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Received — 27 May 2026 ⏭

Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuheng Jing · Kai Li · Ziwen Zhang · Jiajun Zhang · Zeyao Ma · Jiaxi Yang · Lei Zhang · Zhe Wu · Jinmin He · Junliang Xing · Jian Cheng
26 May 2026 at 12:00
arXiv:2605.24423v1 Announce Type: new Abstract: In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with unknown partners is required-remains unexplored. To rigorously evaluate this, we introduce a large-scale benchmark ICRL4AHT, built upon a high-throughput JAX implementation of Overcooked-V2. Our benchmark includes a large, diverse teammate suite spanning both RL and heuristic policies, enabling controlled train-test shifts, and provides a reproducible end-to-end pipeline for teammate generation, learning-history collection, dataset construction, and online multi-episode evaluation. We evaluate representative history-conditioned ICRL algorithms, including Algorithm Distillation (AD) and Decision-Pretrained Transformer (DPT), across millions of transitions. Results reveal notable limitations: contrary to their success in single-agent domains, these baselines fail to exhibit robust test-time adaptation in multi-agent settings. Specifically, these methods frequently underperform random baselines across both unseen teammate and unseen layout tracks, with no clear in-context improvement over long horizons. These findings highlight the challenges of strategic inference under partial observability within the OvercookedV2 AHT protocol, establishing our benchmark as a critical testbed for next-generation coordination algorithms.

Test-Time Deep Thinking to Explore Implicit Rules

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wentong Chen · Xin Cong · Zhong Zhang · Yaxi Lu · Siyuan Zhao · Yesai Wu · Qinyu Luo · Haotian Chen · Yankai Lin · Zhiyuan Liu · Maosong Sun
26 May 2026 at 12:00
arXiv:2605.24828v1 Announce Type: new Abstract: With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in environments governed by implicit rules--hidden constraints that cannot be observed directly and must be inferred through interaction. This causes agents to fall into repetitive trial-and-error loops, ultimately leading to task failure. To address this challenge, we propose Test-Time Exploration (TTExplore), a framework where a thinker component analyzes interaction history to infer these implicit rules and guide an actor. Effective exploration in this setting critically depends on the reasoning ability of the thinker. However, evaluating deep reasoning trajectories is inherently unstable and difficult, which poses a major obstacle to effective training. To overcome this issue, we introduce a novel and stable reinforcement learning pipeline. The core idea is to use accurate task-level scores as indirect rewards to bypass the difficulty of evaluating intermediate reasoning, and to retain only a single thinking node per trajectory to alleviate reward sparsity. Using this pipeline, we train a specialized 7B model, Exp-Thinker. Experiments on five text-based embodied tasks show that TTExplore equipped with Exp-Thinker improves baseline agent performance by an average of $14$-$19$ points, demonstrating the effectiveness of explicitly reasoning about implicit rules.

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minwei Kong · Chonghe Jiang · Ao Qu · Wenbin Ouyang · Zhaoming Zeng · Xiaotong Guo · Zhekai Li · Junyi Li · Yi Fan · Xinshou Zheng · Xi Jing · Yikai Zhang · Zhiwei Liang · Seonghoo Kim · Runqing Yang · Zijian Zhou · Sirui Li · Han Zheng · Wangyang Ying · Ou Zheng · Chonghuan Wang · Jinglong Zhao · Hanzhang Qin · Cathy Wu · Paul Pu Liang · Jinhua Zhao · Hai Wang
26 May 2026 at 12:00
arXiv:2605.25246v2 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems. FrontierOR includes 180 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, expert-verified evaluation suite. We evaluate seven LLMs spanning frontier, cost-effective, and open-source models both in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only 31% of cases in both solution quality and computational efficiency, and even strong coding agents with test-time evolution achieve only 50% on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward a feasible, high-quality, and efficient algorithm.

DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinrui Shi · Kai Liu · Ziqing Zhang · Jianze Li · Anqi Li · Yulun Zhang
26 May 2026 at 12:00
arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, enforcing grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code and models are available at https://github.com/irene-shi/DRScaffold .

Characterization of dysbiosis patterns in gut microbiota of digestive system cancers: an umbrella review

✇Omics in Gastric
By: Jing Wang · Yingkai Liu · Yan Yu · Xiaoli Chen · Genghang Chen · Yang Chen · Xueyin Chen · Shaonan Liu
14 May 2026 at 18:00

Front Microbiol. 2026 Apr 28;17:1782471. doi: 10.3389/fmicb.2026.1782471. eCollection 2026.

ABSTRACT

Digestive system cancers (DSCs) represent a substantial global health burden. In recent years, the role of gut microbiota in the DSCs has garnered considerable attention, but its change pattern during tumor progression and the specific mechanisms are still not fully understood. We conducted a comprehensive systematic review to characterize patterns of gut microbiota dysbiosis across different DSC types and assess their clinical significance. We systematically searched four English and three Chinese databases up to January 2025 to identify systematic reviews focused on the dynamic characteristics of the gut microbiota during gastrointestinal tumorigenesis. Microbiota biodiversity and taxonomic composition were extracted to identify specific signatures associated with DSCs. The ROBIS tool was used to evaluate the methodological quality of the included studies. Ultimately, 59 studies involving six distinct DSC types were included. Data synthesis and comparison revealed distinct microbiota profiles across DSCs. At the phylum level, Bacillota was decreased in esophageal cancer (EC) and pancreatic ductal adenocarcinoma (PDAC), Pseudomonadota was augmented in EC but exhibited divergent trajectories in colorectal cancer (CRC) and PDAC. Genus-level analyses revealed Veillonella enrichment in EC and PDAC, and Fusobacterium outgrowth in EC, gastric cancer (GC) and CRC. Parvimonas and Streptococcus showed a concordant ascending trend in GC and CRC. Prevotella was overrepresented in EC and GC. This synthesis delineates a qualitative landscape of gut microbiota imbalances associated with various DSCs, highlighting the potential for these microbial shifts to serve as markers for early detection and targeted therapy. Multiomics integration and prospective cohort studies should be prioritized to accelerate clinical translation.

PMID:42131199 | PMC:PMC13161176 | DOI:10.3389/fmicb.2026.1782471

Received — 4 April 2026 ⏭

NEMESIS: Noise-suppressed Efficient MAE with Enhanced Superpatch Integration Strategy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kyeonghun Kim · Hyeonseok Jung · Youngung Han · Hyunsu Go · Eunseob Choi · Seongbin Park · Junsu Lim · Jiwon Yang · Sumin Lee · Insung Hwang · Ken Ying-Kai Liao · Nam-Joon Kim
3 April 2026 at 12:00
arXiv:2604.01612v1 Announce Type: cross Abstract: Volumetric CT imaging is essential for clinical diagnosis, yet annotating 3D volumes is expensive and time-consuming, motivating self-supervised learning (SSL) from unlabeled data. However, applying SSL to 3D CT remains challenging due to the high memory cost of full-volume transformers and the anisotropic spatial structure of CT data, which is not well captured by conventional masking strategies. We propose NEMESIS, a masked autoencoder (MAE) framework that operates on local 128x128x128 superpatches, enabling memory-efficient training while preserving anatomical detail. NEMESIS introduces three key components: (i) noise-enhanced reconstruction as a pretext task, (ii) Masked Anatomical Transformer Blocks (MATB) that perform dual-masking through parallel plane-wise and axis-wise token removal, and (iii) NEMESIS Tokens (NT) for cross-scale context aggregation. On the BTCV multi-organ classification benchmark, NEMESIS with a frozen backbone and a linear classifier achieves a mean AUROC of 0.9633, surpassing fully fine-tuned SuPreM (0.9493) and VoCo (0.9387). Under a low-label regime with only 10% of available annotations, it retains an AUROC of 0.9075, demonstrating strong label efficiency. Furthermore, the superpatch-based design reduces computational cost to 31.0 GFLOPs per forward pass, compared to 985.8 GFLOPs for the full-volume baseline, providing a scalable and robust foundation for 3D medical imaging.
Received — 2 April 2026 ⏭

NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classification

✇cs.AI, q-bio.NC updates on arXiv.org
By: Youngung Han · Minkyung Cha · Kyeonghun Kim · Induk Um · Myeongbin Sho · Joo Young Bae · Jaewon Jung · Jung Hyeok Park · Seojun Lee · Nam-Joon Kim · Woo Kyoung Jeong · Won Jae Lee · Pa Hong · Ken Ying-Kai Liao · Hyuk-Jae Lee
1 April 2026 at 12:00
arXiv:2603.29449v1 Announce Type: cross Abstract: Minimizing invasive diagnostic procedures to reduce the risk of patient injury and infection is a central goal in medical imaging. And yet, noninvasive diagnosis of perineural invasion (PNI), a critical prognostic factor involving infiltration of tumor cells along the surrounding nerve, still remains challenging, due to the lack of clear and consistent imaging criteria criteria for identifying PNI. To address this challenge, we present NeoNet, an integrated end-to-end 3D deep learning framework for PNI prediction in cholangiocarcinoma that does not rely on predefined image features. NeoNet integrates three modules: (1) NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR) algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM) with ControlNet, conditioned on anatomical masks to generate synthetic image patches, specifically balancing the dataset to a 1:1 ratio; and (3) NeoCls, the final prediction module. For NeoCls, we developed the PNI-Attention Network (PattenNet), which uses the frozen LDM encoder and specialized 3D Dual Attention Blocks (DAB) designed to detect subtle intensity variations and spatial patterns indicative of PNI. In 5-fold cross-validation, NeoNet outperformed baseline 3D models and achieved the highest performance with a maximum AUC of 0.7903.

Electric dipole moment drives the dynamics of the TNFR1 complex I signalosome

✇Nature - Issue - nature.com science feeds
By: Jianping Liu · Jing Zhao · Jiayang Gao · Kun Zhao · Yaoyao Han · Jing Yang · Zefei Li · Jianyu Ye · Ziyu Sun · Fengyi Wang · Xinyi Liu · Zekai Li · Siyu Ji · Bo Liu · Cong Liu · Yixiao Zhang · Junying Yuan · James J. Chou
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10304-1

Long-range interactions mediated by protein electric dipole moments have a role in driving the assembly and disassembly of super-signalling complex I for promoting NF-κB signalling.
Received — 11 March 2026 ⏭

First-Order Geometry, Spectral Compression, and Structural Compatibility under Bounded Computation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Changkai Li
10 March 2026 at 12:00
arXiv:2603.08494v1 Announce Type: cross Abstract: Optimization under structural constraints is typically analyzed through projection or penalty methods, obscuring the geometric mechanism by which constraints shape admissible dynamics. We propose an operator-theoretic formulation in which computational or feasibility limitations are encoded by self-adjoint operators defining locally reachable subspaces. In this setting, the optimal first-order improvement direction emerges as a pseudoinverse-weighted gradient, revealing how constraints induce a distorted ascent geometry. We further demonstrate that effective dynamics concentrate along dominant spectral modes, yielding a principled notion of spectral compression, and establish a compatibility principle that characterizes the existence of common admissible directions across multiple objectives. The resulting framework unifies gradient projection, spectral truncation, and multi-objective feasibility within a single geometric structure.
Received — 5 March 2026 ⏭

AttackSeqBench: Benchmarking the Capabilities of LLMs for Attack Sequences Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haokai Ma · Javier Yong · Yunshan Ma · Kuei Chen · Anis Yusof · Zhenkai Liang · Ee-Chien Chang
4 March 2026 at 13:00
arXiv:2503.03170v3 Announce Type: replace-cross Abstract: Cyber Threat Intelligence (CTI) reports document observations of cyber threats, synthesizing evidence about adversaries' actions and intent into actionable knowledge that informs detection, response, and defense planning. However, the unstructured and verbose nature of CTI reports poses significant challenges for security practitioners to manually extract and analyze such sequences. Although large language models (LLMs) exhibit promise in cybersecurity tasks such as entity extraction and knowledge graph construction, their understanding and reasoning capabilities towards behavioral sequences remains underexplored. To address this, we introduce AttackSeqBench, a benchmark designed to systematically evaluate LLMs' reasoning abilities across the tactical, technical, and procedural dimensions of adversarial behaviors, while satisfying Extensibility, Reasoning Scalability, and Domain-dpecific Epistemic Expandability. We further benchmark 7 LLMs, 5 LRMs and 4 post-training strategies across 3 benchmark settings and 3 benchmark tasks within our AttackSeqBench to identify their advantages and limitations in such specific domain. Our findings contribute to a deeper understanding of LLM-driven CTI report understanding and foster its application in cybersecurity operations. Our code of benchmark construction and evaluation and the corresponding dataset are available at: https://github.com/hulkima/AttackSeqBench.
Received — 25 February 2026 ⏭

Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yousef Emami · Hao Zhou · Radha Reddy · Atefeh Hajijamali Arani · Biliang Wang · Kai Li · Luis Almeida · Zhu Han
24 February 2026 at 13:00
arXiv:2602.19534v1 Announce Type: cross Abstract: Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.

JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kai Liu · Wei Li · Lai Chen · Shengqiong Wu · Yanhao Zheng · Jiayi Ji · Fan Zhou · Jiebo Luo · Ziwei Liu · Hao Fei · Tat-Seng Chua
24 February 2026 at 13:00
arXiv:2503.23377v2 Announce Type: replace-cross Abstract: This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Based on the powerful Diffusion Transformer (DiT) architecture, JavisDiT simultaneously generates high-quality audio and video content from open-ended user prompts in a unified framework. To ensure audio-video synchronization, we introduce a fine-grained spatio-temporal alignment mechanism through a Hierarchical Spatial-Temporal Synchronized Prior (HiST-Sypo) Estimator. This module extracts both global and fine-grained spatio-temporal priors, guiding the synchronization between the visual and auditory components. Furthermore, we propose a new benchmark, JavisBench, which consists of 10,140 high-quality text-captioned sounding videos and focuses on synchronization evaluation in diverse and complex real-world scenarios. Further, we specifically devise a robust metric for measuring the synchrony between generated audio-video pairs in real-world content. Experimental results demonstrate that JavisDiT significantly outperforms existing methods by ensuring both high-quality generation and precise synchronization, setting a new standard for JAVG tasks. Our code, model, and data are available at https://javisverse.github.io/JavisDiT-page/.

Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ailin Huang · Ang Li · Aobo Kong · Bin Wang · Binxing Jiao · Bo Dong · Bojun Wang · Boyu Chen · Brian Li · Buyun Ma · Chang Su · Changxin Miao · Changyi Wan · Chao Lou · Chen Hu · Chen Xu · Chenfeng Yu · Chengting Feng · Chengyuan Yao · Chunrui Han · Dan Ma · Dapeng Shi · Daxin Jiang · Dehua Ma · Deshan Sun · Di Qi · Enle Liu · Fajie Zhang · Fanqi Wan · Guanzhe Huang · Gulin Yan · Guoliang Cao · Guopeng Li · Han Cheng · Hangyu Guo · Hanshan Zhang · Hao Nie · Haonan Jia · Haoran Lv · Hebin Zhou · Hekun Lv · Heng Wang · Heung-Yeung Shum · Hongbo Huang · Hongbo Peng · Hongyu Zhou · Hongyuan Wang · Houyong Chen · Huangxi Zhu · Huimin Wu · Huiyong Guo · Jia Wang · Jian Zhou · Jianjian Sun · Jiaoren Wu · Jiaran Zhang · Jiashu Lv · Jiashuo Liu · Jiayi Fu · Jiayu Liu · Jie Cheng · Jie Luo · Jie Yang · Jie Zhou · Jieyi Hou · Jing Bai · Jingcheng Hu · Jingjing Xie · Jingwei Wu · Jingyang Zhang · Jishi Zhou · Junfeng Liu · Junzhe Lin · Ka Man Lo · Kai Liang · Kaibo Liu · Kaijun Tan · Kaiwen Yan · Kaixiang Li · Kang An · Kangheng Lin · Lei Yang · Liang Lv · Liang Zhao · Liangyu Chen · Lieyu Shi · Liguo T
24 February 2026 at 13:00
arXiv:2602.10604v2 Announce Type: replace-cross Abstract: We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.
Received — 17 February 2026 ⏭

Breaking Data Efficiency Dilemma: A Federated and Augmented Learning Framework For Alzheimer's Disease Detection via Speech

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiao Wei · Bin Wen · Yuqin Lin · Kai Li · Mingyang gu · Xiaobao Wang · Longbiao Wang · Jianwu Dang
17 February 2026 at 13:00
arXiv:2602.14655v1 Announce Type: cross Abstract: Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI-based speech detection is non-invasive and cost-effective, it faces a critical data efficiency dilemma due to medical data scarcity and privacy barriers. Therefore, we propose FAL-AD, a novel framework that synergistically integrates federated learning with data augmentation to systematically optimize data efficiency. Our approach delivers three key breakthroughs: First, absolute efficiency improvement through voice conversion-based augmentation, which generates diverse pathological speech samples via cross-category voice-content recombination. Second, collaborative efficiency breakthrough via an adaptive federated learning paradigm, maximizing cross-institutional benefits under privacy constraints. Finally, representational efficiency optimization by an attentive cross-modal fusion model, which achieves fine-grained word-level alignment and acoustic-textual interaction. Evaluated on ADReSSo, FAL-AD achieves a state-of-the-art multi-modal accuracy of 91.52%, outperforming all centralized baselines and demonstrating a practical solution to the data efficiency dilemma. Our source code is publicly available at https://github.com/smileix/fal-ad.
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