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Received — 6 April 2026 ⏭

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaowei Hu · Daniel S Araujo · Chachrit Khunsriraksakul · Lida Wang · Quan Sun · Jia Wen · Lingbo Zhou · Lynette Ekunwe · Leslie A Lange · Ethan M Lange · Stephen B Montgomery · Alexander P Reiner · Francois Aguet · Kristin G Ardlie · Tuuli Lappalainen · Christopher R Gignoux · Esteban G Burchard · Kent D Taylor · Xiuqing Guo · Jerome I Rotter · Stephen S Rich · Elaine Cornell · Peter Durda · Russell P Tracy · Yongmei Liu · W Craig Johnson · George P Papanicolaou · Minoli A Perera · Michael H Cho · Dajiang J Liu · Laura M Raffield · Yun Li · TOPMed Multi-Omics Working Group · Heather E Wheeler · Hae Kyung Im · Ani Manichaikul
3 April 2026 at 18:00

Am J Hum Genet. 2026 Apr 2;113(4):828-841. doi: 10.1016/j.ajhg.2026.03.008.

ABSTRACT

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models' prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait's relevance to the PBMCs used to build our transcriptome prediction models.

PMID:41932314 | DOI:10.1016/j.ajhg.2026.03.008

Received — 2 April 2026 ⏭

PAR$^2$-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xingyu Li · Rongguang Wang · Yuying Wang · Mengqing Guo · Chenyang Li · Tao Sheng · Sujith Ravi · Dan Roth
1 April 2026 at 12:00
arXiv:2603.29085v1 Announce Type: new Abstract: Large language models (LLMs) remain brittle on multi-hop question answering (MHQA), where answering requires combining evidence across documents through retrieval and reasoning. Iterative retrieval systems can fail by locking onto an early low-recall trajectory and amplifying downstream errors, while planning-only approaches may produce static query sets that cannot adapt when intermediate evidence changes. We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a two-stage framework that separates \emph{coverage} from \emph{commitment}. PAR$^2$-RAG first performs breadth-first anchoring to build a high-recall evidence frontier, then applies depth-first refinement with evidence sufficiency control in an iterative loop. Across four MHQA benchmarks, PAR$^2$-RAG consistently outperforms existing state-of-the-art baselines, compared with IRCoT, PAR$^2$-RAG achieves up to \textbf{23.5\%} higher accuracy, with retrieval gains of up to \textbf{10.5\%} in NDCG.
Received — 26 March 2026 ⏭

PersonalQ: Select, Quantize, and Serve Personalized Diffusion Models for Efficient Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qirui Wang · Qi Guo · Yiding Sun · Junkai Yang · Dongxu Zhang · Shanmin Pang · Qing Guo
25 March 2026 at 12:00
arXiv:2603.22943v1 Announce Type: new Abstract: Personalized text-to-image generation lets users fine-tune diffusion models into repositories of concept-specific checkpoints, but serving these repositories efficiently is difficult for two reasons: natural-language requests are often ambiguous and can be misrouted to visually similar checkpoints, and standard post-training quantization can distort the fragile representations that encode personalized concepts. We present PersonalQ, a unified framework that connects checkpoint selection and quantization through a shared signal -- the checkpoint's trigger token. Check-in performs intent-aligned selection by combining intent-aware hybrid retrieval with LLM-based reranking over checkpoint context and asks a brief clarification question only when multiple intents remain plausible; it then rewrites the prompt by inserting the selected checkpoint's canonical trigger. Complementing this, Trigger-Aware Quantization (TAQ) applies trigger-aware mixed precision in cross-attention, preserving trigger-conditioned key/value rows (and their attention weights) while aggressively quantizing the remaining pathways for memory-efficient inference. Experiments show that PersonalQ improves intent alignment over retrieval and reranking baselines, while TAQ consistently offers a stronger compression-quality trade-off than prior diffusion PTQ methods, enabling scalable serving of personalized checkpoints without sacrificing fidelity.

Not All Tokens Are Created Equal: Query-Efficient Jailbreak Fuzzing for LLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenyu Chen · Xiangtao Meng · Chuanchao Zang · Li Wang · Xinyu Gao · Jianing Wang · Peng Zhan · Zheng Li · Shanqing Guo
25 March 2026 at 12:00
arXiv:2603.23269v1 Announce Type: cross Abstract: Large Language Models(LLMs) are widely deployed, yet are vulnerable to jailbreak prompts that elicit policy-violating outputs. Although prior studies have uncovered these risks, they typically treat all tokens as equally important during prompt mutation, overlooking the varying contributions of individual tokens to triggering model refusals. Consequently, these attacks introduce substantial redundant searching under query-constrained scenarios, reducing attack efficiency and hindering comprehensive vulnerability assessment. In this work, we conduct a token-level analysis of refusal behavior and observe that token contributions are highly skewed rather than uniform. Moreover, we find strong cross-model consistency in refusal tendencies, enabling the use of a surrogate model to estimate token-level contributions to the target model's refusals. Motivated by these findings, we propose TriageFuzz, a token-aware jailbreak fuzzing framework that adapts the fuzz testing approach with a series of customized designs. TriageFuzz leverages a surrogate model to estimate the contribution of individual tokens to refusal behaviors, enabling the identification of sensitive regions within the prompt. Furthermore, it incorporates a refusal-guided evolutionary strategy that adaptively weights candidate prompts with a lightweight scorer to steer the evolution toward bypassing safety constraints. Extensive experiments on six open-source LLMs and three commercial APIs demonstrate that TriageFuzz achieves comparable attack success rates (ASR) with significantly reduced query costs. Notably, it attains a 90% ASR with over 70% fewer queries compared to baselines. Even under an extremely restrictive budget of 25 queries, TriageFuzz outperforms existing methods, improving ASR by 20-40%.
Received — 5 March 2026 ⏭

Catch Me If You Can Describe Me: Open-Vocabulary Camouflaged Instance Segmentation with Diffusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tuan-Anh Vu · Duc Thanh Nguyen · Qing Guo · Nhat Chung · Binh-Son Hua · Ivor W. Tsang · Sai-Kit Yeung
5 March 2026 at 13:00
arXiv:2312.17505v2 Announce Type: replace-cross Abstract: Text-to-image diffusion techniques have shown exceptional capabilities in producing high-quality, dense visual predictions from open-vocabulary text. This indicates a strong correlation between visual and textual domains in open concepts and that diffusion-based text-to-image models can capture rich and diverse information for computer vision tasks. However, we found that those advantages do not hold for learning of features of camouflaged individuals because of the significant blending between their visual boundaries and their surroundings. In this paper, while leveraging the benefits of diffusion-based techniques and text-image models in open-vocabulary settings, we aim to address a challenging problem in computer vision: open-vocabulary camouflaged instance segmentation (OVCIS). Specifically, we propose a method built upon state-of-the-art diffusion empowered by open-vocabulary to learn multi-scale textual-visual features for camouflaged object representation learning. Such cross-domain representations are desirable in segmenting camouflaged objects where visual cues subtly distinguish the objects from the background, and in segmenting novel object classes which are not seen in training. To enable such powerful representations, we devise complementary modules to effectively fuse cross-domain features, and to engage relevant features towards respective foreground objects. We validate and compare our method with existing ones on several benchmark datasets of camouflaged and generic open-vocabulary instance segmentation. The experimental results confirm the advances of our method over existing ones. We believe that our proposed method would open a new avenue for handling camouflages such as computer vision-based surveillance systems, wildlife monitoring, and military reconnaissance.
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