In order to accelerate the NES search phase, we generated the pool using the weight sharing schemes proposed by Random Search with WeightSharing[37]andDARTS[39]. Specifically, we trained one-shot weight-sharing models usingeachof these two algorithms, then we sampled architectures from the weightshared models uniformly at random to build the pool.

Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling.However, existing work focuses on training an extra reward model or fusion model to select or combine all candidate answers, posing a great challenge to the generalization on unseen data distributions.Besides, prior methods use textual responses as communication media, ignoring the valuable information in the internal representations.In this work, we propose a training-free ensemble framework \textsc{DeePEn}, fusing the informative probability distributions yielded by different LLMs at each decoding step.Unfortunately, the vocabulary discrepancy between heterogeneous LLMs directly makes averaging the distributions unfeasible due to the token misalignment.To address this challenge, \textsc{DeePEn} maps the probability distribution of each model from its own probability space to a universal \textit{relative space} based on the relative representation theory, and performs aggregation.Next, we devise a search-based inverse transformation to transform the aggregated result back to the probability space of one of the ensembling LLMs (main model), in order to determine the next token.We conduct extensive experiments on ensembles of different number of LLMs, ensembles of LLMs with different architectures, and ensembles between the LLM and the specialist model.Experimental results show that (i) \textsc{DeePEn} achieves consistent improvements across six benchmarks covering subject examination, reasoning, and knowledge, (ii) a well-performing specialist model can benefit from a less effective LLM through distribution fusion, and (iii) \textsc{DeePEn} has complementary strengths with other ensemble methods such as voting.

Objective: Current ICU enteral feeding remains sub-optimal due to limited personalization and ongoing uncertainty about appropriate calorie, protein, and fluid targets--particularly in the context of rapidly changing metabolic demands and heterogeneous responses to therapeutic interventions. This study introduces DeepEN, a novel reinforcement learning (RL)-based framework designed to dynamically personalize enteral nutrition (EN) dosing for critically ill patients using electronic health record data. Methods: DeepEN was trained on data from over 11,000 ICU patients in the MIMIC-IV database to generate 4-hourly, patient-specific targets for caloric, protein, and fluid intake. The model's state space integrates demographics, comorbidities, vital signs, laboratory measurements, and recent interventions considered relevant to nutritional management. The reward function was designed with domain expertise to balance short-term physiological and nutrition-related goals with long-term survival outcomes, reflecting real-world clinical priorities. The framework employs a dueling double deep Q-network with Conservative Q-Learning regularization to ensure safe and reliable policy learning from retrospective data. Model performance was benchmarked against both clinician-derived and guideline-based policies. Results: DeepEN outperformed both clinician and guideline-based policies, achieving a 3.7 0.17 percentage-point absolute reduction in estimated morarXiv:2510.08350v2 [cs.LG] 19 Nov 2025 tality compared with the clinician policy (18.8% vs 22.5%) and higher expected returns relative to the gold-standard guideline policy (11.89 vs 8.11). Control of key nutritional biomarkers was also improved under the learned policy.

The plots show the NLL vs. ensemble sizes on CIFAR-10

Large language models (LLMs) exhibit complementary strengths in various tasks, motivating the research of LLM ensembling.However, existing work focuses on training an extra reward model or fusion model to select or combine all candidate answers, posing a great challenge to the generalization on unseen data distributions.Besides, prior methods use textual responses as communication media, ignoring the valuable information in the internal representations.In this work, we propose a training-free ensemble framework \textsc{DeePEn}, fusing the informative probability distributions yielded by different LLMs at each decoding step.Unfortunately, the vocabulary discrepancy between heterogeneous LLMs directly makes averaging the distributions unfeasible due to the token misalignment.To address this challenge, \textsc{DeePEn} maps the probability distribution of each model from its own probability space to a universal \textit{relative space} based on the relative representation theory, and performs aggregation.Next, we devise a search-based inverse transformation to transform the aggregated result back to the probability space of one of the ensembling LLMs (main model), in order to determine the next token.We conduct extensive experiments on ensembles of different number of LLMs, ensembles of LLMs with different architectures, and ensembles between the LLM and the specialist model.Experimental results show that (i) \textsc{DeePEn} achieves consistent improvements across six benchmarks covering subject examination, reasoning, and knowledge, (ii) a well-performing specialist model can benefit from a less effective LLM through distribution fusion, and (iii) \textsc{DeePEn} has complementary strengths with other ensemble methods such as voting.

Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior distribution, like diffusion models. By contrast, it is challenging for diffusion models to be trained in an E2E fashion. This paper introduces a Deep End-to-End Posterior ENergy (DEEPEN) framework, which enables MAP estimation as well as sampling. We learn the parameters of the posterior, which is the sum of the data consistency error and the negative log-prior distribution, using maximum likelihood optimization in an E2E fashion. The proposed approach does not require algorithm unrolling, and hence has a smaller computational and memory footprint than current E2E methods, while it does not require contraction constraints typically needed by current PnP methods. Our results demonstrate that DEEPEN offers improved performance than current E2E and PnP models in the MAP setting, while it also offers faster sampling compared to diffusion models. In addition, the learned energy-based model is observed to be more robust to changes in image acquisition settings.

Deepfakes - manipulated or forged audio and video media - pose significant security risks to individuals, organizations, and society at large. To address these challenges, machine learning-based classifiers are commonly employed to detect deepfake content. In this paper, we assess the robustness of such classifiers through a systematic penetration testing methodology, which we introduce as DeePen. Our approach operates without prior knowledge of or access to the target deepfake detection models. Instead, it leverages a set of carefully selected signal processing modifications - referred to as attacks - to evaluate model vulnerabilities. Using DeePen, we analyze both real-world production systems and publicly available academic model checkpoints, demonstrating that all tested systems exhibit weaknesses and can be reliably deceived by simple manipulations such as time-stretching or echo addition. Furthermore, our findings reveal that while some attacks can be mitigated by retraining detection systems with knowledge of the specific attack, others remain persistently effective. We release all associated code.

This paper proposes Tensorized LSTMs for efficient sequence learning. It represents hidden layers as tensors, and employs cross-layer memory cell convolution for efficiency and effectiveness. The model is clearly formulated. Experimental results show the utility of the proposed method. Although the paper is well written, I still have some questions/confusion as follows.