The paper introduces GNM Head, a generative anthropometric model that extends beyond traditional head models by including detailed ocular and intra‑oral structures such as eyeballs, teeth, and tongue. Built from a large collection of high‑resolution 3D scans and artist‑crafted samples, GNM offers improved geometric fidelity and state‑of‑the‑art fitting performance for target 3D face scans. The authors release the full framework publicly to support further research and development.
By Stylianos Ploumpis, Jan Bednarik, Gaspard Zoss, Ruslan Guseinov, Luca Prasso, Prashanth Chandran, Oliver Boyne, Vasileios Choutas, Timo Bolkart, Daoye Wang, Menglei Chai, Di Qiu, Sebastian Winberg, Gilles Rainer, Lewis Bridgeman, Leonhard Helminger, Edo Collins, Delio Vicini, J\'er\'emy Riviere, Yannick Boetzel, Alexander Koumis, Stylianos Moschoglou, Jay Busch, Cynthia Herrera, Jacob Still, Scott Ysebert, Peter Lincoln, Sergio Orts Escolano, Christoph Rhemann, Erroll Wood, Thabo Beeler, Stefanos Zafeiriou
The paper introduces Orbformer, a transferable wavefunction model that uses deep neural networks to pretrain on 22,000 equilibrium and dissociating molecular structures. Fine‑tuning Orbformer on unseen molecules achieves an accuracy‑cost ratio comparable to classical multireference methods, consistently reaching chemical accuracy (1 kcal/mol) on standard benchmarks, challenging bond dissociations, and Diels‑Alder reactions. This demonstrates that amortizing the cost of solving the Schrödinger equation across many molecules is feasible in quantum chemistry.
By Adam Foster, Zeno Sch\"atzle, P. Bern\'at Szab\'o, Lixue Cheng, Jonas K\"ohler, Gino Cassella, Nicholas Gao, Jiawei Li, Frank No\'e, Jan Hermann
arXiv:2609.12397v1 Announce Type: new
Abstract: Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement...
By Danning Zhang, Yijing Lin, Shuhan Zhuang, Mengqi Huang, Shaojin Wu, Shancheng Fang, Zhendong Mao
The paper introduces MTD3-BC, a model‑free offline reinforcement learning algorithm that optimizes yaw control for wind farms amid changing wind directions. By learning from a pre‑collected dataset and incorporating an action consistency term, it reduces the need for extensive simulator interactions. Experimental wind‑tunnel tests show that MTD3‑BC improves farm‑level power output by about 10% compared to a greedy baseline and matches a model‑based benchmark, all while cutting training costs dramatically.
By Yuhan Su, Hongyang Dong, Simone Tamaro, Filippo Campagnolo, Carlo L. Bottasso, Xiaowei Zhao
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.
By \'Edouard Gu\'egain, Tristan Coignion
The paper presents a curriculum‑based adversarial heterogeneous agent reinforcement learning (HARL‑AC) approach for autonomous quad‑copter landing on a ship deck in maritime settings. Using Heterogeneous‑Agent Proximal Policy Optimization (HAPPO) in NVIDIA Isaac Lab, the authors train a cooperative control policy that outperforms domain‑randomized baselines, achieving up to 97.5% success on in‑distribution sea states and higher median success and lower crash rates on out‑of‑distribution sea states. The adversarially trained policy also exhibits more cautious behavior, slightly increasing timeouts but improving safety in severe, unseen conditions.
By Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman, Kurt Driessens, Rico M\"ockel
The paper introduces a new criticality metric specifically designed for vulnerable road users (VRUs) and a scenario‑independent prediction framework that applies to all traffic participants. The VRU‑centric metric improves pedestrian criticality classification by up to 50 %, while the prediction framework surpasses state‑of‑the‑art metrics by 275 %, achieving an F1‑score of 0.96 on the DeepAccident dataset. These advances enable more accurate, scenario‑agnostic safety assessments for autonomous driving systems.
By J\"org Gamerdinger, Victor Schwarzenberger, Philipp Schmid, Sven Teufel, Oliver Bringmann
R2VC is a modular fact‑checking system that separates retrieval, reasoning, verification, and confidence calibration. It uses hybrid sparse‑plus‑dense Wikipedia retrieval, a fine‑tuned generator for structured verdicts, an NLI cross‑encoder for selecting evidence‑based candidates, and a lightweight calibrator for confidence and abstention. On the FEVER benchmark, R2VC improves accuracy by 13.74% over a baseline and shows that verifier‑based candidate selection and calibration are key contributors to performance.
By Dhruv Dixit, Paritosh Pandey
GAUGE is a new offline protocol that evaluates whether the common practice of using an LLM-as-a-judge to rank task‑oriented agents actually aligns with a verifiable reward. Across 25 agents from six providers on two benchmarks, GAUGE finds that user satisfaction scores are largely uncorrelated with task success, and that the judge’s ranking loses precision when agents are closely matched in performance. The study highlights a gap between ranking validity and construct validity in current evaluation practices.
By Umesh Bodhwani, Thanh Tran, Kai Wei
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
By Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu, Gourav Datta, Shreyas Sen
The paper investigates whether small models distilled from larger ones behave similarly when using byte versus token tokenization. It introduces two methods—Marginalize‑It (approximate) and End‑Of‑Token (exact)—to convert token logits to byte logits, and conducts a large‑scale study on decoder‑only dense transformers ranging from 1 billion to 1 trillion bytes of data. Results show that while token‑based models excel early, byte‑based models eventually surpass them with more compute, achieving higher performance ceilings, greater data efficiency, and lower logit storage costs.
By Kalyani Marathe, Artidoro Pagnoni, Tomasz Limisiewicz, Margaret Li, Mike Lewis, Luke Zettlemoyer, Srinivasan Iyer
BEAST is the first Bayesian Swin Transformer for atmospheric forecasting at 0.25° global resolution, capable of quantifying both aleatoric and epistemic uncertainty. The authors introduce a 4D‑parallelization scheme with domain‑tensor‑parallelism and a novel uncertainty parallel method, allowing a 2.4‑billion‑parameter model to reach 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs. A 700‑million‑parameter BEAST trained on 40 years of data achieves predictive skill comparable to state‑of‑the‑art probabilistic AI models, predicts extreme events with high accuracy, and generates large ensembles 3–4× faster than the current best AI model.
By Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus G\"otz, Charlotte Debus
The paper introduces PA-CDM, a new position‑aware character detection matching metric for handwritten mathematical expression recognition that improves on existing render‑based and tree‑edit metrics by incorporating position‑forest encoding and divergence‑level weighting. It also presents StructPerturb v2.0, a benchmark of 1,340 controlled perturbation pairs, and a cross‑metric consistency protocol that includes a sensitivity matrix, a human study, and LLM‑judge calibration. In a six‑annotator study, PA‑CDM achieves the highest correlation with human judgments (Spearman rho = 0.9535) among seven automatic metrics, approaching the performance of a costly LLM judge while remaining deterministic and cost‑free.
By Shiliang Luo (East China Normal University)
The paper introduces Spatial‑FAD, a few‑shot medical anomaly detection framework that fuses Vision‑Language Model (CLIP) semantics with spatial priors from Vision Foundation Models (DINO). A VFM‑enhanced adapter injects structural affinity into CLIP features, while a sliding‑window aggregation produces high‑resolution embeddings for finer lesion localization. Prototype‑enhanced support memory further improves efficiency and performance, yielding significant gains on Liver CT, Retinal OCT, and Brain MRI datasets, notably an 11.4% Dice improvement in 4‑shot scenarios.
By Juzheng Miao, Yuchen Yuan, Cheng Chen, Pheng-Ann Heng
The paper introduces an online sketched Newton method that uses a generalized accelerated sketch-and-project solver (GAS) to approximate Newton directions efficiently. GAS incorporates Nesterov momentum and a flexible projection metric, achieving accelerated convergence and reduced computational cost. The authors prove asymptotic normality and a functional central limit theorem for the averaged iterates, enabling an online inference procedure via random scaling that yields a pivotal test statistic with a parameter‑free limiting distribution.
By Xinchen Du, Elizaveta Rebrova, Micha{\l} Derezi\'{n}ski, Sen Na
ExpertHTR is a unified vision‑language framework for handwritten text recognition that tackles the challenge of small, heterogeneous datasets by organizing structural annotations into a common Page‑Region‑Line representation. It defines four related training tasks—complete transcription, physical‑line coverage, text localization, and localized recognition—without extra manual labels. The model combines a jointly trained dense backbone with a sparse Mixture‑of‑Experts architecture, using Sparsegen routing and regularization to adaptively activate experts, achieving state‑of‑the‑art results on the IAM benchmark and outperforming general‑purpose OCR systems on most datasets.
By Dang Hoai Nam, Nguyen Duy Hieu, Quang Huu Hieu, Vo Nguyen Le Duy
The paper introduces Fundamental Dynamical Units (FDUs), signed three‑node interaction patterns that reduce the combinatorial complexity of interaction architectures in networked dynamical systems. By embedding FDU‑regularized structural inference into a physics‑informed neural ODE, the authors jointly recover interaction structure and perturbation‑resolved trajectories, demonstrating the approach on synthetic benchmarks. This framework enables motif‑prescribed intervention design and mechanistically interpretable inference in complex systems.
By Nima Nouri
The paper introduces a pre‑action verification framework for large language model agents that emit shell commands or code edits. By running deterministic checks before execution, the system can catch invalid commands (95.8% success with a 10.0% false‑positive rate) and prevent silent failures in code edits, achieving high recall while minimizing false positives. The authors provide benchmarks, verifiers, and guards for both action modalities.
By Asaad Althoubi
The paper introduces Decision-Flow Sampling (DF‑Sample), a training‑free, data‑free inference framework that builds a hierarchical reasoning tree, evaluates entire trajectories, and back‑propagates utilities to guide branching decisions. Unlike local step‑wise sampling, DF‑Sample explicitly assesses global paths, enabling it to recover high‑quality, low‑probability reasoning chains that standard decoding misses. On the GPQA benchmark, DF‑Sample attains 45.6% accuracy, outperforming power sampling (38.9%) and GRPO (39.9%) and consistently surpassing baselines across multiple models and benchmarks, demonstrating significant latent reasoning potential in pretrained LLMs.
By Zhendong Mi, Shaoyi Huang
ParaRecover is a new process-level benchmark designed to evaluate error localization and recovery in multi-turn parallel tool-use agents. It contains 10,626 instances across two difficulty levels, built on a taxonomy of 14 error types that cover planning dependencies, tool selection, and argument matching. The benchmark introduces the SDE rubric, which assesses structural integrity, diagnostic reasoning, and evolutionary strategy during agent execution, and demonstrates that it can guide improvements in agents’ reflective recovery capabilities.
By Bowen Guan, Zhentao Yin, Yanming Shen