GenAI4ELab/papercli-papers huggingface dataset:
Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models
Authors: Weidi Luo; Tianyu Lu; Qiming Zhang; Xiaogeng Liu; Bin Hu; Yue Zhao; Jieyu Zhao; Song Gao; Patrick McDaniel; Zhen Xiang; Chaowei Xiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in multi-modal large reasoning models (MLRMs) have shown significant ability to interpret complex visual content. While these models possess impressive reasoning capabilities, they also introduce novel and underexplored privacy risks. In this paper, we identify a novel category of privacy leakage in MLRMs: Adversaries can infer sensitive geolocation information, such as users' home addresses or neighborhoods, from user-generated images, including selfies captured in private settings. To formalize and evaluate these risks, we propose a three-level privacy risk framework that categorizes image based on contextual sensitivity and potential for geolocation inference. We further introduce DoxBench, a curated dataset of 500 real-world images reflecting diverse privacy scenarios divided into 6 categories. Our evaluation across 13 advanced MLRMs and MLLMs demonstrates that most of these models outperform non-expert humans in geolocation inference and can effectively leak location-related private information. This significantly lowers the barrier for adversaries to obtain users' sensitive geolocation information. We further analyze and identify two primary factors contributing to this vulnerability: (1) MLRMs exhibit strong geolocation reasoning capabilities by leveraging visual clues in combination with their internal world knowledge; and (2) MLRMs frequently rely on privacy-related visual clues for inference without any built-in mechanisms to suppress or avoid such usage. To better understand and demonstrate real-world attack feasibility, we propose GeoMiner, a collaborative attack framework that decomposes the prediction process into two stages consisting of clue extraction and reasoning to improve geolocation performance. Our findings highlight the urgent need to reassess inference-time privacy risks in MLRMs to better protect users' sensitive information.
Distribution-informed Online Conformal Prediction
Authors: Dongjian Hu; Junxi Wu; Shu-Tao Xia; Changliang Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Conformal prediction provides a pivotal and flexible technique for uncertainty quantification by constructing prediction sets with a predefined coverage rate. Many online conformal prediction methods have been developed to address data distribution shifts in fully adversarial environments, resulting in overly conservative prediction sets. We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule. Through estimated cumulative distribution function of non-conformity scores, COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate. We establish a joint bound on coverage and regret, which further confirms the validity of our approach. We also prove that COP achieves distribution-free, finite-sample coverage under arbitrary learning rates and can converge when scores are i.i.d. The experimental results also show that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.
Depth Anything 3: Recovering the Visual Space from Any Views
Authors: Haotong Lin; Sili Chen; Jun Hao Liew; Donny Y. Chen; Zhenyu Li; Yang Zhao; Sida Peng; Hengkai Guo; Xiaowei Zhou; Guang Shi; Jiashi Feng; Bingyi Kang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. 
In pursuit of minimal modeling, DA3 yields two key insights:
a single plain transformer (e.g., vanilla DINOv2 encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2).
We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 35.7\% in camera pose accuracy and 23.6\% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets.
Estimating Worst-Case Frontier Risks of Open-Weight LLMs
Authors: Eric Wallace; Olivia Watkins; Miles Wang; Kai Chen; Chris Koch
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we study the worst-case frontier risks of the OpenAI gpt-oss model. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning gpt-oss to be as capable as possible in two domains: biology and cybersecurity. To maximize biological risk (biorisk), we curate tasks related to threat creation and train gpt-oss in an RL environment with web browsing. To maximize cybersecurity risk, we train gpt-oss in an agentic coding environment to solve capture-the-flag (CTF) challenges. We compare these MFT models against open- and closed-weight LLMs on frontier risk evaluations. Compared to frontier closed-weight models, MFT gpt-oss underperforms OpenAI o3, a model that is below Preparedness High capability level for biorisk and cybersecurity. Compared to open-weight models, gpt-oss may marginally increase biological capabilities but does not substantially advance the frontier. Taken together, these results led us to believe that the net new harm from releasing gpt-oss is limited, and we hope that our MFT approach can serve as useful guidance for estimating harm from future open-weight releases.
Efficient Degradation-agnostic Image Restoration via Channel-Wise Functional Decomposition and Manifold Regularization
Authors: Bin Ren; Yawei Li; Xu Zheng; Yuqian Fu; Danda Pani Paudel; Hong Liu; Ming-Hsuan Yang; Luc Van Gool; Nicu Sebe
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Degradation-agnostic image restoration aims to handle diverse corruptions with one unified model, but faces fundamental challenges in balancing efficiency and performance across different degradation types. Existing approaches either sacrifice efficiency for versatility or fail to capture the distinct representational requirements of various degradations. We present MIRAGE, an efficient framework that addresses these challenges through two key innovations. First, we propose a channel-wise functional decomposition that systematically repurposes channel redundancy in attention mechanisms by assigning CNN, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively. This principled decomposition enables degradation-agnostic learning while achieving superior efficiency-performance trade-offs. Second, we introduce manifold regularization that performs cross-layer contrastive alignment in Symmetric Positive Definite (SPD) space, which empirically improves feature consistency and generalization across degradation types. Extensive experiments demonstrate that MIRAGE achieves state-of-the-art performance with remarkable efficiency, outperforming existing methods in various all-in-one IR settings while offering a scalable and generalizable solution for challenging unseen IR scenarios.
GoldenStart: Q-Guided Priors and Entropy Control for Distilling Flow Policies
Authors: He Zhang; Ying Sun; Hui Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Flow-matching policies hold great promise for reinforcement learning (RL) by capturing complex, multi-modal action distributions. However, their practical application is often hindered by prohibitive inference latency and ineffective online exploration.  Although recent works have employed one-step distillation for fast inference, the structure of the initial noise distribution remains an overlooked factor that presents significant untapped potential. This overlooked factor, along with the challenge of controlling policy stochasticity, constitutes two critical areas for advancing distilled flow-matching policies. To overcome these limitations, we propose GoldenStart (GS-flow), a policy distillation method with Q-guided priors and explicit entropy control. Instead of initializing generation from uninformed noise, we introduce a Q-guided prior modeled by a conditional VAE. This state-conditioned prior repositions the starting points of the one-step generation process into high-Q regions, effectively providing a ``golden start'' that shortcuts the policy to promising actions. Furthermore, for effective online exploration, we enable our distilled actor to output a stochastic distribution instead of a deterministic point. This is governed by entropy regularization, allowing the policy to shift from pure exploitation to principled exploration. Our integrated framework demonstrates that by designing the generative startpoint and explicitly controlling policy entropy, it is possible to achieve efficient and exploratory policies, bridging the generative models and the practical actor-critic methods. We conduct extensive experiments on offline and online continuous control benchmarks, where our method significantly outperforms prior state-of-the-art approaches.
AutoCode: LLMs as Problem Setters for Competitive Programming
Authors: Shang Zhou; Zihan Zheng; Kaiyuan Liu; Zeyu Shen; Zerui Cheng; Zexing Chen; Hansen He; Jianzhu Yao; Huanzhi Mao; Qiuyang Mang; Tianfu Fu; Beichen Li; Dongruixuan Li; Wenhao Chai; Zhuang Liu; Aleksandra Korolova; Peter Henderson; Natasha Jaques; Pramod Viswanath; Saining Xie; Jingbo Shang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Writing competitive programming problems is exacting. Authors must: set constraints, input distributions, and edge cases that rule out shortcuts; target specific algorithms (e.g., max-flow, dynamic programming, data structures); and calibrate complexity beyond the reach of most competitors. We argue that this makes for an ideal test of general large language model capabilities and study whether they can do this reliably. We introduce AutoCode, which uses multiple rounds of validation to yield competition-grade problem statements and test cases. On held-out problems, AutoCode test suites approach 99% consistency with official judgments, a significant improvement over current state-of-the-art methods like HardTests, which achieve less than 81%. Furthermore, starting with a random seed problem, AutoCode can create novel variants with reference and brute-force solutions. By cross-verifying these generated solutions against test cases, we can further filter out malformed problems. Our system ensures high correctness, as verified by human experts. AutoCode successfully produces novel problems judged by Grandmaster-level (top 0.3%) competitive programmers to be of contest quality.
SongEcho: Towards Cover Song Generation via Instance-Adaptive Element-wise Linear Modulation
Authors: Sifei Li; Yang Li; Zizhou Wang; Yuxin Zhang; Fuzhang Wu; Oliver Deussen; Tong-Yee Lee; Weiming Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cover songs constitute a vital aspect of musical culture, preserving the core melody of an original composition while reinterpreting it to infuse novel emotional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as a conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongEcho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wise Linear Modulation (FiLM) to an Element-wise Linear Modulation (EiLM), to facilitate precise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptive Condition Refinement (IACR), which refines conditioning features by interacting with the hidden states of the generative model, yielding instance-adaptive conditioning. Additionally, to address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k, a high-quality AI song dataset enriched with comprehensive annotations. Experimental results across multiple datasets demonstrate that our approach generates superior cover songs compared to existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at [https://github.com/lsfhuihuiff/SongEcho_ICLR2026](https://github.com/lsfhuihuiff/SongEcho_ICLR2026).
One step further with Monte-Carlo sampler to guide diffusion better
Authors: Minsi Ren; Wenhao Deng; Ruiqi Feng; Tailin Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Stochastic differential equation (SDE)-based generative models have achieved
substantial progress in conditional generation via training-free differentiable
loss-guided approaches. However, existing methodologies utilizing posterior sam-
pling typically confront a substantial estimation error, which results in inaccurate
gradients for guidance and leading to inconsistent generation results. To mitigate
this issue, we propose that performing an additional backward denoising step and
Monte-Carlo sampling (ABMS) can achieve better guided diffusion, which is a
plug-and-play adjustment strategy. To verify the effectiveness of our method, we
provide theoretical analysis and propose the adoption of a dual-evaluation frame-
work, which further serves to highlight the critical problem of cross-condition
interference prevalent in existing approaches. We conduct experiments across var-
ious task settings and data types, mainly including conditional online handwritten
trajectory generation, image inverse problems (inpainting, super resolution and
gaussian deblurring), and molecular inverse design. Experimental results demon-
strate that our approach consistently improves the quality of generation samples
across all the different scenarios.
FullPart: Generating each 3D Part at Full Resolution
Authors: Lihe Ding; Shaocong Dong; Yaokun Li; Chenjian Gao; Xiao Chen; Rui Han; Yihao Kuang; Hong Zhang; Bo Huang; Zhanpeng Huang; Zibin Wang; Dan Xu; Tianfan Xue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficient geometric details. Another line of work adopts an explicit voxel representation but shares a global voxel grid among all parts; this often causes small parts to occupy too few voxels, leading to degraded quality.
In this paper, we propose \textit{FullPart}, a novel framework that combines both implicit and explicit paradigms.
It first derives the bounding box layout through an implicit box vector-set diffusion process, a task that implicit diffusion handles effectively since box tokens contain little geometric detail.
Then, it generates detailed parts, each within its own fixed full-resolution voxel grid. Instead of sharing a global low-resolution space, each part in our method—even small ones—is generated at full resolution, enabling the synthesis of intricate details.
We further introduce a center-point encoding strategy to address the misalignment issue when exchanging information between parts of different actual sizes, thereby maintaining global coherence. Moreover, to tackle the scarcity of reliable part data, we present \textit{PartVerse-XL}, the largest human-annotated 3D part dataset to date with 40K objects and 320K parts. Extensive experiments demonstrate that FullPart achieves state-of-the-art results in 3D part generation. 
Code, model, and dataset are available at https://fullpart3d.github.io.
APPLE: Toward General Active Perception via Reinforcement Learning
Authors: Tim Schneider; Cristiana de Farias; Roberto Calandra; Liming Chen; Jan Peters
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Active perception is a fundamental skill that enables us humans to deal with uncertainty in our inherently partially observable environment. For senses such as touch, where the information is sparse and local, active perception becomes crucial. In recent years, active perception has emerged as an important research domain in robotics. However, current methods are often bound to specific tasks or make strong assumptions, which limit their generality. To address this gap, this work introduces APPLE (Active Perception Policy Learning) – a novel framework that leverages reinforcement learning (RL) to address a range of different active perception problems. APPLE jointly trains a transformer-based perception module and decision-making policy with a unified optimization objective, learning how to actively gather information. By design, APPLE is not limited to a specific task and can, in principle, be applied to a wide range of active perception problems. We evaluate two variants of APPLE across different tasks, including tactile exploration problems from the Tactile MNIST benchmark. Experiments demonstrate the efficacy of APPLE, achieving high accuracies on both regression and classification tasks. These findings underscore the potential of APPLE as a versatile and general framework for advancing active perception in robotics.

Project page: https://timschneider42.github.io/apple
Unified Brain Surface and Volume Registration
Authors: Mazdak Abulnaga; Andrew Hoopes; Malte Hoffmann; Robin Magnet; Maks Ovsjanikov; Lilla Zollei; John Guttag; Bruce Fischl; Adrian V Dalca
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the interior volume. Traditional methods treat volumetric and surface-based registration separately, which often leads to inconsistencies that limit downstream analyses. We propose a deep learning framework, UCS, that registers 3D brain MRI images by jointly aligning both cortical and subcortical regions, through a unified volume-and-surface-based representation. Our approach leverages an intermediate spherical coordinate space to bridge anatomical surface topology with volumetric anatomy, enabling consistent and anatomically accurate alignment. By integrating spherical registration into the learning, our method ensures geometric coherence between volume and surface domains. In a series of experiments on both in-domain and out-of-domain datasets, our method consistently outperforms both classical and machine learning-based registration methods--improving the Dice score by up to 7 points while maintaining regular deformation fields. Additionally, it is orders of magnitude faster than the standard method for this task, and is simpler to use because it requires no additional inputs beyond an MRI scan. Its superior accuracy, fast inference, and ease of use sets a new standard for joint cortical and subcortical registration.
VideoAgentTrek: Computer-Use Pretraining from Unlabeled Videos
Authors: Dunjie Lu; Yiheng Xu; Junli Wang; Haoyuan Wu; Xinyuan Wang; Zekun Wang; Junlin Yang; Hongjin SU; Jixuan Chen; Junda Chen; Yuchen Mao; Junyang Lin; Binyuan Hui; Tao Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training computer-use agents requires massive amounts of GUI interaction data, but manually annotating action trajectories at scale is prohibitively expensive. We present VideoAgentTrek, a scalable pipeline that automatically mines training data from publicly available screen-recorded videos, eliminating the need for manual annotation. Our approach addresses a key challenge: raw videos contain implicit demonstrations but lack explicit action labels. To solve this, we develop Video2Action, an inverse dynamics module (IDM) with two components: (1) a video grounding model that detects and localizes GUI actions with precise temporal boundaries, and (2) an action-content recognizer that extracts structured parameters like click coordinates and typed text. Applied to 39,000 YouTube tutorial videos, our pipeline generates 1.52 million interaction steps. We leverage this data through continued pretraining followed by supervised fine-tuning. On OSWorld-Verified, our approach improves task success rates from 9.3% (SFT-only baseline) to 15.8%, a 70% relative improvement. On AgentNetBench, step accuracy increases from 64.1% to 69.3%. Our results demonstrate that passive internet videos can be transformed into high-quality supervision for computer-use agents, providing a scalable alternative to expensive manual annotation.
Information Estimation with Discrete Diffusion
Authors: Alberto Foresti; Giulio Franzese; Pietro Michiardi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Information-theoretic measures, such as Mutual Information (MI), play a crucial role in understanding non-linear relationships between random variables and are widely used across scientific disciplines. Yet, their use on real-world discrete data remains challenging. Existing methods typically rely on embedding discrete data into a continuous space and apply neural estimators originally designed for continuous distributions. This process requires careful engineering for both the embedding model and estimator architecture, but suffers from issues related to high data dimensionality. In this work, we introduce InfoSEDD, a discrete diffusion–based approach that bridges information-theoretic estimation and generative modeling such that they can be used to compute Kullback–Leibler divergences. Backed by Continuous Time Markov Chains theory principles, the design of InfoSEDD is lightweight and scalable and allows seamless integration with pretrained models. 
We showcase the versatility of our approach through applications on motif discovery in genetic promoter data, semantic-aware model selection in text summarization, and entropy estimation in Ising models.
Finally, we construct consistency tests on real-world textual and genomics data. Our experiments demonstrate that InfoSEDD outperforms alternatives that rely on the ''embedding trick''. Our results position InfoSEDD as a robust and scalable tool for information-theoretic analysis of discrete data.
We provide the open source code at https://github.com/AlbertoForesti/mutinfo-diffusion.