The article surveys fake review detection research, focusing on how pre‑trained language models (PLMs) and large language models (LLMs) influence both the generation of deceptive reviews and their detection. It reviews 211 studies from 2018 to early 2026, categorizing methods by evidence source—such as review text, sentiment, rating behavior, temporal metadata, user‑product graphs, multimodal content, external knowledge, and LLM‑generated signals—and by fusion level. The survey traces the evolution from traditional machine learning to PLM‑based and LLM‑based approaches, evaluates performance on Amazon, Yelp, and OpSpam benchmarks, and highlights open challenges including adversarial generation, cross‑domain transfer, uncertainty‑aware fusion, robustness to missing sources, interpretability, and trustworthy evaluation of AI‑generated deceptive content.
By Fanji Yang (Guizhou University of Finance and Economics), Huiyao Chen (Harbin Institute of Technology), Xi Yu (Guizhou University of Finance and Economics), Meishan Zhang (Harbin Institute of Technology), Xiaohong Xiao (Guizhou University of Commerce), Mingsen Deng (Guizhou University of Finance and Economics)
The report examines how software architects view the growing use of AI development agents in their field. A focus group of 22 industry and academic participants discussed current practices, trust, validation, governance, and educational implications, concluding that decision‑making, accountability, and guardrail authoring remain human responsibilities. They introduced the concept of harness engineering—building systems that govern AI‑assisted creation—and identified criticality and cognitive debt as key factors for calibrating human oversight.
By Uwe van Heesch, Olaf Zimmermann, Christian Kohls
The paper presents a taxonomy-driven framework for identifying, categorizing, and explaining bias in AI-generated Python code. By extending an existing dataset and manually annotating bias categories and justifications, the authors evaluate both proprietary and open-source large language models (LLMs) for automated bias detection and explanation. Results show that models such as Gemini and Qwen3-coder achieve high classification accuracy and produce justification and code identification similarities that closely match human-authored reasoning.
By Manaal Basha, Aimee M. Ribeiro, Gema Rodriguez-Perez
The paper introduces a three‑step framework for deploying multi‑UAV networks in threat‑prone environments. First, a threat‑aware K‑means algorithm determines the minimal number of UAVs and safe initial positions. Second, an optimal matching stage assigns UAVs to these positions to reduce energy use. Third, a threat‑aware multi‑agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes UAV trajectories, power, and user associations, achieving zero safety violations and superior energy efficiency compared to other learning methods and heuristic baselines.
By Faisal Al-Kamali, Hussein A. Ammar, Francois Chan, James H. Bayes, Yasser Gadallah, Mohamed H. Ahmed
The paper introduces SAGE, a unified framework for RGB‑Thermal (RGB‑T) image alignment and fusion that integrates frequency equalization, hierarchical alignment, and subband fusion. SAGE uses invertible joint encoding and source‑specific low‑frequency modulation to generate structural and gain guidance, then performs hierarchical frequency collaborative alignment to estimate global affine geometry and refine high‑frequency details. Guided subband fusion aggregates aligned frequency coefficients under propagated guidance, coordinating complementary low‑ and high‑frequency information to reconstruct the fused image via inverse wavelet transform. Experiments on real‑world and synthetic misaligned RGB‑T datasets show that SAGE achieves competitive performance in both alignment and fusion, validating the effectiveness of source‑anchored guidance for weakly registered RGB‑T images.
By Timing Li, Yiming Sun, Boan Tao, Xiyuan Gao, Haifang Cao, Pengfei Zhu
The study investigates how Google’s Gemma 4‑e4b language model resolves conflicts between two documents. Using a counterbalanced design, researchers found that the semantic framing of a source (e.g., labeling it as an official guideline) dominates over the order in which documents appear. While the model shows a primacy bias toward the first document, this bias varies widely with wording and is amplified only when the documents are structurally identical.
By Amanda Fitch
SustainAI is a water‑aware, closed‑loop framework that embeds environmental accountability into AI deployment. It combines real‑time water metering, a hallucination‑aware penalty model, and a water‑aware routing algorithm that considers regional water stress. In tests with small language models, water footprints varied 11‑fold across data centers, and 1,335 inference runs consumed about 399 mL of water but yielded only 240 correct outputs, highlighting the resource cost of inaccurate responses.
By Farnaz Farid, Tashfia Towkee, Sania Nasreen, Sami bin Azad
The paper introduces LIRSeg, a method that replaces explicit Chain-of-Thought reasoning in multimodal large language models with a compact set of learnable latent tokens for reasoning segmentation. LIRSeg is trained in two stages—spatial alignment and GRPO—while employing extreme-advantage sampling, decoupled exploration-stability updates, and latent diversity amplification to enhance token informativeness. Experiments show that LIRSeg improves segmentation accuracy and reasoning efficiency, achieving significant gIoU gains over the VisionReasoner baseline and reducing reasoning tokens by about 16×.
By Tianhang Guo, Yulin He, Wei Chen, Wenjuan Zhou, Yuhang Li, Xinbiao Gan
The paper introduces iDriveVLA, a multi‑modal planning framework for autonomous driving that addresses a generation‑evaluation asymmetry by improving candidate trajectory spaces and providing a unified, safety‑aware evaluator. It combines a Safety‑aware Scorer for risk estimation with a VLM‑guided Modulator that adapts weighting to the scene, and employs an oracle‑aligned progressive training strategy. On the NAVSIM v1 leaderboard, iDriveVLA achieves a new state‑of‑the‑art PDMS score of 94.95, surpassing human‑expert performance.
By Zeyu He, Shiqi Liu, Ke Chen, Yun Yan, Jinzi Wu, Dianqiao Lei, Sirui Wang, ShuRui Peng, Tao Chen, Zhuo Huang, Yu Wu, Yadong Shao, Zhichao Li, Ke Sun, Yang Guan, Keqiang Li, Shengbo Eben Li
The paper introduces Persistent Negative Adversarial Distillation, a method that improves black-box on-policy distillation by maintaining a live pool of historical teacher–student comparisons to stabilize the discriminator’s negative distribution. By anchoring the discriminator with these persistent negatives, the approach reduces reward-estimation error and yields smoother, higher-performing student policies across multiple benchmarks. The study demonstrates that the choice of negative samples is a critical design factor in effective black-box distillation.
By Haixu Ma, Saad Lahrichi, Weiwei Li, Kevin Han, Weiqiang Wu, Peggy Yang, Dongzhuo Li, Ruiyi Li, Serena Li, Gedi Zhou, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Lizhu Zhang
FARE (Forensic Acceptance Region Estimation) is a method designed to audit the integrity of AI image generators at deployment time. By training on images from a certified generator, FARE learns generator‑specific forensic features and tightens its acceptance region using hard samples, enabling it to determine whether a new image originates from the enrolled generator. Experiments show that FARE consistently outperforms existing baselines in detecting generator swaps, even when the swapped model is similar or under exact‑model and decision‑only attacks.
By Kai Yao, Marc Juarez
The paper investigates whether pixels alone can determine an image’s origin—human, AI class, or specific generator—under adversarial edits. It establishes a minimax limit: the best possible robust acceptance gap equals the minimum total‑variation distance between the target distribution and attacked source distributions, independent of verifier design. The study also shows that practical public verifiers can fail before reaching this theoretical ceiling, highlighting the need to evaluate both statistical limits and deployed verifier behavior separately.
By Kai Yao
G$^2$PTQ is a post‑training quantization framework that improves large language models by combining first‑ and second‑order information in a globally supervised, block‑wise optimization. It refreshes gradient and Hessian estimates before each Transformer block and uses a trust‑region scaling mechanism to stabilize gradient steps, preventing exploding weight updates. The method achieves better alignment with full‑precision models and outperforms state‑of‑the‑art baselines across various model families and bit‑widths.
By Ruikang Liu, Haoli Bai, Yuxuan Sun, Qian Zhang, Wenzheng Cai, Yanqi Hao, Feiyu Wang, Weidong Zhong, Zhuang Wang, Tong Yang, Xiangsheng Zhou
BAT-CLIP is a trimodal alignment framework that jointly aligns intracranial EEG (iEEG) neural embeddings to both pretrained audio and text anchors within a shared, frozen audio‑text manifold. Unlike existing CLIP‑style brain‑speech models that anchor neural activity to a single modality, BAT‑CLIP leverages both audio and text to preserve temporal structure and linguistic separability. On the naturalistic Podcast benchmark, BAT‑CLIP produces more robust representations than bimodal CLIP baselines and demonstrates the value of self‑supervised foundation models for CLIP training.
By Suhyun Kim, Jinmo Han, Danny Dongyeop Han, Ahhyun Lucy Lee, Jewoon Lee, Yonghyeon Gwon, Zach Paris, Chun Kee Chung, Saewoong Bahk, Nam Soo Kim, Seong Jae Hwang, Jiook Cha
UniAR is a unified framework that improves autism spectrum disorder (ASD) recognition by using multi-granularity prompt learning and a large multimodal model to generate diagnostic descriptions at word, phrase, and sentence levels. It aligns these semantic representations with visual evidence through a Mixture-of-Experts-based Multi-Scale Alignment Module, enabling robust ASD detection across heterogeneous data types. Experiments on four brain MRI and facial expression benchmarks show that UniAR outperforms state‑of‑the‑art methods, achieving 75.9% accuracy on MRI and 91.6% on facial benchmarks, with gains of 1.5 and 1.2 percentage points respectively.
By Lei Xin, Zeheng Wang, Jiayin Zhu, Shihong Huang, Fanhu Zeng, Changjiang Jiang, Dengbo He, Yutao Yue, Zhenglun Kong
AgentXploit is a two‑role auditing system that separates repository‑level attack‑path discovery from runtime exploitation for AI agents. The Analyzer Agent traces attacker‑controlled inputs to sensitive operations and records candidate attack paths, while the Exploiter Agent turns these paths into concrete attacks and refines them using runtime feedback. The system is evaluated on AgentXploit‑Bench, a benchmark of 72 reproducible vulnerabilities across 12 open‑source AI‑agent systems, achieving 59.3% end‑to‑end success compared to 38.4% for Codex, and 79.2% attack success on AgentDojo versus 52.7% for AgentVigil.
By Weida Liang, Shi Qiu, Zhun Wang, Simon Sure, Xiaoyuan Liu, Tianneng Shi, Zhaorun Chen, Wenbo Guo, Dawn Song
CG-HAF is a global‑local fusion framework for ordinal acne severity grading that explicitly combines holistic facial severity probabilities with structured lesion‑burden descriptors such as lesion count, detection confidence, and lesion area. The model uses a lightweight, interpretable classifier to produce the final grade, achieving statistically significant improvements over global‑evidence‑only baselines, especially for severe cases. Cross‑dataset testing reveals that strong performance within a dataset does not automatically transfer, largely due to mismatched grading criteria rather than detection failures.
By Muhammad Muhtasim Shahriar, Md. Naimur Asif Borno, Saad Aloteibi, Mohammad Ali Moni
The paper introduces a safety‑bounded gateway that translates IEEE 11073 Service‑Oriented Device Connectivity (SDC) into the Model Context Protocol (MCP) for medical AI agents. It exposes device metrics, alarms, context references, and semantic metadata as read‑only resources, while representing selected action affordances as policy‑validated dry‑run tools, ensuring that agent requests never trigger actual device operations. A Python prototype demonstrates fault and lifecycle experiments, deterministic baselines, and multi‑model agent evaluation, showing improved semantic conformity and preservation of the no‑execution boundary.
By Bennet Gerlach, Stefan Fischer
The paper introduces the Active Provenance Gate (APG), a post‑debate verification layer for multi‑agent debate synthesis that audits debate logs, applies self‑correction, and blocks unsupported claims before publication. Empirical studies show that APG more than doubles provenance fidelity in crisis simulations and that users prefer explicit failure reports over fabricated consensus. The work shifts data origin tracing from passive logging to active conditional blocking, addressing safety gaps in large‑language‑model‑based debate systems.
By Jakub Mas{\l}owski, Jaros{\l}aw A. Chudziak
The paper introduces the first federated learning framework for infant movement analysis, specifically targeting General Movement Assessment using skeletal motion data. It employs Monte Carlo Dropout to estimate predictive uncertainty and proposes an Uncertainty-Aware Federated Averaging (UA‑FedAvg) strategy that weights client updates by this uncertainty. Experiments with three clients show that federated learning outperforms local models and approaches centralized training performance, with UA‑FedAvg generally surpassing standard FedAvg.
By Edmond S. L. Ho