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Received — 2 April 2026 ⏭

LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haihong Hao · Lei Chen · Mingfei Han · Changlin Li · Dong An · Yuqiang Yang · Zhihui Li · Xiaojun Chang
1 April 2026 at 12:00
arXiv:2603.29165v1 Announce Type: cross Abstract: Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by actions. As a result, they often lack an effective understanding of the causal relationship between actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can imagine the near future by leveraging action-dynamics causality, which improves both environmental understanding and navigation choices. Inspired by this capability, we propose LatentPilot, a new paradigm that exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics, while requiring no access to future frames at inference. Concretely, we propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution, with an expert takeover triggered when the agent deviates excessively. LatentPilot further learns visual latent tokens without explicit supervision; these latent tokens attend globally in a continuous latent space and are carried across steps, serving as both the current output and the next input, thereby enabling the agent to dream ahead and reason about how actions will affect subsequent observations. Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene. Project page:https://abdd.top/latentpilot/

Liquid Biopsies in HNSCC: Current Landscape and Emerging Opportunities in the Era of HPV Stratification

✇MRD
By: Akshaya Poonepalle · Jianqiang Yang · Nabil F Saba · Yang Liu · Yong Teng
28 March 2026 at 18:00

Int J Mol Sci. 2026 Mar 20;27(6):2847. doi: 10.3390/ijms27062847.

ABSTRACT

Head and neck squamous cell carcinoma (HNSCC) is biologically and clinically dichotomous according to HPV status, a distinction that fundamentally dictates the design, implementation, and interpretation of liquid biopsy strategies. Conventional anatomical imaging lacks sufficient sensitivity for minimal residual disease (MRD) detection, contributing significantly to treatment failure and suboptimal clinical outcomes. This review provides a critical, evidence-based synthesis of the three principal circulating analytes, circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), and their evolving roles in real-time, non-invasive molecular monitoring. Critically, the clinical readiness of these analytes differs substantially: while ctDNA, particularly HPV-related ctDNA, is approaching clinical validation for MRD detection and recurrence surveillance in HPV-positive HNSCC, exosomes and CTCs remain investigational tools hindered by ongoing technical challenges including lack of standardized assays, limited reproducibility across platforms, and insufficient prospective validation. We review how the presence of a clonal, virally derived DNA target in HPV-positive HNSCC contrasts with the heterogeneous somatic mutational landscape of HPV-negative tumors, necessitating divergent analytical platforms and yielding distinct clinical utility profiles for MRD detection and recurrence surveillance. We further outline a pragmatic translational pathway focused on assay standardization, particularly for exosomes and CTCs where this foundational work is most urgently needed, integration of complementary multimodal liquid biopsy approaches, and rigorously designed prospective interventional clinical trials to establish clinical utility. Collectively, these efforts aim to transition HNSCC management from reactive, anatomy-based surveillance to proactive, molecularly guided precision oncology, with the potential to improve therapeutic decision-making and patient outcomes.

PMID:41898706 | PMC:PMC13027142 | DOI:10.3390/ijms27062847

Received — 26 March 2026 ⏭

CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liyuan Chen · Shilong Li · Jiangpeng Yan · Shuoling Liu · Qiang Yang · Xiu Li
25 March 2026 at 12:00
arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic decision-making agents in complex financial environments. However, the evolution of LLMs into autonomous financial agents faces a significant dilemma in evaluation paradigms. Direct live trading is irreproducible and prone to outcome bias by confounding luck with skill, whereas existing static benchmarks are often confined to entity-level stock picking and ignore broader market attention. To facilitate the rigorous analysis of these challenges, we introduce CN-Buzz2Portfolio, a reproducible benchmark grounded in the Chinese market that maps daily trending news to macro and sector asset allocation. Spanning a rolling horizon from 2024 to mid-2025, our dataset simulates a realistic public attention stream, requiring agents to distill investment logic from high-exposure narratives instead of pre-filtered entity news. We propose a Tri-Stage CPA Agent Workflow involving Compression, Perception, and Allocation to evaluate LLMs on broad asset classes such as Exchange Traded Funds (ETFs) rather than individual stocks, thereby reducing idiosyncratic volatility. Extensive experiments on nine LLMs reveal significant disparities in how models translate macro-level narratives into portfolio weights. This work provides new insights into the alignment between general reasoning and financial decision-making, and all data, codes, and experiments are released to promote sustainable financial agent research.
Received — 14 March 2026 ⏭

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

✇Cell Death Discovery nature.com science feeds
By: Shi Qiu · Dandan Xie · Sifan Guo · Zhibo Wang · Ying Cai · Xian Wang · Zhencai Hu · Shiwei Wang · Chunsheng Lin · Hong Yao · Qiang Yang · Yu Guan · Qiqi Zhao · Songqi Tang · Wenjie Sun · Yiqiang Xie · Aihua Zhang
11 March 2026 at 08:00

Cell Death Discovery, Published online: 11 March 2026; doi:10.1038/s41420-026-02990-7

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism
Received — 25 February 2026 ⏭

Federated Co-tuning Framework for Large and Small Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tao Fan · Yan Kang · Guoqiang Ma · Lixin Fan · Shuoling Liu · Kai Chen · Qiang Yang
24 February 2026 at 13:00
arXiv:2411.11707v2 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a gap persists in achieving simultaneous mutual enhancement between the server's LLM and the downstream clients' Small Language Models (SLMs). To address this, we propose FedCoLLM, a novel and parameter-efficient federated framework designed for co-tuning LLMs and SLMs. This approach is aimed at adaptively transferring server-side LLMs knowledge to clients' SLMs while simultaneously enriching the LLMs with domain insights from the clients. To accomplish this, FedCoLLM utilizes lightweight adapters in conjunction with SLMs, facilitating knowledge exchange between server and clients in a manner that respects data privacy while also minimizing computational and communication overhead. Our evaluation of FedCoLLM, utilizing various public LLMs and SLMs across a range of NLP text generation tasks, reveals that the performance of clients' SLMs experiences notable improvements with the assistance of the LLMs. Simultaneously, the LLMs enhanced via FedCoLLM achieves comparable performance to that obtained through direct fine-tuning on clients' data. Our code has been contributed to the FATE open-source project and is now publicly accessible at https://github.com/FederatedAI/FATE-LLM/tree/main/python/fate_llm/algo/fedcollm.
Received — 17 February 2026 ⏭

Model-based Large Language Model Customization as Service

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhaomin Wu · Jizhou Guo · Junyi Hou · Bingsheng He · Lixin Fan · Qiang Yang
17 February 2026 at 13:00
arXiv:2410.10481v5 Announce Type: replace-cross Abstract: Prominent Large Language Model (LLM) services from providers like OpenAI and Google excel at general tasks but often underperform on domain-specific applications. Current customization services for these LLMs typically require users to upload data for fine-tuning, posing significant privacy risks. While differentially private (DP) data synthesis presents a potential alternative, its application commonly results in low effectiveness due to the introduction of excessive noise on data for DP. To overcome this, we introduce Llamdex, a novel framework that facilitates LLM customization as a service, where the client uploads pre-trained domain-specific models rather than data. This client-uploaded model, optionally protected by DP with much lower noise, is inserted into the base LLM via connection modules. Significantly, these connecting modules are trained without requiring sensitive domain data, enabling clients to customize LLM services while preserving data privacy. Experiments demonstrate that Llamdex improves domain-specific accuracy by up to 26% over state-of-the-art private data synthesis methods under identical privacy constraints and, by obviating the need for users to provide domain context within queries, maintains inference efficiency comparable to the original LLM service.
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