EpiPersona is a framework that explicitly couples persona and episode information to improve pluralistic preference modeling for large language models. It projects noisy preference feedback into a low‑dimensional persona space, aggregates similar personas into discrete codewords, and then couples the inferred persona representation with the current episode for episode‑aware preference prediction. Experiments demonstrate that EpiPersona outperforms baselines, especially in hard episodic‑shift scenarios and with sparse preference data.
By Yujie Zhang, Weikang Yuan, Zhuoren Jiang, Pengwei Yan
The paper investigates whether state‑of‑the‑art multi‑modal large language models (LLMs) can detect deepfake images. Twelve recent LLMs were benchmarked against traditional detectors on multiple datasets, including real‑world deepfakes, and prompt tuning was used to improve performance. Results show that the best LLMs can match or exceed conventional methods on out‑of‑distribution data, though many LLMs perform poorly; larger model size helps, but newer versions and reasoning abilities do not necessarily improve detection.
By Simiao Ren (Dennis), Yao Yao (Dennis), Kidus Zewde (Dennis), Zisheng Liang (Dennis), Tsang (Dennis), Ng (Dennis), Ning-Yau Cheng, Xiaoou Zhan, Qinzhe Liu, Yifei Chen, Hengwei Xu
The paper introduces ActiveKD, a framework that combines active learning with knowledge distillation by using large vision‑language models as teachers. It leverages the structured prediction bias of these models to design Probabilistic CoreSet (PCoreSet), a selection strategy that maximizes coverage in probability space rather than feature space. Experiments on 11 datasets show that ActiveKD improves all selection methods over a no‑distillation baseline, and PCoreSet consistently outperforms random sampling across multiple students and teachers.
By Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Dongseop Kim, Sung Ju Hwang
The paper introduces ParallaxRAG, a multi-view retrieval-augmented generation framework that addresses multi-hop reasoning challenges in large language models over knowledge graphs. It identifies that transformer attention heads specialize in distinct semantic relations across reasoning stages, forming a hop-aligned relay pattern, and proposes decoupling queries and KGs into head-specific semantic spaces to enforce relational diversity. Experiments on WebQSP, CWQ, and BioASQ show state‑of‑the‑art performance, reduced hallucination, and strong generalization.
By Jinliang Liu, Jiale Bai, Shaoning Zeng
The paper investigates how different types of personally identifiable information (PII) are learned and leaked by large language models for code (LLM4Code). By creating a diverse PII dataset, fine‑tuning models of various scales, and analyzing training dynamics through a structural causal model, the authors find that leakage risk varies by PII type—easy‑to‑learn data such as IP addresses leak more often, while harder types like keys and passwords leak less. The study provides causal evidence that leakage risks are type‑dependent and suggests that defenses should be tailored to specific PII categories and their learnability.
By Hua Yang, Alejandro Velasco, Sen Fang, Bowen Xu, Denys Poshyvanyk
DeALOG is a decentralized multi‑agent framework designed for multimodal question answering. It employs specialized agents—Table, Context, Visual, Summarizing, and Verification—that communicate via a shared natural‑language log, serving as persistent memory. This log‑based coordination enables collaborative error detection and verification without central control, leading to competitive performance on several benchmark datasets.
By Abhijit Chakraborty, Ashish Raj Shekhar, Shiven Agarwal, Vivek Gupta
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
By Simiao Ren (Neo), Yuchen Zhou (Neo), Xingyu Shen (Neo), Kidus Zewde (Neo), Tommy Duong (Neo), George Huang (Neo), Hatsanai (Neo), Tiangratanakul (Dennis), Tsang (Dennis), Ng (Dennis), En Wei, Jiayu Xue
The paper proposes a Multi-Agentic AI framework to enable fairness‑aware and accelerated inference of large, heterogeneous multi‑modal language models on mobile edge networks. It introduces a Global Planning Agent, a Prompt Scheduling Agent, and multiple on‑node LM Deployment Agents that jointly optimize prompt routing and model deployment using natural language reasoning over telemetry and historical data. Experiments on a city‑wide testbed show a 42.9% reduction in end‑to‑end latency versus a state‑of‑the‑art baseline and an 80.6% reduction versus a full‑activation baseline, while preserving high fairness (Jain's index 0.925).
By Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou
EntailLLM is a framework that verifies vulnerability discovery paths generated by large language model agents using a separate knowledge graph and logic programming. The system checks whether the agent’s traversal of a function call graph is entailed by domain knowledge, improving pooled entailment from 77% to 98% across various binaries and CWE classes. It is deployed on real medical-device binaries, achieving high entailment without per-device tuning and offering logical, explainable verification grounded in formal semantics.
By Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Colton Payne, Paulo Shakarian, Dana Warmsley, Nigel Stepp, Evelyn Kim
arXiv:2610.32235v1 Announce Type: new
Abstract: Small language models deployed under cost, latency, or privacy constraints cannot simply switch to larger models when reasoning fails. Multi-candidate...
By Qianli Ma, Zhiqing Tang, Fanshuai Meng, Zhi Yao, Hanshuai Cui, Weijia Jia
arXiv:2610.32462v1 Announce Type: new
Abstract: Graph structure learning (GSL) adapts propagation graphs to prediction tasks, but the relative predictive value of feature-derived priors and learned r...
By Yuxia Sun, Yutszyuk Wong, Huihong Chen, Longhao Lv, Chang-Dong Wang, Li Zeng
arXiv:2610.32748v1 Announce Type: new
Abstract: The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of hybrid deep generative models (hybrid-DG...
By Yubo Ye, Maryam Toloubidokhti, Sumeet Vadhavkar, Xiajun Jiang, Huafeng Liu, Linwei Wang
arXiv:2610.31920v1 Announce Type: cross
Abstract: In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements....
By Muzaffar Qureshi, Tochukwu E. Ogri, Trivikram Satharasi, Kyle Volle, Rushikesh Kamalapurkar
arXiv:2610.31995v1 Announce Type: cross
Abstract: As large language models (LLMs) face growing demands for data removal driven by regulatory compliance, copyright concerns, and privacy protection, ap...
By Mengying Zhang, Derui Wang, Ibrahim Khalil, Bowen Liu, Xiaoyu Xia, Minhui Xue
arXiv:2610.32051v1 Announce Type: cross
Abstract: We study the dynamic allocation of scarce medications in Liberia's community health program. A central warehouse supplies a health facility and the C...
By Baris Ata, Yufan Chen, Robert Montgomery
arXiv:2610.32888v1 Announce Type: cross
Abstract: Vision-language-action (VLA) policies can adapt to local human intervention, while federated learning can combine distributed robot experience withou...
By Vigneshwaran Subbaraju, Obaidullah Zaland, Monowar Bhuyan
arXiv:2610.03983v2 Announce Type: replace
Abstract: We study constrained online convex optimization with adversarial convex costs and constraints ($\mathsf{COCO}$). At each round $t\in[T]$, a learner...
By Dhruv Sarkar, Abhishek Sinha
arXiv:2610.33279v1 Announce Type: new
Abstract: Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed...
By Daisy Rae Lopez, Angela Boccia, Maurizio Sessa
arXiv:2610.33495v1 Announce Type: new
Abstract: Combining language models is usually done by averaging weights, which works only when the models share a common starting point. We ask whether five fro...
By Oladimeji Anthonio, Dimeji Olawuyi, Oloruntoba Ajayi, Temilola Aderemi
arXiv:2610.33586v1 Announce Type: new
Abstract: Depression severity estimation is crucial for mental health assessment and intervention, yet traditional approaches depend on clinical interviews or se...
By Nada Xu, Xueqin Chen, Qiang Gao, Hongru Liang, Wenqiang Lei