW) conditions remains a challenging problem due to large variations in appearance, head pose, illumination, occlusions, and subject-specific patterns of affective expression. We present a multimodal method for valence-arousal estimation ITW. Our method combines three complementary modalities: face, behavior, and audio. The face modality relies on GRADA-based frame-level embeddings and Transformer-based temporal regression. We use Qwen3-VL-4B-Instruct to extract behavior-relevant information from video segments, while Mamba is used to model temporal dynamics across segments. The audio modality relies on WavLM-Large with attention-statistics pooling and includes a cross-modal filtering stage to reduce the influence of unreliable or non-speech segments. To fuse modalities, we explore two fusion strategies: a Directed Cross-Modal Mixture-of-Experts Fusion Strategy that learns interactions between modalities with adaptive weighting, and a Reliability-Aware Audio-Visual Fusion Strategy that combines visual features at the frame-level while using audio as complementary context. The results are reported on the Aff-Wild2 dataset following the 10th Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. Experiments demonstrate that the proposed multimodal fusion strategy achieves a Concordance Correlation Coefficient (CCC) of 0.658 on the Aff-Wild2 development set.
Received — 14 March 2026 ⏭

Noninvasive biomarkers in thymic epithelial tumors: a systematic review of cfDNA/ctDNA detection, molecular profiling, and organoid-based monitoring

✇MRD
By: Raffaele Rocco · Ani Antaramian · Vikram Krishna · Philicia Moonsamy · Andrew R Brownlee · Harmik J Soukiasian
12 March 2026 at 18:00

J Thorac Dis. 2026 Feb 28;18(2):171. doi: 10.21037/jtd-2025-1-2467. Epub 2026 Feb 26.

ABSTRACT

BACKGROUND: Thymic epithelial tumors (TETs), including thymomas and thymic carcinomas, are rare malignancies with limited treatment options and no established biomarkers for surveillance. Circulating cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) provide a non-invasive method for understanding tumor biology, detecting minimal residual disease (MRD), and possibly identifying recurrence. While this approach has added to the management of other solid tumors, its role in TETs remains poorly defined. The objective of this review was to evaluate the feasibility, molecular insights, and clinical utility of cfDNA and ctDNA for diagnosis, molecular profiling, and recurrence monitoring in TETs.

METHODS: This systematic review summarizes the current evidence on cfDNA and ctDNA in TETs. Studies were identifies through systematic searches of PubMed, Embase, Web of Science, MEDLINE, Cochrane Library, and American Society of Clinical Oncology (ASCO) meeting abstracts from inception through July 2025. Eligible studies reported cfDNA or ctDNA analysis in patients with histologically confirmed thymoma or thymic carcinoma, and excluded reviews, commentaries, abstracts without full text, and non-blood based liquid biopsy studies. Data extraction included patient characteristics, assay platforms, mutational findings, and clinical applications. Data were synthesized narratively due to methodological heterogeneity. No formal risk of bias assessment was performed because of the small number of included studies.

RESULTS: Six studies involving 289 patients met inclusion criteria. ctDNA detection was feasible across all studies, with detection rates ranging from 46% to 80%. Recurrent alterations included TP53, CDKN2A/B, KIT, and other variants. Liquid biopsy enabled genomic profiling at diagnosis and dynamic monitoring during treatment. Notably, several studies have suggested that disease recurrence may be detectable through liquid biopsy prior to the appearance of radiographic changes on conventional imaging. Despite these promising observations, evidence remains limited by small sample size, variability in assay methods, and short follow up duration.

CONCLUSIONS: Liquid biopsy approaches based on cfDNA and ctDNA have shown applicability in TETs and provide clinically relevant molecular information in settings where tissue-based analysis is limited. Tumor informed ctDNA strategies show particular promise for postoperative monitoring and longitudinal disease assessment, whereas broader clinical adoption remains investigational. Further prospective, multicenter studies are needed to establish standardized workflows and clarify the role of liquid biopsy across diagnostic, therapeutic, and surveillance contexts in TETs.

PMID:41816481 | PMC:PMC12972770 | DOI:10.21037/jtd-2025-1-2467

The dynamic basis of G-protein recognition and activation by a GPCR

✇Nature - Issue - nature.com science feeds
By: Kazuhiro Kobayashi · Kouki Kawakami · Toshiki E. Matsui · Shun Yokoi · Masahiro Fukuda · Tomohiro J. Narita · Hiroki Arai · Mai Tambo · Takashi Sumikama · Manae Tatsumi · Keitaro Yamashita · Junki Koyanagi · Mai Kugawa · Hisako Ikeda · Ayumi Sumino · Ayori Mitsutake · Brian K. Kobilka · Asuka Inoue · Hideaki E. Kato
11 March 2026 at 08:00

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10228-w

Conventional and time-resolved cryo-electron microscopy reveal how NTSR1 dynamically engages and releases different G proteins, capturing over 20 intermediates and uncovering key mechanistic steps in GDP- and GTP-driven activation, subtype selectivity and distinct dissociation pathways.

Author Correction: Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study

✇Nature Medicine
By: Jiyeon Oh · Soeun Kim · Yesol Yim · Min Seo Kim · Jae Il Shin · Ahmed A. J. Jabbar · Hasan Aalruz · Mohammadreza Abbasian · Abdallah H. A. Abd Al Magied · Samar Abd ElHafeez · Atef Abdelkader · Mawada Hassan Abdelmagied · Parsa Abdi · Mohammad-Amin Abdollahifar · Deldar Morad Abdulah · Auwal Abdullahi · Nigusie Abebaw · Roberto Ariel Abeldaño Zuñiga · Richard Gyan Aboagye · Hassan Abolhassani · Mohamed Abouzid · Lucas Guimarães Abreu · Dariush Abtahi · Rana Kamal Abu Farha · Ibrahim Jatau Abubakar · Eman Abu-Gharbieh · Hana J. Abukhadijah · Salahdein Aburuz · Ahmed Abu-Zaid · Juan Manuel Acuna · Mesafint Molla Adane · Isaac Yeboah Addo · Oyelola A. Adegboye · Victor Adekanmbi · Charles Oluwaseun Adetunji · Temitayo Esther Adeyeoluwa · Rishan Adha · Kishor Adhikari · Ripon Kumar Adhikary · Qorinah Estiningtyas Sakilah Adnani · Aanuoluwapo Adeyimika Afolabi · Rotimi Felix Afolabi · Fatemeh Afrashteh · Arya Afrooghe · Anurag Agrawal · Williams Agyemang-Duah · Aqeel Ahmad · Muayyad M. Ahmad · Sajjad Ahmad · Shahzaib Ahmad · Ali Ahmadi · Ayman Ahmed · Haroon Ah
11 March 2026 at 08:00

Nature Medicine, Published online: 11 March 2026; doi:10.1038/s41591-026-04288-8

Author Correction: Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study
Received — 11 March 2026 ⏭

Breast Cancer Screening Knowledge and Sentiments in Singaporean Women: Mixed Methods Study Using Topic Modeling, Sentiment Analysis, and Structured Questionnaire Data

✇Journal of Medical Internet Research
By: Peh Joo Ho · Zi Lin Lim · Jenny Liu · Nur Khaliesah Mohamed Riza · Ying Jia Chew · Yi Ying Lim · Hui Ling Tan · Su-Ann Goh · Han Boon Oh · Chi Hui Chin · Sing Cheer Kwek · Zhi Peng Zhang · Desmond Luan Seng Ong · Swee Tian Quek · Sujith Wijerathne · Philip Tsau Choong Iau · Mikael Hartman · Jingmei Li
11 March 2026 at 04:15
Background: Mammography screening uptake in Singapore remains below 40% despite campaigns and subsidies. Natural language processing (NLP) can extract nuanced attitudes from free text that fixed response options miss, revealing latent factors influencing breast cancer (BC) screening behavior. Objective: This study characterized women’s attitudes toward mammography using mixed methods data, examined associations between BC awareness and screening willingness, and identified barriers and facilitators through NLP of free-text responses. Methods: We conducted a cross-sectional study within the multicenter cohort in Singapore (October 2021-December 2023). In total, 4169 women aged 35‐59 years (median 48, IQR 43‐54) were recruited via convenience sampling (3 hospitals and 2 polyclinics). Participants completed online structured questionnaires on demographics and screening history, then a BC education quiz with feedback. Participants answering >80% correctly were classified as “BC-aware.” Posteducation, participants reported screening willingness (motivated or neutral) with optional free-text explanations. Logistic regression models (adjusted for study site, age, ethnicity, marital status, housing, and education) examined the associations with willingness. For 3819 English-language respondents, biterm topic modeling identified themes and sentiment analysis quantified emotional tone. Statistical significance: =.05. Results: Overall, 79% (3287/4169) were BC-aware, and 94% (3908/4169) reported increased motivation posteducation. BC-aware women had higher screening motivation than BC-unaware women (adjusted odds ratio [aOR] 2.88, 95% CI 2.19‐3.80;

Towards a more efficient bias detection in financial language models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Firas Hadj Kacem · Ahmed Khanfir · Mike Papadakis
10 March 2026 at 12:00
arXiv:2603.08267v1 Announce Type: new Abstract: Bias in financial language models constitutes a major obstacle to their adoption in real-world applications. Detecting such bias is challenging, as it requires identifying inputs whose predictions change when varying properties unrelated to the decision, such as demographic attributes. Existing approaches typically rely on exhaustive mutation and pairwise prediction analysis over large corpora, which is effective but computationally expensive-particularly for large language models and can become impractical in continuous retraining and releasing processes. Aiming at reducing this cost, we conduct a large-scale study of bias in five financial language models, examining similarities in their bias tendencies across protected attributes and exploring cross-model-guided bias detection to identify bias-revealing inputs earlier. Our study uses approximately 17k real financial news sentences, mutated to construct over 125k original-mutant pairs. Results show that all models exhibit bias under both atomic (0.58\%-6.05\%) and intersectional (0.75\%-5.97\%) settings. Moreover, we observe consistent patterns in bias-revealing inputs across models, enabling substantial reuse and cost reduction in bias detection. For example, up to 73\% of FinMA's biased behaviours can be uncovered using only 20\% of the input pairs when guided by properties derived from DistilRoBERTa outputs.

A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic

✇cs.AI, q-bio.NC updates on arXiv.org
By: Peter Brodeur, Jacob M. Koshy, Anil Palepu, Khaled Saab, Ava Homiar, Roma Ruparel, Charles Wu, Ryutaro Tanno, Joseph Xu, Amy Wang, David Stutz, Hannah M. Ferrera, David Barrett, Lindsey Crowley, Jihyeon Lee, Spencer E. Rittner, Ellery Wulczyn, Selena K. Zhang, Elahe Vedadi, Christine G. Kohn, Kavita Kulkarni, Vinay Kadiyala, Sara Mahdavi, Wendy Du, Jessica Williams, David Feinbloom, Renee Wong, Tao Tu, Petar Sirkovic, Alessio Orlandi, Christopher Semturs, Yun Liu, Juraj Gottweis, Dale R. Webster, Jo\"elle Barral, Katherine Chou, Pushmeet Kohli, Avinatan Hassidim, Yossi Matias, James Manyika, Rob Fields, Jonathan X. Li, Marc L. Cohen, Vivek Natarajan, Mike Schaekermann, Alan Karthikesalingam, Adam Rodman
10 March 2026 at 12:00
arXiv:2603.08448v1 Announce Type: cross Abstract: Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Scale Space Diffusion Soumik Mukhopadhyay · Prateksha Udhayanan · Abhinav Shrivastava
    arXiv:2603.08709v1 Announce Type: cross Abstract: Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images - raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the
     
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Scale Space Diffusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Soumik Mukhopadhyay · Prateksha Udhayanan · Abhinav Shrivastava
10 March 2026 at 12:00
arXiv:2603.08709v1 Announce Type: cross Abstract: Diffusion models degrade images through noise, and reversing this process reveals an information hierarchy across timesteps. Scale-space theory exhibits a similar hierarchy via low-pass filtering. We formalize this connection and show that highly noisy diffusion states contain no more information than small, downsampled images - raising the question of why they must be processed at full resolution. To address this, we fuse scale spaces into the diffusion process by formulating a family of diffusion models with generalized linear degradations and practical implementations. Using downsampling as the degradation yields our proposed Scale Space Diffusion. To support Scale Space Diffusion, we introduce Flexi-UNet, a UNet variant that performs resolution-preserving and resolution-increasing denoising using only the necessary parts of the network. We evaluate our framework on CelebA and ImageNet and analyze its scaling behavior across resolutions and network depths. Our project website ( https://prateksha.github.io/projects/scale-space-diffusion/ ) is available publicly.

Online Neural Networks for Change-Point Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mikhail Hushchyn · Kenenbek Arzymatov · Denis Derkach
10 March 2026 at 12:00
arXiv:2010.01388v2 Announce Type: replace-cross Abstract: Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear computational complexity and are suitable for change-point detection in large time series. We compare them with the best known algorithms on various synthetic and real world data sets. Experiments show that the proposed methods outperform known approaches. We also prove the convergence of the algorithms to the optimal solutions and describe conditions rendering current approach more powerful than offline one.

Wasserstein Gradient Flows for Scalable and Regularized Barycenter Computation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Eduardo Fernandes Montesuma · Yassir Bendou · Mike Gartrell
10 March 2026 at 12:00
arXiv:2510.04602v3 Announce Type: replace-cross Abstract: Wasserstein barycenters provide a principled approach for aggregating probability measures, while preserving the geometry of their ambient space. Existing discrete methods are not scalable as they assume access to the complete set of samples from the input measures. Meanwhile, neural network approaches do scale well, but rely on complex optimization problems and cannot easily incorporate label information. We address these limitations through gradient flows in the space of probability measures. Through time discretization, we achieve a scalable algorithm that i) relies on mini-batch optimal transport, ii) accepts modular regularization through task-aware functions, and iii) seamlessly integrates supervised information into the ground-cost. We empirically validate our approach on domain adaptation benchmarks that span computer vision, neuroscience, and chemical engineering. Our method establishes a new state-of-the-art barycenter solver, with labeled barycenters consistently outperforming unlabeled ones.

Impact of LLMs news Sentiment Analysis on Stock Price Movement Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Walid Siala (SnT · University of Luxembourg · Luxembourg) · Ahmed Khanfir (RIADI · ENSI · University of Manouba · Tunisia · SnT · University of Luxembourg · Luxembourg) · Mike Papadakis (SnT · University of Luxembourg · Luxembourg)
10 March 2026 at 12:00
arXiv:2602.00086v3 Announce Type: replace-cross Abstract: This paper addresses stock price movement prediction by leveraging LLM-based news sentiment analysis. Earlier works have largely focused on proposing and assessing sentiment analysis models and stock movement prediction methods, however, separately. Although promising results have been achieved, a clear and in-depth understanding of the benefit of the news sentiment to this task, as well as a comprehensive assessment of different architecture types in this context, is still lacking. Herein, we conduct an evaluation study that compares 3 different LLMs, namely, DeBERTa, RoBERTa and FinBERT, for sentiment-driven stock prediction. Our results suggest that DeBERTa outperforms the other two models with an accuracy of 75% and that an ensemble model that combines the three models can increase the accuracy to about 80%. Also, we see that sentiment news features can benefit (slightly) some stock market prediction models, i.e., LSTM-, PatchTST- and tPatchGNN-based classifiers and PatchTST- and TimesNet-based regression tasks models.
  • ✇Nature Medicine
  • Mosquito-borne viruses, vaccine-borne hope Mike May
    Nature Medicine, Published online: 09 March 2026; doi:10.1038/d41591-026-00014-6From chikungunya and dengue to yellow fever and Zika, mosquito‑transmitted diseases are spreading with urbanization, travel and climate change. A new generation of vaccines, trials and public‑health tools aim to keep ahead of the threat.
     
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Received — 5 March 2026 ⏭

Cryo-SWAN: the Multi-Scale Wavelet-decomposition-inspired Autoencoder Network for molecular density representation of molecular volumes

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Li · Artsemi Yushkevich · Mikhail Kudryashev · Artur Yakimovich
5 March 2026 at 13:00
arXiv:2603.03342v1 Announce Type: cross Abstract: Learning robust representations of 3D shapes from voxelized data is essential for advancing AI methods in biomedical imaging. However, most contemporary 3D computer vision approaches operate on point clouds, meshes, or octrees, while volumetric density maps, the native format of structural biology and cryo-EM, remain comparatively underexplored. We present Cryo-SWAN, a voxel-based variational autoencoder inspired by multi-scale wavelet decomposition. The model performs conditional coarse-to-fine latent encoding and recursive residual quantization across perception scales, enabling accurate capture of both global geometry and high-frequency structural detail in molecular density volumes. Evaluated on ModelNet40, BuildingNet, and a newly curated dataset of cryo-EM volumes, ProteinNet3D, Cryo-SWAN consistently improves reconstruction quality over state-of-the-art 3D autoencoders. We demonstrate that the molecular densities organize in learned latent space according to shared geometric features, while integration with diffusion models enables denoising and conditional shape generation. Together, Cryo-SWAN is a practical framework for data-driven structural biology and volumetric imaging.

Sleeper Cell: Injecting Latent Malice Temporal Backdoors into Tool-Using LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bhanu Pallakonda · Mikkel Hindsbo · Sina Ehsani · Prag Mishra
5 March 2026 at 13:00
arXiv:2603.03371v1 Announce Type: cross Abstract: The proliferation of open-weight Large Language Models (LLMs) has democratized agentic AI, yet fine-tuned weights are frequently shared and adopted with limited scrutiny beyond leaderboard performance. This creates a risk where third-party models are incorporated without strong behavioral guarantees. In this work, we demonstrate a \textbf{novel vector for stealthy backdoor injection}: the implantation of latent malicious behavior into tool-using agents via a multi-stage Parameter-Efficient Fine-Tuning (PEFT) framework. Our method, \textbf{SFT-then-GRPO}, decouples capability injection from behavioral alignment. First, we use SFT with LoRA to implant a "sleeper agent" capability. Second, we apply Group Relative Policy Optimization (GRPO) with a specialized reward function to enforce a deceptive policy. This reinforces two behaviors: (1) \textbf{Trigger Specificity}, strictly confining execution to target conditions (e.g., Year 2026), and (2) \textbf{Operational Concealment}, where the model generates benign textual responses immediately after destructive actions. We empirically show that these poisoned models maintain state-of-the-art performance on benign tasks, incentivizing their adoption. Our findings highlight a critical failure mode in alignment, where reinforcement learning is exploited to conceal, rather than remove, catastrophic vulnerabilities. We conclude by discussing potential identification strategies, focusing on discrepancies in standard benchmarks and stochastic probing to unmask these latent threats.

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maksim Kuznetsov · Zulfat Miftahutdinov · Rim Shayakhmetov · Mikolaj Mizera · Roman Schutski · Bogdan Zagribelnyy · Ivan Ilin · Nikita Bondarev · Thomas MacDougall · Mathieu Reymond · Mihir Bafna · Kaeli Kaymak-Loveless · Eugene Babin · Maxim Malkov · Mathias Lechner · Ramin Hasani · Alexander Amini · Vladimir Aladinskiy · Alex Aliper · Alex Zhavoronkov
5 March 2026 at 13:00
arXiv:2603.03517v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain.

Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ramin Hashemi · Vismika Ranasinghe · Teemu Veijalainen · Petteri Kela · Risto Wichman
4 March 2026 at 13:00
arXiv:2603.02745v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) communication systems, particularly those leveraging multi-user multiple-input and multiple-output (MU-MIMO) with hybrid beamforming, face challenges in optimizing user throughput and minimizing latency due to the high complexity of dynamic beam selection and management. This paper introduces a deep reinforcement learning (DRL) approach for enhancing user throughput in multi-panel mmWave radio access networks in a practical network setup. Our DRL-based formulation utilizes an adaptive beam management strategy that models the interaction between the communication agent and its environment as a Markov decision process (MDP), optimizing beam selection based on real-time observations. The proposed framework exploits spatial domain (SD) characteristics by incorporating the cross-correlation between the beams in different antenna panels, the measured reference signal received power (RSRP), and the beam usage statistics to dynamically adjust beamforming decisions. As a result, the spectral efficiency is improved and end-to-end latency is reduced. The numerical results demonstrate an increase in throughput of up to 16% and a reduction in latency by factors 3-7x compared to baseline (legacy beam management).

Scores Know Bobs Voice: Speaker Impersonation Attack

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chanwoo Hwang · Sunpill Kim · Yong Kiam Tan · Tianchi Liu · Seunghun Paik · Dongsoo Kim · Mondal Soumik · Khin Mi Mi Aung · Jae Hong Seo
4 March 2026 at 13:00
arXiv:2603.02781v1 Announce Type: cross Abstract: Advances in deep learning have enabled the widespread deployment of speaker recognition systems (SRSs), yet they remain vulnerable to score-based impersonation attacks. Existing attacks that operate directly on raw waveforms require a large number of queries due to the difficulty of optimizing in high-dimensional audio spaces. Latent-space optimization within generative models offers improved efficiency, but these latent spaces are shaped by data distribution matching and do not inherently capture speaker-discriminative geometry. As a result, optimization trajectories often fail to align with the adversarial direction needed to maximize victim scores. To address this limitation, we propose an inversion-based generative attack framework that explicitly aligns the latent space of the synthesis model with the discriminative feature space of SRSs. We first analyze the requirements of an inverse model for score-based attacks and introduce a feature-aligned inversion strategy that geometrically synchronizes latent representations with speaker embeddings. This alignment ensures that latent updates directly translate into score improvements. Moreover, it enables new attack paradigms, including subspace-projection-based attacks, which were previously infeasible due to the absence of a faithful feature-to-audio mapping. Experiments show that our method significantly improves query efficiency, achieving competitive attack success rates with on average 10x fewer queries than prior approaches. In particular, the enabled subspace-projection-based attack attains up to 91.65% success using only 50 queries. These findings establish feature-aligned inversion as a key tool for evaluating the robustness of modern SRSs against score-based impersonation threats.

Learning of Population Dynamics: Inverse Optimization Meets JKO Scheme

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mikhail Persiianov · Jiawei Chen · Petr Mokrov · Alexander Tyurin · Evgeny Burnaev · Alexander Korotin
4 March 2026 at 13:00
arXiv:2506.01502v3 Announce Type: replace-cross Abstract: Learning population dynamics involves recovering the underlying process that governs particle evolution, given evolutionary snapshots of samples at discrete time points. Recent methods frame this as an energy minimization problem in probability space and leverage the celebrated JKO scheme for efficient time discretization. In this work, we introduce $\texttt{iJKOnet}$, an approach that combines the JKO framework with inverse optimization techniques to learn population dynamics. Our method relies on a conventional $\textit{end-to-end}$ adversarial training procedure and does not require restrictive architectural choices, e.g., input-convex neural networks. We establish theoretical guarantees for our methodology and demonstrate improved performance over prior JKO-based methods. The code of $\texttt{iJKOnet}$ is available at https://github.com/MuXauJl11110/iJKOnet.
Received — 25 February 2026 ⏭

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mateen A. Abbasi, Tommi J. Mikkonen, Petri J. Ihantola, Muhammad Waseem, Pekka Abrahamsson, Niko K. M\"akitalo
24 February 2026 at 13:00
arXiv:2602.19718v1 Announce Type: cross Abstract: The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities. At the same time, organizations are increasingly embedding governance mechanisms into GenAI-assisted development to support trust, transparency, and accountability. However, these governance mechanisms introduce additional computational workloads, including repeated inference, regeneration cycles, and expanded validation pipelines, increasing energy use and the carbon footprint of GenAI-assisted development. This paper proposes Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers. CAGG comprises three components: (i) an Energy and Carbon Provenance Ledger, (ii) a Carbon Budget Manager, and (iii) a Green Validation Orchestrator, operationalized through governance policies and reusable design patterns.
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