The paper introduces Ensemble Feedback Controllable Synthesis (EFCS), a framework that generates diverse, challenging synthetic face images while preserving visual realism. EFCS improves distributional variability, achieving higher FID and KID scores than single‑feedback or random synthesis, yet maintains high precision. Models trained on EFCS data outperform baselines on multiple benchmarks, and the authors provide an analytical formulation linking perturbation difficulty, sample utility, and performance degradation to guide synthetic data complexity.
By Saransh Suri, Shivang Agarwal, Mayank Vatsa, Richa Singh
The paper introduces Heterogeneous-Modal Unsupervised Domain Adaptation (HMUDA), a setting where a labeled source domain and an unlabeled target domain have entirely different modalities, bridged by an unlabeled domain containing paired observations from both modalities. It proposes Latent Space Bridging (LSB), a dual‑branch framework that uses a feature consistency loss on paired bridge samples to close the modality gap and a class‑centroid alignment loss to reduce source‑target discrepancy. Experiments on eight benchmark settings, covering both 2D‑to‑3D and 3D‑to‑2D transfers, show that LSB achieves state‑of‑the‑art performance.
By Jiawen Yang, Shuhao Chen, Shengtao Zhang, Ke Tang, Yu Zhang
The paper proposes an adaptive multi‑discriminator Wasserstein GAN (MD‑WGAN) framework for Internet of Vehicles (IoV) that combines reinforcement learning and game‑theoretic coordination to manage resource allocation for distributed training. Roadside units host generators with Deep Q‑Network agents that select discriminator subsets and coordinate training epochs, while a unified objective links adversarial quality to resource efficiency, communication overhead, and latency. Experiments on real‑world NGSIM trajectory data show comparable prediction accuracy to state‑of‑the‑art GANs, with a 28% reduction in CPU usage and 6% reduction in memory usage, while maintaining competitive communication overhead and latency.
By Farhoud Jafari Kaleibar, Amr M. Zaki, Marin Litoiu
The paper introduces MARS-Gov, a standpoint-aware multi-agent system designed to detect and mitigate bias in Dutch government documents. It combines legal evidence retrieval, open-set target screening, specialized jurors, conservative routing, and rewrite verification, and adds a dynamic "10th juror" when new bias groups are identified. On the DGDB benchmark, MARS-Gov achieves state‑of‑the‑art performance with an F1 score of 0.880, surpassing zero‑shot LLM detectors and supervised Dutch encoders while reducing unnecessary interventions to 2.5%.
By Yuchen Miao, Zijun Wang, Chang Han, Yurui Shi, Mingtai Zhang, Siyang Xu
The paper presents a reference‑free framework for detecting hallucinations in Sinhala‑to‑English neural machine translation. It introduces a 45,000‑sample synthetic dataset created via five linguistically motivated corruption strategies and a semantic rescue mechanism that uses character‑level similarity. Using a fine‑tuned mDeBERTa‑v3 model, the authors achieve a token‑level F1 of 0.841 and demonstrate that the detector relies on the Sinhala source, not on surface artefacts, with an AUROC of 0.970 on a source‑disjoint test set. The study also benchmarks eight NMT systems, showing that hallucination rates vary by an order of magnitude across architectures.
By Navam Obeysekara, Nevidu Jayatilleke
The paper proposes augmenting the global [CLS] token and register tokens of vision transformers with spatial region tokens to better capture fine‑grained image details. By matching each semantic token with a corresponding image patch, the method automatically generates localized ROI tokens without external annotations. These tokens are then used in a multi‑vector retrieval framework inspired by ColBERT, improving fine‑grained image retrieval while keeping storage costs manageable.
By Ian de Holanda Cavalcanti Bezerra, Vivek Trivedy, Lucas Pascotti Valem, Longin Jan Latecki
The paper introduces EDMD-kDL, a kernel-based method that learns finite-dimensional Koopman embeddings directly from data using bilevel optimization and collocation techniques. It combines kernel dictionary learning with extended dynamic mode decomposition to produce Koopman approximations without pre-specifying a dictionary. Experiments on sea-surface-temperature forecasting and video data show that EDMD‑kDL matches or surpasses ANN-based approaches while remaining scalable to large datasets.
By Joel-Pascal Ntwali N'konzi, Feliks N\"{u}ske, Stefan Klus
SpikeSSL is a universal spike inference framework that uses bidirectional IIR state‑space layers inspired by calcium dynamics as its temporal backbone. It incorporates a multi‑modal conditioning encoder to adapt to different calcium indicators, sampling rates, and trace statistics, and provides per‑frame uncertainty estimates. On a benchmark of 33 public datasets, SpikeSSL achieves state‑of‑the‑art performance both in‑domain and in zero‑shot leave‑one‑indicator‑out settings, and a biophysical simulation pipeline further improves cross‑indicator generalization.
By Chenghao Yue, Siming Xing, Shuran Liu, Angran Li, Yuanlong Zhang
The paper introduces DPVar, a fairness metric that measures the variance of the conditional mean prediction to directly optimize mean demographic parity in nonlinear regression. It presents two solvers—FBO, using a closed‑form hypergradient, and ITD, which differentiates through the conditional‑mean estimator updates—to handle the resulting functional bilevel problem. Experiments on 21 tabular datasets and the Communities & Crime data show that these methods achieve competitive or superior accuracy‑fairness trade‑offs compared to existing approaches.
By Ieva Petrulionyte, Julien Mairal, Michael Arbel
The paper presents Conversational Voice Aesthetic Model (CVAM), a speech large language model that describes voice aesthetics in conversational contexts. CVAM predicts nine categorical attributes such as gender, pitch, pacing, emotion, and delivery, and is trained on human-annotated data from the CANDOR corpus. Experiments show that CVAM aligns better with human listeners than Gemini 3.1 Pro and other open‑source speech LLMs, demonstrating the value of grounding voice aesthetics in human perception.
By Xilin Jiang, Shun Zhang, Tejas Jayashankar, Yinghao Aaron Li, Osama Hanna
FAITH is a model‑free reinforcement learning framework that separates safety from task performance by using a learned safety filter. The filter approximates the optimal state‑action safety value with a feedforward network, allowing the task policy to optimize return through filtered dynamics without a competing safety term. Experiments on a double integrator, Safety Gym, a 29‑DoF humanoid, and a real Unitree G1 show that FAITH achieves high returns while maintaining near‑perfect safety and handling infeasible starts gracefully.
By Songyuan Zhang, Baljeet Singh, Sarthak Ranjeet Kaingade, Chuchu Fan, Bryan Trinh
The paper introduces BFM-MSDA, a brain foundation model-guided multi-source domain adaptation framework for cross-subject motor‑imagery EEG decoding. It first uses pretrained brain foundation model representations to estimate source–target compatibility and select relevant source subjects. A relevance-weighted dual alignment strategy then aligns marginal and conditional feature distributions, achieving higher accuracies on two public MI‑EEG benchmarks and demonstrating the benefits of informed source selection.
By Jinzhou Wu, Baoping Tang, Qikang Li, Yi Wang, Cheng Li, Shujian Yu
The study investigates how subtle changes in the presentation of a structured judging request can influence the behavior of an answer‑grading model. By adding a single colon to a candidate answer, the researchers found that the model’s false‑acceptance rate rose dramatically—from 1–3% to over 26%—across two benchmark datasets (DROP and GSM8K) and different grading configurations. The effect varied with the presentation format, showing that logically equivalent requests can lead to markedly different vulnerabilities.
By Hongliang Liu
The paper introduces Relative Patch Response Learning (PRL) to detect AI‑generated images by comparing patch responses across two mixed views of aligned real‑generated image pairs. PRL uses a relative response objective, a reference coherence objective, and an area ranking objective to address contextual shifts caused by self‑attention, providing precise supervision for patch‑level detection. Experiments show PRL outperforms prior methods by 4.3% and 5.9% in average balanced accuracy on standard and in‑the‑wild benchmarks.
By Tianyu Wang, Ouxiang Li, Yanbin Hao, Zhenhua Tang, Shuo Wang
OPERA is a framework that improves single‑view 3D reconstruction by guiding multi‑view diffusion sampling with pretrained perception models through lightweight alignment modules. The modules are trained independently while keeping the generative and perception models frozen, enabling multiple signals to be combined at inference without joint fusion training. Experiments on Google Scanned Objects and OmniObject3D show that OPERA reduces mean Chamfer Distance by 30.4% and 24.8% on the primary baseline compared to unguided reconstruction.
By Y Huynh, Duc Thanh Nguyen, Mohamed Abdelrazek
arXiv:2610. 12153v1 Announce Type: new Abstract: We study episodic constrained Markov decision processes with adversarial losses under stochastic hard constraints.
By Qian Zuo, Francesco Emanuele Stradi
The study examined 106 depressed adolescents, 67 with non‑suicidal self‑injury (NSSI) and 39 without, using EEG microstate analysis during social pain, physical pain, and rest. A deep sequence model distinguished NSSI participants from controls with 68.55% accuracy, revealing weakened transitions between microstates linked to emotion/interoception (MS3) and action preparation (MS5) during social pain. Time‑resolved analyses showed altered recruitment patterns of these microstates in NSSI adolescents, indicating disrupted emotion‑action coupling.
By Ying Xu, Xiaojun Liang, Li Zhang, Yixuan Yuan, Gan Huang, Yongjie Zhou, Zhen Liang
The paper introduces two inference‑time designs—factor‑compiled admission (FC) and permission‑semantic admission (PS)—to govern persistent memory in large language model agents without retraining. FC evaluates entire memory entries, while PS decomposes entries into typed units, both using deterministic policies to decide what recalled information enters the working context and how it is expressed. Experiments on a development suite and an external benchmark show that FC and PS reduce failure rates and cross‑domain leakage compared to verbatim injection, though they increase personalization failures and do not meet all preregistered criteria.
By Chang Liu, Deliang Ding
The paper introduces grasp, a neurosymbolic framework that learns probabilistic logic programs by combining functional gradient boosting with large language models (LLMs). In grasp, the weak learner is a first‑order rule and the combinatorial search for new rules is delegated to an LLM proposal oracle, allowing systematic inductive reasoning. Experiments on four relational benchmarks—including molecular toxicity prediction, mutagenesis, and citation matching—show that grasp outperforms purely symbolic, neural, and LLM‑based baselines while producing interpretable weighted rule ensembles.
By Saurabh Mathur, Sahil Sidheekh, Bhavan Vasu, Farbod Tavakkoli, Prasad Tadepalli, Kristian Kersting, Sriraam Natarajan
The paper introduces SED-MCTS, a Monte Carlo tree search method that enhances PDE solution discovery by distilling structural experience from evaluated expressions. Unlike previous approaches that rely on a single terminal score, SED-MCTS uses counterfactual subtree interventions to estimate local structural contributions, guiding the search toward reliable construction edges and preserving useful components in a structural archive. The method extends naturally to coupled multiphysics systems and demonstrates improved performance, efficiency, and robustness across diverse PDE benchmarks, especially under noisy or scarce observations.
By Yunpeng Gong, Huolong Wu, Can Yang, Min Jiang