GenAI4ELab/papercli-papers huggingface dataset:
Nef-Net v2: Adapting Electrocardio Panorama in the wild
Authors: Zehui Zhan; Yaojun Hu; Jiajing Zhang; Wanchen Lian; Wanqing Wu; Jintai Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals
from a fixed set of anatomical viewpoints defined by lead placement. However, cer-
tain cardiac conditions (e.g., Brugada syndrome) require additional, non-standard
viewpoints to reveal diagnostically critical patterns that may be absent in standard
leads. To systematically overcome this limitation, Nef-Net was recently introduced
to reconstruct a continuous electrocardiac field, enabling virtual observation of
ECG signals from arbitrary views (termed Electrocardio Panorama). Despite
its promise, Nef-Net operates under idealized assumptions and faces in-the-wild
challenges, such as long-duration ECG modeling, robustness to device-specific
signal artifacts, and suboptimal lead placement calibration. This paper presents
NEF-NET V2, an enhanced framework for realistic panoramic ECG synthesis that
supports arbitrary-length signal synthesis from any desired view, generalizes across
ECG devices, and compensates for operator-induced deviations in electrode place-
ment. These capabilities are enabled by a newly designed model architecture that
performs direct view transformation, incorporating a workflow comprising offline
pretraining, device calibration tuning steps as well as an on-the-fly calibration step
for patient-specific adaptation. To rigorously evaluate panoramic ECG synthe-
sis, we construct a new Electrocardio Panorama benchmark, called Panobench,
comprising 4470 recordings with 48 views per subject, capturing the full spatial
variability of cardiac electrical activity. Experimental results show that NEF-NET
V2 delivers substantial improvements over Nef-Net, yielding an increase of around
6 dB in PSNR in real-world settings. Our data and code are publicly available at
https://github.com/HKUSTGZ-ML4Health-Lab/NEFNET-v2.
Efficient Reasoning with Balanced Thinking
Authors: Yulin Li; Tengyao Tu; Li Ding; Junjie Wang; Hui-Ling Zhen; Yixin Chen; Yong Li; Zhuotao Tian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose \textsc{ReBalance}, a training-free framework that achieves efficient reasoning with balanced thinking. \textsc{ReBalance} leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs’ reasoning trajectories. A dynamic control function modulates this vector’s strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that \textsc{ReBalance} effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Code and models will be made publicly available.
Noise Stability of Transformer Models
Authors: Themistoklis Haris; Zihan Zhang; Yuichi Yoshida
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding simplicity biases in deep learning offers a promising path toward developing reliable AI. A common metric for this, inspired by Boolean function analysis, is average sensitivity, which captures a model's robustness to single-token perturbations. We argue that average sensitivity has two key limitations: it lacks a natural generalization to real-valued domains and fails to explain the "junta-like" input dependence we empirically observe in modern LLMs. To address these limitations, we propose *noise stability* as a more comprehensive simplicity metric. Noise stability expresses a model's robustness to correlated noise applied to *all* input coordinates simultaneously. We provide a theoretical analysis of noise stability for single-layer attention and ReLU MLP layers and tackle the multi-layer propagation problem with a covariance interval propagation approach. Building on this theory, we develop a practical *noise stability regularization* method. Experiments on algorithmic and next-token-prediction tasks show that our regularizer consistently catalyzes grokking and accelerates training by approximately $35$\% and $75$\% respectively. Our results establish noise stability as a powerful tool for understanding and improving modern Transformers.
FieryGS: In-the-Wild Fire Synthesis with Physics-Integrated Gaussian Splatting
Authors: Qianfan Shen; Ningxiao Tao; Qiyu Dai; Tianle Chen; Minghan Qin; Yongjie Zhang; Mengyu Chu; Wenzheng Chen; Baoquan Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We consider the problem of synthesizing photorealistic, physically plausible combustion effects in in-the-wild 3D scenes. Traditional CFD and graphics pipelines can produce realistic fire effects but rely on handcrafted geometry, expert-tuned parameters, and labor-intensive workflows, limiting their scalability to the real world. Recent scene modeling advances like 3D Gaussian Splatting (3DGS) enable high-fidelity real-world scene reconstruction, yet lack physical grounding for combustion. To bridge this gap, we propose FieryGS, a physically-based framework that integrates physically-accurate and user-controllable combustion simulation and rendering within the 3DGS pipeline, enabling realistic fire synthesis for real scenes. Our approach tightly couples three key modules: (1) multimodal large-language-model-based physical material reasoning, (2) efficient volumetric combustion simulation, and (3) a unified renderer for fire and 3DGS. By unifying reconstruction, physical reasoning, simulation, and rendering, FieryGS removes manual tuning and automatically generates realistic, controllable fire dynamics consistent with scene geometry and materials. Our framework supports complex combustion phenomena—including flame propagation, smoke dispersion, and surface carbonization—with precise user control over fire intensity, airflow, ignition location and other combustion parameters. Evaluated on diverse indoor and outdoor scenes, FieryGS outperforms all comparative baselines in visual realism, physical fidelity, and controllability.
VideoPhy-2: A Challenging Action-Centric Physical Commonsense Evaluation in Video Generation
Authors: Hritik Bansal; Clark Peng; Yonatan Bitton; Roman Goldenberg; Aditya Grover; Kai-Wei Chang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world actions remains unclear (e.g., playing tennis, backflip). Existing benchmarks suffer from limitations such as limited size, lack of human evaluation, sim-to-real gaps, and absence of fine-grained physical rule analysis. To address this, we introduce VideoPhy-2, an action-centric dataset for evaluating physical commonsense in generated videos. We curate 4000 diverse and detailed prompts for video synthesis from modern generative models. We perform human evaluation that assesses semantic adherence, physical commonsense, and grounding of physical rules in the generated videos. Our findings reveal major shortcomings, with even the best model achieving only $47.7\%$ joint performance (i.e., high semantic and physical commonsense adherence) on the hard subset of VideoPhy-2. We find that the models particularly struggle with conservation laws like mass and momentum. Finally, we also train VideoPhy-2-AutoEval, an automatic evaluator for fast, reliable assessment on our dataset. Overall, VideoPhy-2 serves as a rigorous benchmark, exposing critical gaps in video generative models and guiding future research in physically-grounded video generation. The data and code is available at \url{https://videophy2.github.io/}.
CHROMA: Consistent Harmonization of Multi-View Appearance via Bilateral Grid Prediction
Authors: Jisu Shin; Richard Shaw; Seunghyun Shin; Zhensong Zhang; Hae-Gon Jeon; Eduardo Pérez-Pellitero
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern camera pipelines apply extensive on-device processing, such as exposure adjustment, white balance, and color correction, which, while beneficial individually, often introduce photometric inconsistencies across views. These appearance variations violate multi-view consistency and degrade novel view synthesis. 
Joint optimization of scene-specific representations and per-image appearance embeddings has been proposed to address this issue, but with increased computational complexity and slower training. 
In this work, we propose a generalizable, feed-forward approach that predicts spatially adaptive bilateral grids to correct photometric variations in a multi-view consistent manner. Our model processes hundreds of frames in a single step, enabling efficient large-scale harmonization, and seamlessly integrates into downstream 3D reconstruction models, providing cross-scene generalization without requiring scene-specific retraining. To overcome the lack of paired data, we employ a hybrid self-supervised rendering loss leveraging 3D foundation models, improving generalization to real-world variations.
Extensive experiments show that our approach outperforms or matches the reconstruction quality of existing scene-specific optimization methods with appearance modeling, without significantly affecting the training time of baseline 3D models.
Reconstruct Anything Model a lightweight general model for computational imaging
Authors: Matthieu Terris; Samuel Hurault; Maxime Song; Julián Tachella
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion methods leveraging pretrained denoisers, and unrolled architectures that are trained end-to-end for specific imaging problems. Iterative methods in the first class are computationally costly and often yield suboptimal reconstruction performance, whereas unrolled architectures are generally problem-specific and require expensive training. In this work, we propose a novel non-iterative, lightweight architecture that incorporates knowledge about the forward operator (acquisition physics and noise parameters) without relying on unrolling. Our model is trained to solve a wide range of inverse problems, such as deblurring, magnetic resonance imaging, computed tomography, inpainting, and super-resolution, and handles arbitrary image sizes and channels, such as grayscale, complex, and color data. The proposed model can be easily adapted to unseen inverse problems or datasets with a few fine-tuning steps (up to a few images) in a self-supervised way, without ground-truth references. Throughout a series of experiments, we demonstrate state-of-the-art performance from medical imaging to low-photon imaging and microscopy.
Our code is available at https://github.com/matthieutrs/ram.
Text2Arch: A Dataset for Generating Scientific Architecture Diagrams from Natural Language Descriptions
Authors: Shivank Garg; Sankalp Mittal; Manish Gupta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Communicating complex system designs or scientific processes through text alone is inefficient and prone to ambiguity. A system that automatically generates scientific architecture diagrams from text with high semantic fidelity can be useful in multiple applications like enterprise architecture visualization, AI-driven software design, and educational content creation. Hence, in this paper, we focus on leveraging language models to perform semantic understanding of the input text description to generate intermediate code that can be processed to generate high-fidelity architecture diagrams. Unfortunately, no clean large-scale open-access dataset exists, implying lack of any effective open models for this task. Hence, we contribute a comprehensive dataset, \system, comprising scientific architecture images, their corresponding textual descriptions, and associated DOT code representations.  Leveraging this resource, we fine-tune a suite of small language models, and also perform in-context learning using GPT-4o. Through extensive experimentation, we show that \system{} models significantly outperform existing baseline models like DiagramAgent and perform at par with in-context learning based generations from GPT-4o. We have added code and data as Supplementary material, and will make them (and models) publicly available on acceptance.
CTBench: Cryptocurrency Time Series Generation Benchmark
Authors: Yihao Ang; Qiang Wang; Qiang Huang; Yifan Bao; Xinyu Xi; Anthony Kum Hoe Tung; Chen Jin; Zhiyong Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Synthetic time series are vital for data augmentation, stress testing, and prototyping in quantitative finance. Yet in cryptocurrency markets, characterized by 24/7 trading, extreme volatility, and rapid regime shifts, existing Time Series Generation (TSG) methods and benchmarks often fall short, jeopardizing practical utility. Most prior work targets non-financial or traditional financial domains, focuses narrowly on classification and forecasting while neglecting crypto-specific complexities, and lacks critical financial evaluations, particularly for trading applications. To bridge these gaps, we introduce \textbf{CTBench}, the first \textbf{C}ryptocurrency \textbf{T}ime series generation \textbf{Bench}mark. It curates an open-source dataset of 452 tokens and evaluates models across 13 metrics spanning forecasting accuracy, rank fidelity, trading performance, risk assessment, and computational efficiency. A key innovation is a dual-task evaluation framework: the Predictive Utility measures how well synthetic data preserves temporal and cross-sectional patterns for forecasting, while the Statistical Arbitrage assesses whether reconstructed series support mean-reverting signals for trading. We systematically benchmark eight state-of-the-art models from five TSG families across four market regimes, revealing trade-offs between statistical quality and real-world profitability. Notably, CTBench provides ranking analysis and practical guidance for deploying TSG models in crypto analytics and trading applications. The source code is available at \url{https://github.com/MilleXi/CTBench/}.
Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation
Authors: Sayash Kapoor; Benedikt Stroebl; Peter Kirgis; Nitya Nadgir; Zachary S Siegel; Boyi Wei; Tianci Xue; Ziru Chen; Felix Chen; Saiteja Utpala; Franck Ndzomga; Dheeraj Oruganty; Sophie Luskin; Kangheng Liu; Botao Yu; Amit Arora; Dongyoon Hahm; Harsh Trivedi; Huan Sun; Juyong Lee; Tengjun Jin; Yifan Mai; Yifei Zhou; Yuxuan Zhu; Rishi Bommasani; Daniel Kang; Dawn Song; Peter Henderson; Yu Su; Percy Liang; Arvind Narayanan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of how well agents really work (Figure 1). We introduce the Holistic Agent Leaderboard (HAL) to address these challenges. We make three main contributions. First, we provide a standardized evaluation harness that orchestrates parallel evaluations across hundreds of VMs, reducing evaluation time from weeks to hours while eliminating common implementation bugs. Second, we conduct three-dimensional analysis spanning models, scaffolds, and benchmarks. We validate the harness by conducting 21,730 agent rollouts across 9 models and 9 benchmarks in coding, web navigation, science, and customer service with a total cost of about $40,000. Our analysis reveals surprising insights, such as higher reasoning effort reducing accuracy in the majority of runs. Third, we use LLM-aided log inspection to uncover previously unreported behaviors, such as searching for the benchmark on HuggingFace instead of solving a task, or misusing credit cards in flight booking tasks. We share all agent logs, comprising 2.5B tokens of language model calls, to incentivize further research into agent behavior. By standardizing how the field evaluates agents and addressing common pitfalls in agent evaluation, we hope to shift the focus from agents that ace benchmarks to agents that work reliably in the real world.
Detecting Data Contamination in LLMs via In-Context Learning
Authors: Michał Zawalski; Meriem Boubdir; Klaudia Bałazy; Besmira Nushi; Pablo Ribalta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Contamination Detection via Context (CoDeC), a practical and accurate method to detect and quantify training data contamination in large language models. CoDeC distinguishes between data memorized during training and data outside the training distribution by measuring how in-context learning affects model performance. We find that in‑context examples typically boost confidence for unseen datasets but may reduce it when the dataset was part of training, due to disrupted memorization patterns. Experiments show that CoDeC produces interpretable contamination scores that clearly separate seen and unseen datasets, and reveals strong evidence of memorization in open-weight models with undisclosed training corpora. The method is simple, automated, and both model- and dataset-agnostic, making it easy to integrate with benchmark evaluations.
Efficient Zero-shot Inpainting with Decoupled Diffusion Guidance
Authors: Badr MOUFAD; Yazid Janati; Navid Bagheri Shouraki; Alain Oliviero Durmus; Thomas Hirtz; Eric Moulines; Jimmy Olsson
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage a pretrained diffusion model, without any retraining, have been shown to achieve highly effective reconstructions. However, state-of-the-art zero-shot methods typically rely on a sequence of surrogate likelihood functions, whose scores are used as proxies for the ideal score. This procedure however requires vector-Jacobian products through the denoiser at every reverse step, introducing significant memory and runtime overhead. To address this issue, we propose a new likelihood surrogate that yields simple and efficient to sample Gaussian posterior transitions, sidestepping the  backpropagation through the denoiser network. Our extensive experiments show that our method achieves strong observation consistency compared with fine-tuned baselines and produces coherent, high-quality reconstructions, all while significantly reducing inference cost.
Reversible Primitive–Composition Alignment for Continual Vision–Language Learning
Authors: Canran Xiao; Tianxiang Xu; siyuanma; Yiyang Jiang; Haoyu Gao; Yuhan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language (VL) models are increasingly deployed in non-stationary settings, yet under sequential adaptation they often preserve primitive recognition while losing compositional structure, especially with tight rehearsal budgets and no task IDs. We address this gap by asking how a continual VL system can maintain structurally dependable behaviour while safeguarding zero-shot performance. We introduce Compo-ReAlign, a structure-first recipe built around three components: a reversible composer that maps primitive embeddings to compositions by design, a multi-positive InfoNCE that jointly aligns textual and composed views of the same target, and a spectral trust region that clips updates when alignment sensitivity inflates.  Across compositional DIL and multi-domain MTIL retrieval, Compo-ReAlign sets a new state of the art, improves over the strongest prior by +2.4 R@1, and reduces forgetting by 40%. We provide a compact, reversible alignment head with geometry-aware training for compositionally robust VL continual learning.
Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecasting
Authors: Siyuan Wang; Peng Chen; Yihang Wang; Wanghui Qiu; Chenjuan Guo; Bin Yang; Yang Shu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing time series forecasting methods primarily rely on the numerical data itself. However, real-world time series exhibit complex patterns associated with multimodal information, making them difficult to predict with numerical data alone. While several multimodal time series forecasting methods have emerged, they either utilize text with limited supplementary information or focus merely on representation extraction, extracting minimal textual information for forecasting. To unlock the Value of Text, we propose VoT, a method with Event-driven Reasoning and Multi-level Alignment. Event-driven Reasoning combines the rich information in exogenous text with the powerful reasoning capabilities of LLMs for time series forecasting. To guide the LLMs in effective reasoning, we propose the Historical In-context Learning that retrieves and applies historical examples as in-context guidance. To maximize the utilization of text, we propose Multi-level Alignment. At the representation level, we utilize the Endogenous Text Alignment to integrate the endogenous text information with the time series. At the prediction level, we design the Adaptive Frequency Fusion to fuse the frequency components of event-driven prediction and numerical prediction to achieve complementary advantages. Experiments on real-world datasets across 10 domains demonstrate significant improvements over existing methods, validating the effectiveness of our approach in the utilization of text. The code is made available at https://github.com/decisionintelligence/VoT.