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Received — 10 September 2026 ⏭

MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shih-Yu Lai · Wen-Fan Wang · Sai Ling · Shaune Jan · Bing-Yu Chen · Xiang Anthony Chen
10 September 2026 at 12:00
arXiv:2609.10385v1 Announce Type: cross Abstract: Animation and VFX pre-production review requires teams to translate loosely specified creative intent--briefs, evolving specifications, heterogeneous references, and verbal decisions--into revisions that junior artists can execute without repeated clarification. In practice, criteria drift across iterations, review judgments lose their evidential basis, and the reasoning behind a request rarely survives the senior-junior handoff. We contribute a design framework for intent-evidence-action alignment: intent is articulated into a shared project record, judgments are anchored to grounded evidence, and authorized decisions are converted into clear revision tasks tied directly to reference notes. We instantiate this framework in MOONWALK, a professional pre-production review system comprising a shared intent record, reference/specification anchoring, structured work-in-progress comparison, and supervisor-authorized action planning. In this workflow, AI handles administrative coordination--flagging missing context and organizing notes--while artists retain full creative direction. An in-studio study with professional practitioners compares MOONWALK with a chat-only (chatbot) interface using matched production materials, while participants' existing workflows provide a retrospective ecological baseline. Results indicate stronger intent alignment, decision traceability, and checklist executability, while also showing that aesthetic authority and final prioritization must remain with practitioners. The evaluation establishes the value of the integrated structured workflow over unstructured conversational AI chatbot. Code: https://github.com/Akinesia112/Moonwalk/tree/english-version

Integrated transcriptomic and immunogenomic analysis unravels the immunological functions and prognostic landscape of WD repeat domain 76

✇Omics in Hepatocellular
By: Yifan Wang · Rong Zhou · Shenglong Guo · Shaoting Nan · Yanlong Duan · Lei Cao · Xiujuan Huang · Hailong Yan
3 September 2026 at 18:00

Int J Immunopathol Pharmacol. 2026 Jan-Dec;40:3946320261486727. doi: 10.1177/03946320261486727. Epub 2026 Sep 3.

ABSTRACT

BackgroundWD Repeat Domain 76 (WDR76) plays a potential role in cellular regulation; however, its comprehensive landscape across human malignancies and its specific biological function in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsWe conducted a systematic pan-cancer analysis utilizing multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) atabases to evaluate WDR76 expression, subcellular localization, and its correlation with clinicopathologic features, genomic instability, and immune infiltration. Diagnostic and prognostic values were assessed via Receiver operating characteristic (ROC) and Kaplan-Meier analyses. Furthermore, the functional role of WDR76 in HCC was validated in vitro using Hep-3B and Huh7 cell lines through siRNA-mediated knockdown, followed by CCK-8, wound-healing, and transwell assays.ResultsWDR76 was significantly upregulated in the majority of tumor types, including LIHC, LUAD, and COAD, while exhibiting nuclear localization. Elevated WDR76 expression correlated with advanced tumor staging, metastasis, and poor clinical outcomes across multiple cohorts, particularly in ACC, KIRP, and LIHC. ROC analysis highlighted its exceptional diagnostic precision in cancers such as GBM and LIHC. Immunologically, WDR76 expression was intricately linked to immune cell infiltration, immune checkpoint markers, and genomic instability parameters, suggesting a role in shaping the tumor microenvironment. Drug sensitivity profiling revealed that high WDR76 levels correlate with resistance to specific chemotherapeutic agents. Experimentally, silencing WDR76 in HCC cells significantly suppressed cell proliferation, migration, and invasion capabilities.ConclusionOur study establishes WDR76 as a robust pan-cancer prognostic biomarker and a potential immunotherapeutic target. Specifically, we provide experimental evidence that WDR76 functions as an oncogenic driver in liver cancer, promoting malignant phenotypes and offering a novel avenue for targeted therapeutic intervention.

PMID:42690047 | PMC:PMC13542525 | DOI:10.1177/03946320261486727

MUC13 promotes cisplatin resistance in intrahepatic cholangiocarcinoma through regulation by histone H3K18 lactylation

✇Cell Death Discovery nature.com science feeds
By: Zhijie Yin · Qingfu Lang · Jia Sun · Yinfan Wang · Chen Jia · Boxin Chen · Zhen Cui · Ze Wang · Wenjun Chen · Zejun Luo · Ming Yang · Tiemin Pei
1 September 2026 at 08:00

Cell Death Discovery, Published online: 01 September 2026; doi:10.1038/s41420-026-03324-3

MUC13 promotes cisplatin resistance in intrahepatic cholangiocarcinoma through regulation by histone H3K18 lactylation
Received — 4 April 2026 ⏭

FourierMoE: Fourier Mixture-of-Experts Adaptation of Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Juyong Jiang · Fan Wang · Hong Qi · Sunghun Kim · Jing Tang
3 April 2026 at 12:00
arXiv:2604.01762v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a crucial paradigm for adapting large language models (LLMs) under constrained computational budgets. However, standard PEFT methods often struggle in multi-task fine-tuning settings, where diverse optimization objectives induce task interference and limited parameter budgets lead to representational deficiency. While recent approaches incorporate mixture-of-experts (MoE) to alleviate these issues, they predominantly operate in the spatial domain, which may introduce structural redundancy and parameter overhead. To overcome these limitations, we reformulate adaptation in the spectral domain. Our spectral analysis reveals that different tasks exhibit distinct frequency energy distributions, and that LLM layers display heterogeneous frequency sensitivities. Motivated by these insights, we propose FourierMoE, which integrates the MoE architecture with the inverse discrete Fourier transform (IDFT) for frequency-aware adaptation. Specifically, FourierMoE employs a frequency-adaptive router to dispatch tokens to experts specialized in distinct frequency bands. Each expert learns a set of conjugate-symmetric complex coefficients, preserving complete phase and amplitude information while theoretically guaranteeing lossless IDFT reconstruction into real-valued spatial weights. Extensive evaluations across 28 benchmarks, multiple model architectures, and scales demonstrate that FourierMoE consistently outperforms competitive baselines in both single-task and multi-task settings while using significantly fewer trainable parameters. These results highlight the promise of spectral-domain expert adaptation as an effective and parameter-efficient paradigm for LLM fine-tuning.

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhang Wu · Xiangqing Shen · Fanfan Wang · Cangqi Zhou · Zhen Wu · Xinyu Dai · Rui Xia
3 April 2026 at 12:00
arXiv:2604.02091v1 Announce Type: cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated relevance labels in isolation, decoupled from the downstream generation process. This isolation leads to a fundamental misalignment: documents identified as topically relevant by information retrieval metrics often fail to provide the actual utility required by the LLM for precise answer generation. To bridge this gap, we introduce ReRanking Preference Optimization (RRPO), a reinforcement learning framework that directly aligns reranking with the LLM's generation quality. By formulating reranking as a sequential decision-making process, RRPO optimizes for context utility using LLM feedback, thereby eliminating the need for expensive human annotations. To ensure training stability, we further introduce a reference-anchored deterministic baseline. Extensive experiments on knowledge-intensive benchmarks demonstrate that RRPO significantly outperforms strong baselines, including the powerful list-wise reranker RankZephyr. Further analysis highlights the versatility of our framework: it generalizes seamlessly to diverse readers (e.g., GPT-4o), integrates orthogonally with query expansion modules like Query2Doc, and remains robust even when trained with noisy supervisors.
Received — 26 March 2026 ⏭

Tuning the sensitivity of mechanosensory receptors through histidine scanning

✇Cell
By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao
18 February 2026 at 08:00
Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.
Received — 11 March 2026 ⏭

DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingxi Zou · Jiaxiang Chen · Junfan Li · Langzhang Liang · Qifan Wang · Xu Yinghui · Zenglin Xu
10 March 2026 at 12:00
arXiv:2603.08145v1 Announce Type: cross Abstract: Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous human preferences. In practice, systematic annotator and user-group disagreement makes mean-reward maximization brittle and susceptible to proxy over-optimization. We propose **Disagreement-Aware Alignment via Risk-Constrained Decoding (DARC)**, a retraining-free inference-time method that frames response selection as distributionally robust, risk-sensitive decision making. Given multiple preference samples or scalable disagreement proxies, DARC reranks candidates by maximizing a *KL-robust (entropic)* satisfaction objective, and provides simple deployment controls that cap or penalize the corresponding entropic risk premium relative to the mean, enabling explicit risk budgets without retraining. We provide theoretical characterization linking this decoding rule to principled pessimism and KL-based distributionally robust optimization. Experiments on alignment benchmarks show that DARC reduces disagreement and tail risk while maintaining competitive average quality under noisy, heterogeneous feedback.
Received — 5 March 2026 ⏭

xLLM Technical Report

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tongxuan Liu · Tao Peng · Peijun Yang · Xiaoyang Zhao · Xiusheng Lu · Weizhe Huang · Zirui Liu · Xiaoyu Chen · Zhiwei Liang · Jun Xiong · Donghe Jin · Minchao Zhang · Jinrong Guo · Yingxu Deng · Xu Zhang · Xianzhe Dong · Siqi Wang · Siyu Wu · Yu Wu · Zihan Tang · Yuting Zeng · Yanshu Wang · Jinguang Liu · Meng Kang · Menxin Li · Yunlong Wang · Yiming Liu · Xiaolong Ma · Yifan Wang · Yichen Zhang · Jinrun Yin · Keyang Zheng · Jiawei Yin · Jun Zhang · Ziyue Wang · Xiaobo Lin · Liangyu Liu · Liwei Lan · Yang Liu · Chunhua Peng · Han Liu · Songcheng Ren · Xuezhu Wang · Yunheng Shen · Yi Wang · Guyue Liu · Yitao Hu · Hui Chen · Tong Yang · Hailong Yang · Jing Li · Guiguang Ding · Ke Zhang
4 March 2026 at 13:00
arXiv:2510.14686v2 Announce Type: replace-cross Abstract: We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intelligent scheduling module that efficiently processes multimodal requests and co-locates online and offline tasks through unified elastic scheduling to maximize cluster utilization. This module also relies on a workload-adaptive dynamic Prefill-Decode (PD) disaggregation policy and a novel Encode-Prefill-Decode (EPD) disaggregation policy designed for multimodal inputs. Furthermore, it incorporates a distributed architecture to provide global KV Cache management and robust fault-tolerant capabilities for high availability. At the engine layer, xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources. This is achieved through comprehensive multi-layer execution pipeline optimizations, an adaptive graph mode and an xTensor memory management. xLLM-Engine also further integrates algorithmic enhancements such as optimized speculative decoding and dynamic EPLB, collectively serving to substantially boost throughput and inference efficiency. Extensive evaluations demonstrate that xLLM delivers significantly superior performance and resource efficiency. Under identical TPOT constraints, xLLM achieves throughput up to 1.7x that of MindIE and 2.2x that of vLLM-Ascend with Qwen-series models, while maintaining an average throughput of 1.7x that of MindIE with Deepseek-series models. xLLM framework is publicly available at https://github.com/jd-opensource/xllm and https://github.com/jd-opensource/xllm-service.
Received — 25 February 2026 ⏭

IR$^3$: Contrastive Inverse Reinforcement Learning for Interpretable Detection and Mitigation of Reward Hacking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mohammad Beigi · Ming Jin · Junshan Zhang · Jiaxin Zhang · Qifan Wang · Lifu Huang
24 February 2026 at 13:00
arXiv:2602.19416v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) enables powerful LLM alignment but can introduce reward hacking - models exploit spurious correlations in proxy rewards without genuine alignment. Compounding this, the objectives internalized during RLHF remain opaque, making hacking behaviors difficult to detect or correct. We introduce IR3 (Interpretable Reward Reconstruction and Rectification), a framework that reverse-engineers, interprets, and surgically repairs the implicit objectives driving RLHF-tuned models. We propose Contrastive Inverse Reinforcement Learning (C-IRL), which reconstructs the implicit reward function by contrasting paired responses from post-alignment and baseline policies to explain behavioral shifts during RLHF. We then decompose the reconstructed reward via sparse autoencoders into interpretable features, enabling identification of hacking signatures through contribution analysis. Finally, we propose mitigation strategies - clean reward optimization, adversarial shaping, constrained optimization, and feature-guided distillation - that target problematic features while preserving beneficial alignment. Experiments across multiple reward model configurations show that IR3 achieves 0.89 correlation with ground-truth rewards, identifies hacking features with over 90% precision, and significantly reduces hacking behaviors while maintaining capabilities within 3% of the original model.
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