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Normal view

Received — 27 May 2026 ⏭

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism

✇cs.AI, q-bio.NC updates on arXiv.org
By: Long Zhao · Qinghe Wang · Jiaan Zhu · Youhui Bai · Zewen Jin · Chaoyi Ruan · Shengnan Wang · Cheng Li
26 May 2026 at 12:00
arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-stage RLHF pipeline is often bottlenecked by the generation stage, where response-length skew causes the effective batch size to shrink rapidly during decoding, leaving GPUs underutilized while a few long responses remain unfinished. Mainstream frameworks employ a static tensor parallelism (TP) configuration that cannot adapt to changing batch characteristics, leaving substantial performance headroom unexplored. We propose PAT, an adaptive TP method that dynamically reconfigures TP during the generation stage of each RLHF iteration. PAT introduces two key techniques. First, a predictor-guided online reconfiguration method decides both the reconfiguration point and the target TP configuration based on offline profiling, triggering reconfiguration only when the predicted latency benefit outweighs the reconfiguration overhead. Second, a lightweight online reconfiguration mechanism updates only the states and layouts affected by TP changes: it adapts unfinished decoding states through a cost-model-based choice between KV-cache migration and recomputation, performs in-place weight resharding, and reuses cached communication groups. We implement PAT on top of SGLang and integrate it with the VeRL framework. Evaluations on LLaMA3.1-8B and Qwen3-14B using DeepScaleR show that PAT reduces generation latency by up to 34.6% and end-to-end RLHF training iteration latency by up to 27.2% compared to the original VeRL setup.

SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyang Hu · Hongjin Qian · Shuting Wang · Jiongnan Liu · Ziliang Zhao · Jiejun Tan · Zheng Liu · Zhicheng Dou
26 May 2026 at 12:00
arXiv:2605.24468v1 Announce Type: new Abstract: Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these histories grow long, but that information needed for the current decision may be scattered across distant steps and only become relevant later. Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse, but they do not explicitly model how access to past interaction should adapt to the agent's evolving state. We instead cast long-horizon reasoning as a problem of state-adaptive memory. To this end, we propose State-Adaptive Memory~(SAM), a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall. These cues are not treated as replacements for history; rather, they serve as lightweight handles that allow the agent to reconstruct temporally distant information according to its current needs, without retraining the underlying backbone. We further optimize the memory module through expert-guided supervision and reinforcement learning, aligning it with trajectory-level utility. Across BrowseComp, BrowseComp-ZH, WideSearch, and HLE, SAM consistently outperforms strong baselines over diverse agent backbones. Our results suggest that explicit memory modeling provides a simple and effective foundation for long-horizon agentic reasoning.

AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyang Hu · Hongjin Qian · Shuting Wang · Jiongnan Liu · Tong Zhao · Xiaoxi Li · Zheng Liu · Zhicheng Dou
26 May 2026 at 12:00
arXiv:2605.24486v1 Announce Type: new Abstract: Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding. In contrast, much less is understood about scaling out: whether multiple peer agents, all targeting the same task, can become an additional source of capability without relying on explicit role specialization or workflow orchestration. We study this question and propose AgentFugue, a collective reasoning framework built around a shared reasoning hub. As peer agents explore the same task in parallel, the hub records concise notes on what each agent has established, attempted, or ruled out, and enables each agent to selectively access what other agents have discovered in a form useful for its current search. This design turns otherwise isolated trajectories into a connected ecology of reusable intermediate reasoning without requiring centralized planning. We instantiate the hub as a plug-in communication layer, trained with supervised fine-tuning and end-to-end reinforcement learning. Across the challenging long-horizon settings we study, AgentFugue improves over strong baselines. Our results suggest that collective reasoning can turn scaling out peer agent systems into a distinct source of capability gains, rather than merely a way of spending more compute.

Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuxin Zhang · Mengxue Hu · Zheng Lin · Xiaoyi Fan · Fan Xie · Zihan Fang · Jing Yang · Wenjun Zhu · Zhiwen Chen · Chengfei Lv · Zhe Chen
26 May 2026 at 12:00
arXiv:2605.24598v1 Announce Type: new Abstract: Large language model (LLM) agents excel at solving complex long-horizon tasks through autonomous interaction with environments. However, their real-world deployment faces a fundamental device--cloud dilemma: on-device models are efficient but often brittle, while cloud models are stronger but costly in computation. State-of-the-art LLM device--cloud routers usually make coarse task-level decisions, which cannot adapt to the changing difficulty of multi-step agent interactions. To address this issue, we present Hera, a step-level device--cloud LLM agent coordinator for long-horizon tasks achieving a strong performance--cost Pareto frontier. Hera adopts a novel two-stage training paradigm: (1) imitation learning for cold-start, followed by (2) reinforcement learning that jointly optimizes task success and cloud usage efficiency. The first stage casts step-level routing as a supervised classification problem: the device agent is replayed on cloud trajectories, with each state labeled by the agreement between device and cloud actions. In the second stage, we perform cost-aware reinforcement learning by grouping identical states across trajectories and updating Hera with labels favoring higher expected return and fewer future cloud calls. We evaluate Hera on ALFWorld, WebShop, and AppWorld, where it consistently outperforms prior methods, achieving 92.5% of the cloud-only success rate with cloud use in only 46.3% of steps.

Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuanting Xie · Zhaochen Guo · Bingheng Li · Xingtong Yu · Zhifei Liao · Zhao Kang · Yuan Fang
26 May 2026 at 12:00
arXiv:2605.24867v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the principle of clustering as reasoning, offering a $k$-means interpretation of how iterative reasoning operates over graph-structured data. We observe that existing graph CoT methods rely on disjoint architectures and fixed graph representations, limiting step-by-step semantic-topological interaction and interpretability. To overcome this limitation, we propose a unified framework named KCoT that integrates CoT reasoning with graph representation learning. Our key theoretical result reveals a formal mathematical correspondence between a Transformer block and the $k$-means algorithm, allowing reasoning to be interpreted as iterative assignment and update steps. Based on this insight, we introduce a Semantic Discriminating Prompt that explicitly formulates these steps as structured CoT reasoning, together with a structure-grounded alignment strategy to fuse topological priors with evolving thought-conditioned representations. Experiments on standard benchmarks demonstrate consistent improvements over state-of-the-art methods, validating clustering as a principled mechanism for CoT-based graph learning.

CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bowen Wang · Dunjie Lu · Junli Wang · Tianyi Bai · Shixuan Liu · Zhipeng Zhang · Haiquan Wang · Hao Hu · Tianbao Xie · Shuai Bai · Dayiheng Liu · Que Shen · Junyang Lin · Tao Yu
26 May 2026 at 12:00
arXiv:2605.25624v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven breakthroughs in domains such as math, tool-use, and software engineering, yet its extension to computer-use agents (CUAs) has been bottlenecked by the scarcity of scalable training data with deterministic rewards. Constructing such data for CUAs requires consistent task instruction, executable environment, and verifiable reward. However, hand-curated benchmarks achieve high reward fidelity but cover few applications and LLM-as-judge-based datasets scale broadly but lack reliable verification. We present CUA-Gym, a scalable pipeline that co-generates task instructions, environment states, and reward functions. Concretely, a Generator agent constructs the initial and golden environment states, and a separate Discriminator agent writes the reward function from the task specification. An orchestrator agent drives the two through iterative rounds upon execution. Generated tuples then pass a final filter combining LLM majority voting and agent rollouts, ensuring quality beyond the per-task adversarial loop. To address the scarcity of training environments, we further synthesize CUA-Gym-Hub, a broad suite of high-fidelity mock web applications grounded in real-world software-use distributions, expanding the scale of CUA RLVR data by magnitude. Using this pipeline, we construct CUA-Gym, a dataset of 32,112 verified RLVR training tuples grounded in 110 environments. Trained with GSPO on CUA-Gym, our CUA-Gym-A3B and CUA-Gym-A17B achieve 62.1% and 72.6% on OSWorld-Verified, outperforming prior open-source CUAs at comparable scales, with performance scaling smoothly in both data volume and environment diversity. The same checkpoints also improve on the held-out WebArena benchmark, indicating transfer beyond the training environments. We will open-source the full synthesis pipeline, dataset, CUA-Gym-Hub environments, and models.

IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xintong Yang · Hao Gu · Binxing Xu · Lujun Li · Bei Liu · Jiacheng Liu · Qiyuan Zhu · Sirui Han · Yike Guo
26 May 2026 at 12:00
arXiv:2605.25475v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly expected to operate over long contexts, yet standard softmax attention incurs a KV cache that grows linearly with sequence length, quickly becoming the bottleneck for long context inference. A practical remedy is to evict less important KV entries; however, existing eviction policies are largely heuristic and struggle to capture the rich, input-dependent distribution of token importance. In this work, we introduce a learnable indexer that predicts KV importance, enabling more accurate retention of critical tokens. Meanwhile, naively evicting tokens permanently discards their information, leading to irreversible forgetting and degraded retrieval over long ranges. To address this, we propose a lightweight latent memory module that compresses evicted tokens into a compact, online-updated state and provides residual readouts to compensate for the attention contributions lost through KV eviction. Collectively, our method enables accurate long-context inference under a bounded KV budget, delivering consistent improvements on RULER (4K/16K) across Qwen, Mistral, and Llama models (up to 25 points under aggressive eviction), markedly more stable Needle-in-a-Haystack retrieval, and superior LongBench scores and compression curves compared to existing eviction policies.

Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jun-Yu Pan · Yansen Wang · Enze Zhang · Bao-Liang Lu · Wei-Long Zheng · Dongsheng Li
26 May 2026 at 12:00
arXiv:2605.18172v2 Announce Type: replace Abstract: Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models. However, visually-evoked EEG datasets remain scarce, leading existing methods to align neural signals mainly with abstract text, a lossy translation that may discard fine-grained perceptual information encoded in brain activity. We propose Generative Visual Grounding (GVG), a framework that visualizes the invisible by using an EEG-to-image generative model as a visual translator. Instead of forcing EEG into text alone, GVG hallucinates instance-specific proxy images for non-visual EEG, providing structured visual contexts that allow MLLMs to exploit their visual priors for clinical-state interpretation. We validate this idea on two MLLM backbones, GVG-X-Omni and GVG-Janus. Image-only alignment is already competitive: the lightweight GVG-X-Omni matches 1.7B-parameter text-aligned baselines while tuning only 170M parameters on a frozen 7B backbone. We further extend GVG-Janus with trimodal Image+Text alignment, where text supplies categorical semantic anchors and visual proxies enrich neural representations with perceptual details. Experiments show consistent gains in EEG understanding and visual generation, suggesting visual proxy grounding as an effective complement to textual alignment.

FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chunyu Xie · Bin Wang · Fanjing Kong · Jincheng Li · Dawei Liang · Ji Ao · Dawei Leng · Yuhui Yin
26 May 2026 at 12:00
arXiv:2510.10921v3 Announce Type: replace-cross Abstract: Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that remains limited in current models, particularly in non-English settings. While models like CLIP perform well on global alignment, they often struggle to capture fine-grained details in object attributes, spatial relations, and linguistic expressions, with limited support for bilingual comprehension. To address these challenges, we introduce FG-CLIP 2, a bilingual vision-language model designed to advance fine-grained alignment for both English and Chinese. Our approach leverages rich fine-grained supervision, including region-text matching and long-caption modeling, alongside multiple discriminative objectives. We further introduce the Textual Intra-modal Contrastive (TIC) loss to better distinguish semantically similar captions. Trained on a carefully curated mixture of large-scale English and Chinese data, including a newly released 12M Chinese region-text dataset, FG-CLIP 2 achieves powerful bilingual performance. To enable rigorous evaluation, we present a new benchmark for Chinese multimodal understanding, featuring long-caption retrieval and bounding box classification. Extensive experiments on 29 datasets across 8 tasks show that FG-CLIP 2 outperforms existing methods, achieving state-of-the-art results in both languages. We release the model, code, and benchmark to facilitate future research on bilingual fine-grained vision-language alignment.

Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyan Liu · Yeqiu Chen · Hongyi Cai · Tao Lin · Shuo Yang · Zheng Liu · Bo Zhao
26 May 2026 at 12:00
arXiv:2511.16449v5 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and action execution. In real-time deployment, these models must process continuous visual streams, incurring substantial computational overhead. Visual token pruning -- a mainstream technique for accelerating Vision-Language Models (VLMs) by retaining salient tokens while discarding redundant ones -- offers a natural candidate solution to this challenge. However, directly applying VLM-oriented pruning methods to VLA inference can cause severe degradation in manipulation performance. Our analysis attributes this degradation to a key mismatch: VLA inference exhibits distinct attention patterns between the vision-language prefill stage and the action-decode stage, so pruning based only on context-prefill semantic salience is biased toward semantic cues and may remove action-critical visual tokens. Motivated by this observation, we propose VLA-Pruner, an effective plug-and-play token pruning method grounded in the visual requirements of VLA inference, further exploiting the temporal continuity of robot manipulation. Specifically, VLA-Pruner estimates visual-token importance from both semantic prefilling and temporally smoothed action relevance, and then applies a Combine-then-Filter strategy to retain compact, non-redundant tokens under the compute budget. Experiments show that VLA-Pruner outperforms state-of-the-art approaches across multiple VLA architectures, achieving up to 1.99x speedup with comparable manipulation quality.

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihong Tang · Haicheng Liao · Tong Nie · Junlin He · Ao Qu · Kehua Chen · Wei Ma · Zhenning Li · Lijun Sun · Chengzhong Xu
26 May 2026 at 12:00
arXiv:2512.04733v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving (AD) systems increasingly adopt vision-language-action (VLA) models, yet they typically ignore the passenger's emotional state, which is central to comfort and AD acceptance. We introduce Open-Domain End-to-End (OD-E2E) autonomous driving, where an autonomous vehicle (AV) must interpret free-form natural-language commands, infer the emotion, and plan a physically feasible trajectory. We propose E3AD, an emotion-aware VLA framework that augments semantic understanding with two cognitively inspired components: a continuous Valenc-Arousal-Dominance (VAD) emotion model that captures tone and urgency from language, and a dual-pathway spatial reasoning module that fuses egocentric and allocentric views for human-like spatial cognition. A consistency-oriented training scheme, combining modality pretraining with preference-based alignment, further enforces coherence between emotional intent and driving actions. Across real-world datasets, E3AD improves visual grounding and waypoint planning and achieves state-of-the-art (SOTA) VAD correlation for emotion estimation. These evaluation results show that injecting emotion into VLA-style driving yields more human-aligned grounding, planning, and feedback.

RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuecheng Li · Hengwei Ju · Zeyu Song · Wei Yang · Chi Lu · Peng Jiang · Kun Gai
26 May 2026 at 12:00
arXiv:2602.00682v2 Announce Type: replace-cross Abstract: Integrating large language model (LLM) representations into multimodal recommendation has shown promise, yet a fundamental challenge remains largely overlooked: the semantic heterogeneity between generative LM representations and the ID-based collaborative signals that recommendation systems rely on. Naively injecting LM features without alignment degrades recommendation performance rather than improving it. To resolve this, we propose RecGOAT, a dual-granularity semantic alignment framework built on graph neural networks and optimal transport theory. RecGOAT first enriches collaborative semantics through multimodal attentive graphs that capture item-item, user-item, and user-user relationships, initializing user representations via LLM-inferred behavioral preferences. It then aligns LM-derived modality representations with recommendation IDs at two complementary granularities: (1) instance-level alignment via cross-modal contrastive learning (CMCL), which produces discriminative per-sample representations; and (2) distribution-level alignment via optimal adaptive transport (OAT), which minimizes the 1-Wasserstein distance between ID distributions and LLM semantics to produce a unified, consistently aligned feature space. Theoretically, we prove that the unified representation achieves strictly lower target error than any single-modality representation, with the gap bounded by the Wasserstein distance and the InfoNCE loss, providing rigorous guarantees for both alignment consistency and fusion comprehensiveness. Extensive experiments on three public benchmarks demonstrate state-of-the-art performance. Deployment on a large-scale online advertising platform further validates RecGOAT's industrial scalability. Our code is available at https://github.com/6lyc/RecGOAT-LLM4Rec.

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

✇Nature - Issue - nature.com science feeds
By: Min He · Jia Su · Xiaogang Zhou · Tuo Qi · Jiazheng Wang · Tianxin Zhang · Jinhua Chen · Mawsheng Chern · Youpin Xu · Xiang Lu · Qingqing Hou · Hongrui Liu · Wenfang Huang · Jiawei Liu · Shiying Li · Yunfei Zhu · Xue Chen · Senyu Ran · Han Li · Renju Liu · Mingliang Lei · Guosong Shu · Haicheng Liao · Junjie Yin · Xiaobo Zhu · Yongyan Tang · Li Song · Long Wang · Qing Xiong · Jiali Liu · Yu Bi · Yihua Yang · Hai Qing · Bingtian Ma · Yan Li · Jing Fan · Peng Qin · Yuping Wang · Jianming Wu · Wenming Wang · Shigui Li · Weitao Li · Jing Wang · Xuewei Chen
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

A SAUR gene enhances maize drought resilience by promoting silk elongation

✇Nature - Issue - nature.com science feeds
By: Chaohui Zhu · Zhirui Yang · Shiping Yang · Xueyan Zhou · Boxin Liu · Tian Tian · Bochen Zhao · Yingying Xie · Yujun Liu · Jinkui Cheng · Huaijun Tang · Yanjun Zhang · Xiaoqing Xie · Lei Zhang · Cheng Liu · Xingrong Wang · Shengxue Liu · Feng Qin
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10566-9

The Small Auxin Up RNA (SAUR) protein ZmSAUR72 in maize (Zea mays) promotes silk growth via regulation of H+-ATPase activity, and is a key determinant of the anthesis-silking interval and thus resilience to drought.

SHP1 expression in tumor-associated dendritic cells drives immunoevasion via impairing CD8<sup>+</sup> memory T cell responses

✇Oncogenesis - nature.com science feeds
By: Gao Yu · Yusheng Liang · Weize Yu · Gaojian Zhuang · Bing Li · Jiayi Huang · Jinghao Zheng · Xuanqi Yang · Leyun Wen · Xiaoming Tan · Yuan Zhang
15 May 2026 at 08:00

Oncogenesis, Published online: 15 May 2026; doi:10.1038/s41389-026-00627-z

SHP1 expression in tumor-associated dendritic cells drives immunoevasion via impairing CD8+ memory T cell responses

Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms

✇Omics in Hepatocellular
By: Yajie Qi · Kun Li · Pincheng Li · Jianyu Yan · Shuyue Feng · Dan Wan · Ke Du · Xiao Liang · Fan Yang · Erzheng Zhou · Na Huang · Qian Wang · Nanbin Liu
14 May 2026 at 18:00

Cancer Cell Int. 2026 May 14. doi: 10.1186/s12935-026-04330-2. Online ahead of print.

ABSTRACT

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets remain insufficiently explored. Ribosome biogenesis (RB) is a critical biological process linked to various cancers; however, its systematic role in LIHC remains unclear.

METHODS: This study integrated LIHC single-cell RNA-Seq, bulk RNA-Seq, and spatial transcriptomic data with ribosome biogenesis-related gene sets to construct a single-cell atlas of LIHC. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to characterize myeloid cell subsets. Furthermore, an LIHC prognostic risk model based on RB-related genes was developed using 117 machine-learning algorithm combinations. Key findings were subsequently corroborated through experimental validation and clinical sample analysis.

RESULTS: We identified a distinct macrophage subpopulation with high ribosome biogenesis activity, termed ribosome biogenesis-active macrophages (RAMs). These cells exhibited strong communication with inflammatory macrophages, potentially mediated by MIF-related receptor-ligand interactions. We further constructed an 8-gene prognostic model (PA2G4, GNL2, PWP1, DDX49, NOC4L, GDI2, CST7, and RCL1), which showed good predictive performance. Drug sensitivity analysis suggested that the high-risk group may be more responsive to several agents, including docetaxel. Among these genes, GNL2 was selected for further investigation. Elevated GNL2 expression was associated with increased stemness features in myeloid cells. Molecular docking analysis identified several candidate compounds with potential binding affinity to GNL2. Functionally, GNL2 knockdown in macrophages reduced TGF-β and TNF-α expression and was associated with decreased proliferation, migration, and invasion of LIHC cells.

CONCLUSION: We identified a highly active ribosome biogenesis-macrophage subpopulation (RAM), and constructed a robust risk model to aid in the diagnosis, prognosis, and treatment of LIHC. GNL2 is associated with increased expression of TGF-β and TNF-α and may contribute to LIHC progression.

PMID:42135716 | DOI:10.1186/s12935-026-04330-2

Ancient DNA reveals pervasive directional selection across West Eurasia

✇Nature - Issue - nature.com science feeds
By: Ali Akbari · Annabel Perry · Alison R. Barton · Mohammadreza Kariminejad · Steven Gazal · Zheng Li · Yating Zeng · Alissa Mittnik · Nick Patterson · Matthew Mah · Xiang Zhou · Alkes L. Price · Eric S. Lander · Ron Pinhasi · Nadin Rohland · Swapan Mallick · David Reich
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10358-1

Analysis of 15,836 ancient West Eurasian genomes reveals hundreds of instances of directional selection, showing that sustained changes in allele frequency were widespread, rather than being rare over this period as previously assumed.
Received — 13 April 2026 ⏭

Baseline cellular state dictates the molecular impact of KRAS mutant variants in pancreatic cancer cells

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yanixa Quiñones-Avilés · Barbora Salovska · Cassandra S Markham · Yi Di · Benjamin E Turk · Yansheng Liu · Mandar Deepak Muzumdar
10 April 2026 at 18:00

bioRxiv [Preprint]. 2026 Mar 12:2026.03.10.710185. doi: 10.64898/2026.03.10.710185.

ABSTRACT

KRAS is mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), where hotspot alterations in codons 12, 13, and 61 drive tumor initiation and progression. Although distinct biochemical properties have been described for individual KRAS mutants, whether they generate unique allele-specific signaling programs in PDAC cells remains unresolved. Here, we systematically interrogated the molecular consequences of seven common KRAS mutant variants in reconstituted isogenic, KRAS-deficient PDAC cell lines by integrated transcriptomic, proteomic, and phosphoproteomic profiling. We found that baseline cellular state, rather than allele identity, was the predominant driver of molecular variation. Comparisons with established KRAS reference signatures revealed significant but moderate overlap at the mRNA level and less so at the proteome level. Pathway analyses highlighted interferon response and mitochondrial translation as recurrently altered across alleles, while phosphoproteomic data confirmed robust ERK1/2 activity and suppression of DYRK kinase substrates by mutant KRAS expression. Importantly, no robust allele-specific molecular programs were identified. Together, our study establishes a comprehensive multi-omics resource for KRAS signaling in PDAC and demonstrates that cellular context exerts a stronger influence than allele identity in shaping molecular profiles, with implications for interpreting putative allele-specific signaling dependencies and therapeutic vulnerabilities.

PMID:41959224 | PMC:PMC13060958 | DOI:10.64898/2026.03.10.710185

Received — 8 April 2026 ⏭

Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives

✇cs.AI, q-bio.NC updates on arXiv.org
By: Brian Hsuan-Cheng Liao · Chih-Hong Cheng · Hasan Esen · Alois Knoll
7 April 2026 at 12:00
arXiv:2604.03325v1 Announce Type: cross Abstract: Perception plays a central role in connected and autonomous vehicles (CAVs), underpinning not only conventional modular driving stacks, but also cooperative perception systems and recent end-to-end driving models. While deep learning has greatly improved perception performance, its statistical nature makes perfect predictions difficult to attain. Meanwhile, standard training objectives and evaluation benchmarks treat all perception errors equally, even though only a subset is safety-critical. In this paper, we investigate safety-aligned evaluation and optimization for 3D object detection that explicitly characterize high-impact errors. Building on our previously proposed safety-oriented metric, NDS-USC, and safety-aware loss function, EC-IoU, we make three contributions. First, we present an expanded study of single-vehicle 3D object detection models across diverse neural network architectures and sensing modalities, showing that gains under standard metrics such as mAP and NDS may not translate to safety-oriented criteria represented by NDS-USC. With EC-IoU, we reaffirm the benefit of safety-aware fine-tuning for improving safety-critical detection performance. Second, we conduct an ego-centric, safety-oriented evaluation of AV-infrastructure cooperative object detection models, underscoring its superiority over vehicle-only models and demonstrating a safety impact analysis that illustrates the potential contribution of cooperative models to "Vision Zero." Third, we integrate EC-IoU into SparseDrive and show that safety-aware perception hardening can reduce collision rate by nearly 30% and improve system-level safety directly in an end-to-end perception-to-planning framework. Overall, our results indicate that safety-aligned perception evaluation and optimization offer a practical path toward enhancing CAV safety across single-vehicle, cooperative, and end-to-end autonomy settings.
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