ICLR 2026 Papers

3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits practicality. To address these challenges, we propose \textbf{Nano3D}, a training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view renderings, and further introduces region-aware merging strategies, Voxel/Slat-Merge, which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves superior 3D consistency and visual quality compared with existing methods. Based on this framework, we construct the first large-scale 3D editing datasets \textbf{Nano3D-Edit-100k}, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models.

Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. This task is termed NFV-RA. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this combinatorial complexity of constrained cross-graph mapping. However, RL-driven NFV-RA research lacks a systematic benchmark for comprehensive simulation and rigorous evaluation. This gap hinders in-depth performance analysis and slows algorithm development for emerging networks, resulting in fragmented assessments. In this paper, we introduce Virne, a comprehensive benchmarking framework designed to accelerate the research and application of deep RL for NFV-RA. Virne provides customizable simulations for diverse network scenarios, including cloud, edge, and 5G environments. It features a modular and extensible implementation pipeline that integrates over 30 methods of various types. Virne also establishes a rigorous evaluation protocol that extends beyond online effectiveness to include practical perspectives such as solvability, generalizability, and scalability. Furthermore, we conduct in-depth analysis through extensive experiments to provide valuable insights into performance trade-offs for efficient implementation and offer actionable guidance for future research directions. Overall, with its capabilities of diverse simulations, rich implementations, and thorough evaluation, Virne could serve as a comprehensive benchmark for advancing NFV-RA methods and deep RL applications. The code and resources are available at https://anonymous.4open.science/r/anonymous-virne.

Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field. However, existing benchmarks fail to differentiate question difficulty, limiting their ability to effectively distinguish models' capabilities. To address this limitation, we propose RankLLM, a novel framework designed to quantify both question difficulty and model competency. RankLLM introduces difficulty as the primary criterion for differentiation, enabling a more fine-grained evaluation of LLM capabilities. RankLLM's core mechanism facilitates bidirectional score propagation between models and questions. The core intuition of RankLLM is that a model earns a competency score when it correctly answers a question, while a question's difficulty score increases when it challenges a model. Using this framework, we evaluate 30 models on 35,550 questions across multiple domains. RankLLM achieves 90\% agreement with human judgments and consistently outperforms strong baselines such as IRT. It also exhibits strong stability, fast convergence, and high computational efficiency, making it a practical solution for large-scale, difficulty-aware LLM evaluation.

Building Behavioral Foundation Models (BFMs) for humanoid robots has the potential to unify diverse control tasks under a single, promptable generalist policy. However, existing approaches are either exclusively deployed on simulated humanoid characters, or specialized to specific tasks such as tracking. We propose BFM-Zero, a framework that learns an effective shared latent representation that embeds motions, goals, and rewards into a common space, enabling a single policy to be prompted for multiple downstream tasks without retraining. This well-structured latent space in BFM-Zero enables versatile and robust whole-body skills on a Unitree G1 humanoid in the real world, via diverse inference methods, including zero-shot motion tracking, goal reaching, and reward inference, and few-shot optimization-based adaptation. Unlike prior on-policy reinforcement learning (RL) frameworks, BFM-Zero builds upon recent advancements in unsupervised RL and Forward-Backward (FB) models, which offer an objective-centric, explainable, and smooth latent representation of whole-body motions. We further extend BFM-Zero with critical reward shaping, domain randomization, and history-dependent asymmetric learning to bridge the sim-to-real gap. Those key design choices are quantitatively ablated in simulation. A first-of-its-kind model, BFM-Zero establishes a step toward scalable, promptable behavioral foundation models for whole-body humanoid control. Videos: http://bfm-zero-anonymous.pages.dev/

We introduce an approach that reconstructs a closed surface mesh from a noisy point cloud, where the topology of surface is guaranteed to be simply connected, i.e., homeomorphic to a topological 2-sphere. This task enjoys a wide range of applications, e.g., 3D organ and vessel reconstruction from CT scans. Central to our approach is a robust module that takes a collection of oriented triangles in a 3D triangulation as input and outputs a simply connected volumetric mesh whose boundary approximates the input triangles. Starting from a 3D Delaunay triangulation of the input point cloud and initial triangle orientations obtained through a spectral approach, our approach alternates between applying the module to obtain a reconstruction and using that reconstruction to reorient the input triangles. Experimental results on real and synthetic datasets demonstrate the effectiveness of our approach.

This paper presents a theoretical analysis of two of the most impactful interventions in modern learning from demonstration in robotics and continuous control: the practice of *action-chunking* (predicting sequences of actions in open-loop) and *exploratory augmentation* of expert demonstrations. Though recent results show that learning from demonstration, also known as imitation learning (IL), can suffer errors that compound *exponentially* with task horizon in continuous settings, we demonstrate that action chunking and exploratory data collection circumvent exponential compounding errors in different regimes. Our results identify control-theoretic stability as the key mechanism underlying the benefits of these interventions. On the empirical side, we validate our predictions and the role of control-theoretic stability through experimentation on popular robot learning benchmarks. On the theoretical side, we demonstrate that the control-theoretic lens provides fine-grained insights into how compounding error arises, leading to tighter statistical guarantees on imitation learning error when these interventions are applied than previous techniques based on information-theoretic considerations alone.

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a simple Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and module scaling experiments further highlight MoMa's potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration.

Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context, models explore reasoning trajectories and exploit rollouts with correct answers as positive signals for policy optimization. However, these rollouts might involve flawed patterns such as answer-guessing and jump-in-reasoning. Such flawed-positive rollouts are rewarded identically to fully correct ones, causing policy models to internalize these unreliable reasoning patterns. In this work, we first conduct a systematic study of flawed-positive rollouts in RL and find that they enable rapid capability gains during the early optimization stage, while constraining reasoning capability later by reinforcing unreliable patterns. Building on these insights, we propose **F**lawed-**A**ware **P**olicy **O**ptimization (**FAPO**), which presents a parameter-free reward penalty for flawed-positive rollouts, enabling the policy to leverage them as useful shortcuts in the warm-up stage, securing stable early gains, while gradually shifting optimization toward reliable reasoning in the later refinement stage. To accurately and comprehensively detect flawed-positive rollouts, we introduce a generative reward model (GenRM) with a process-level reward that precisely localizes reasoning errors. Experiments show that FAPO is effective in broad domains, improving outcome correctness, process reliability, and training stability without increasing the token budget.

We introduce MoSA, a new parameter-efficient fine-tuning (PEFT) method that replaces low-rank factorization with randomized, fine-grained sharing of weight updates. Each adapted weight matrix is constructed by broadcasting a small set of learned scalars over a fixed tessellation, a pre-defined group assignment of weight entries of the weight matrix, producing expressive changes under the same parameter budget as low-rank adaptation (LoRA). MoSA requires no architectural changes and can be merged into the base model for zero-overhead inference. Across diverse language understanding and generation tasks, MoSA matches or surpasses strong PEFT baselines under strictly matched budgets. Analyses and ablations indicate that non-local parameter sharing acts as an effective regularizer, and that grouping design and budget allocation govern the expressivity–efficiency trade-off. These results position MoSA as a simple, scalable alternative to LoRA.

Deep neural networks are known to often memorize underrepresented, hard examples, with implications for generalization and privacy. Feldman & Zhang (2020) defined a rigorous notion of memorization. However it is prohibitively expensive to compute at scale because it requires training models both with and without the data point of interest in order to calculate the memorization score. We observe that samples that are less memorized tend to be learned earlier in training, whereas highly memorized samples are learned later. Motivated by this observation, we introduce Cumulative Sample Gradient (CSG), a computationally efficient proxy for memorization. CSG is the gradient of the loss with respect to input samples, accumulated over the course of training. The advantage of using input gradients is that per-sample gradients can be obtained with negligible overhead during training. The accumulation over training also reduces per-epoch variance and enables a formal link to memorization. Theoretically, we show that CSG is bounded by memorization and by learning time. Tracking these gradients during training reveals a characteristic rise–peak–decline trajectory whose timing is mirrored by the model’s weight norm. This yields an early-stopping criterion that does not require a validation set: stop at the peak of the weight norm. This early stopping also enables our memorization proxy, CSG, to be up to five orders of magnitude more efficient than the memorization score from Feldman & Zhang (2020). It is also approximately 140 $\times$ and 10$\times$ faster than the prior state-of-the-art memorization proxies, input curvature and cumulative sample loss, while still aligning closely with the memorization score, exhibiting high correlation. Further, we develop Sample Gradient Assisted Loss (SGAL), a proxy that further improves alignment with memorization and is highly efficient to compute. Finally, we show that CSG attains state-of-the-art performance on practical dataset diagnostics, such as mislabeled-sample detection and enables bias discovery, providing a theoretically grounded toolbox for studying memorization in deep networks.

Behavioral Foundation Models (BFMs) have been recently successful in producing agents with the capabilities to adapt to any unknown reward or task. In reality, these methods are only able to produce near-optimal policies for the reward functions that are in the span of some pre-existing _state features_. Naturally, their efficiency relies heavily on the choice of state features that they use. As a result, these BFMs have used a wide variety of complex objectives, often sensitive to environment coverage, to train task spanning features with different inductive properties. With this work, our aim is to examine the question: are these complex representation learning objectives necessary for zero-shot RL? Specifically, we revisit the objective of self-supervised next-state prediction in latent space for state feature learning, but observe that such an objective alone is prone to increasing state-feature similarity, and subsequently reducing span of reward functions that we can represent optimal policies for. We propose an approach, RLDP, that adds a simple regularization to maintain feature diversity and can match or surpass state-of-the-art complex representation learning methods for zero-shot RL. Furthermore, we demonstrate the prior approaches diverge in low-coverage scenarios where RLDP still succeeds.

Human-aligned deep learning models exhibit behaviors consistent with human values, such as robustness, safety, and fairness. Transferring these behavioral properties to models trained on different tasks or data distributions remains challenging: aligned behavior is easily forgotten during fine-tuning, and collecting task-specific data that preserves this behavior can be prohibitively costly. We introduce BIRD, a flexible framework for transferring aligned behavior by matching the internal representation structure of a student model to that of a teacher. Applied to out-of-distribution robustness in image classification, BIRD outperforms fine-tuning, transfer learning, and continual learning methods, improving robust accuracy by up to 18\% over the next strongest baseline. It remains effective even when the teacher is trained on a much simpler dataset and is $25\times$ smaller in parameter count than the student. In a large-scale study of over 400 teacher-student pairs, we show that three interpretable and computable properties of the teacher's representations explain up to 85\% of the variance in transfer success, offering practical guidance for teacher selection and design. We further show that BIRD generalizes beyond applications in vision by enhancing safety alignment in language models when paired with Direct Preference Optimization and improving weak-to-strong generalization when combined with soft-label distillation. BIRD turns small, well-aligned models into scalable alignment seeds, mitigating challenges from key bottlenecks in deploying safe AI systems.

With the rapid development of large language models (LLMs), identifying efficient strategies for training such large-scale systems has become increasingly critical. Although LLMs have achieved remarkable success across diverse applications, the necessity of maintaining full dense matrices during pre-training has been questioned, giving rise to parameter-efficient sparse pre-training methods which retains parameter-efficiency in both training and inference. These methods can be further divided into connectivity sparse training and spectral sparse training, with dynamic connectivity sparse training and low-rank factorization emerging as representative approaches for the two branches. However, a unified framework that effectively combines the strengths of both has yet to be established. In this work, we observe that the cancellation effect between the sparse and low-rank branches may limit the expressivity of the model, manifesting as output conflicts when the two components are combined. To address this issue, we propose a novel scheme that integrates dynamic sparse training with low-rank training, introducing a simple yet effective $\textbf{alignment loss}$ to mitigate the disagreement between the two branches and promote better collaboration. We validate this scheme by combining a representative dynamic sparse training method, CHTs, with low-rank training, resulting in a new parameter-efficient training approach termed $\textbf{CHTsL}$. The method is evaluated on LLaMA60M and LLaMA130M using the OpenWebText and C4 datasets, where only 10\%, 20\%, and 30\% of the parameters are preserved compared to dense training. Experimental results demonstrate that our proposed scheme effectively alleviates the cancellation effect and improves training stability and performance compared to the naive combination of sparse and low-rank components. Also, the new scheme enables CHTsL to consistently outperform other parameter-efficient sparse training methods under the same parameter budget, achieving performance most close to dense training.

AutoRegressive Visual Generation (ARVG) models retain an architecture compatible with language models, while achieving performance comparable to diffusion-based models. Quantization is commonly employed in neural networks to reduce model size and computational latency. However, applying quantization to ARVG remains largely underexplored, and existing quantization methods fail to generalize effectively to ARVG models. In this paper, we explore this issue and identify three key challenges: (1) severe outliers at channel-wise level, (2) highly dynamic activations at token-wise level, and (3) mismatched distribution information at sample-wise level. To these ends, we propose PTQ4ARVG, a training-free post-training quantization (PTQ) framework consisting of: (1) Gain-Projected Scaling (GPS) mitigates the channel-wise outliers, which expands the quantization loss via a Taylor series to quantify the gain of scaling for activation-weight quantization, and derives the optimal scaling factor through differentiation. (2) Static Token-Wise Quantization (STWQ) leverages the inherent properties of ARVG, fixed token length and position-invariant distribution across samples, to address token-wise variance without incurring dynamic calibration overhead. (3) Distribution-Guided Calibration (DGC) selects samples that contribute most to distributional entropy, eliminating the sample-wise distribution mismatch. Extensive experiments show that PTQ4ARVG can effectively quantize the ARVG family models to 8-bit and 6-bit while maintaining competitive performance.

This paper introduces the Behavioral Ambivalence/Hesitancy (BAH) dataset collected for the Ambivalence/Hesitancy (A/H) recognition task in videos. In particular, this task involves recognizing conflicting emotions linked to A/H from question-and-answer videos captured for behavior analysis. The dataset contains videos from 224 subjects with different age, ethnicity collected across 9 Canadian provinces via webcam through our developed web platform. Each user answers to 7 questions that we designed to induce Ambivalence/Hesitancy. Each video captures the response for one question in the subject's environment totaling 1,118 videos for a total duration of 8.26 hours with 1.5 hours of A/H. BAH is a first and unique dataset for Ambivalence/Hesitancy recognition. Our behavioral team annotated timestamp segment where A/H occurs providing frame- and video-level annotation, in addition to used cues for annotation such as face, audio, body, and language. Video transcripts and their timestamps as well as per-frame cropped and aligned faces are also included. This work offers initial baselines for A/H recognition in videos at frame- and video-level with different analysis with single and multimodal setups. The data, code, and pretrained weights are publicly accessible.

Long-Tailed Recognition (LTR) remains a significant challenge in deep learning. While multi-expert architectures are a prominent paradigm, we argue that their efficacy is fundamentally limited by a series of deeply entangled problems at the levels of representation, policy, and optimization. These entanglements induce homogeneity collapse among experts, suboptimal dynamic adjustments, and unstable meta-learning. In this paper, we introduce GUIDE, a novel framework conceived from the philosophy of Hierarchical Disentanglement. We systematically address these issues at three distinct levels. First, we disentangle expert representations and decisions through competitive specialization objectives to foster genuine diversity. Second, we disentangle policy-making from ambiguous signals by using online uncertainty decomposition to guide a dynamic expert refinement module, enabling a differentiated response to model ignorance versus data ambiguity. Third, we disentangle the optimization of the main task and the meta-policy via a two-timescale update mechanism, ensuring stable convergence. Extensive experiments on five challenging LTR benchmarks, including ImageNet-LT, iNaturalist 2018, CIFAR-100-LT, CIFAR-10-LT and Places-LT, demonstrate that GUIDE establishes a new state of the art, validating the efficacy of our disentanglement approach. Code is available at Supplement.

Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent Vision-Language-Action (VLA) models. Yet, most evaluations of VLMs focus on single-view settings, leaving their ability to integrate multi-view information largely underexplored. At the same time, multi-camera setups are increasingly standard in robotic platforms, as they provide complementary perspectives to mitigate occlusion and depth ambiguity. Whether VLMs can effectively leverage such multi-view inputs for robotic reasoning therefore remains an open question. To bridge this gap, we introduce MV-RoboBench, a benchmark specifically designed to evaluate the multi-view spatial reasoning capabilities of VLMs in robotic manipulation. MV-RoboBench consists of 1.7k manually curated QA items across eight subtasks, divided into two primary categories: spatial understanding and robotic execution. We evaluate a diverse set of existing VLMs, including both open-source and closed-source models, along with enhanced versions augmented by Chain-of-Thought (CoT)-inspired enhancements. The results show that state-of-the-art models remain far below human performance, underscoring the substantial challenges VLMs face in multi-view robotic perception. Additionally, our analysis uncovers two key findings: (i) spatial intelligence and robotic task reasoning are correlated in multi-view robotic scenarios; and (ii) strong performance on existing general-purpose single-view spatial understanding benchmarks does not reliably translate to success in the robotic spatial tasks assessed by our benchmark. We release MV-RoboBench as an open resource to foster progress in spatially grounded VLMs and VLAs, providing a foundation for advancing embodied multi-view intelligence in robotics.

Open-Vocabulary Detectors (OVDs) excel in zero-shot benchmarks, but we observe a critical flaw in real-world deployment: a high rate of confident false positive predictions on images that do not contain any target objects (e.g., detecting a tractor in an image of a dog). This issue is masked by standard benchmarks like COCO and LVIS, as they rarely contain images without any of the target classes present. We identify vision-language fusion layers in early-fusion OVD architectures (e.g., Grounding DINO or LLMDet) as the root cause, and show how they distribute irrelevant class information across image features when no prompted object is present. To mitigate background false positives without costly retraining, we propose a simple, training-free method: appending attention sink tokens to the input prompt. We show that such sinks can redirect spurious attention and dramatically reduce background false positives. Our approach significantly improves the performance of all six early-fusion models tested (e.g., boosting AP on LVIS by more than 5x at a false positive rate of 0.01 for some models), making them practical for real-world applications where images without the object of interest are much more prevalent.

Ultrasound is a widely-used imaging modality critical to global healthcare, yet its interpretation remains challenging due to its varying image quality on operators, noises, and anatomical structures. Although large vision-language models (LVLMs) have demonstrated impressive multimodal capabilities across natural and medical domains, their performance on ultrasound remains largely unexplored. We introduce U2-BENCH, the first comprehensive benchmark to evaluate LVLMs on ultrasound understanding across classification, detection, regression, and text generation tasks. U2-BENCH aggregates 7,241 cases spanning 15 anatomical regions and defines 8 clinically inspired tasks, such as diagnosis, view recognition, lesion localization, clinical value estimation, and report generation, across 50 ultrasound application scenarios. We evaluate 23 state-of-the-art LVLMs, both open- and closed-source, general-purpose and medical-specific. Our results reveal strong performance on image-level classification, but persistent challenges in spatial reasoning and clinical language generation. U2-BENCH establishes a rigorous and unified testbed to assess and accelerate LVLM research in the uniquely multimodal domain of medical ultrasound imaging.