NeurIPS 2026 Papers

Reconstructing the complete geometry of a scene from a single RGB image remains challenging—especially when inferring hidden structures where visual evidence is incomplete. We introduce VolFill, a generative framework that predicts the 3D structure of the complete scene rather than relying on traditional pixel-aligned regression. Our method utilizes a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into a compact latent space, paired with a latent Diffusion Transformer that denoises this representation to recover the complete scene. We condition the generation on geometry foundation models, leveraging rich spatial priors for robust reasoning. Unlike existing methods limited by per-ray constraints or unstructured point-cloud queries, VolFill provides a structured representation that supports direct surface extraction and occupancy queries at scale. Extensive experiments on the 3D-FRONT and NRGBD datasets demonstrate that our approach significantly outperforms current baselines, providing a robust foundation for holistic spatial understanding.

Accurate contact modeling is fundamental to understanding hand–object interaction, yet existing contact representations are typically restricted to object surfaces and rely on hand‑crafted rules to recover contact details, leading to severe penetrations and implausible results. To better exploit the rich detail in motion‑capture data, we introduce Volumetric Contact (VolCo), a representation that expands surface points to a set of 3D volumetric grids. VolCo encodes 3D contact that allows precise hand part recovery, and is organized in an inherent hierarchy: local contact details within each volume and global hand geometry across all volumes. Our framework, VolCoDiff, employs two modules to capture local and global features following this hierarchy. For local contact details, we use a 3D variational autoencoder to model the possible hand configurations conditioned on the local object signed distance field (SDF). For global hand geometry, we design a prior‑guided diffusion model that learns the distribution of compressed latent features aggregated from the volumetric grids. We evaluate our method on two benchmark datasets and demonstrate state‑of‑the‑art performance in penetration and stability, indicating the capability to generate tight grasps with much less severe penetrations.

Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completing a recording, dubbing in a new language, or preserving the voices of individuals with speech loss. However, in our work, we find that despite the term, voice cloning does not faithfully "clone" an individual's voice. Instead, we find that widely-used voice cloning models systematically apply style transfer to source voices. As rated by human annotators, cloned voices are perceived as more authoritative, warm, customer-service-like, and human-like compared to their sources. Human annotators also report greater trust in cloned voices than source voices, and a greater willingness to disclose sensitive personal information to them. Our work furthermore shows that voice cloning leads to homogenization of speaker characteristics, as measured by reduced variance in accent, speaking rate, and the audio embedding space. Together, our results highlight a new set of limitations and risks of voice cloning technology and their potential impact on human behavior.

Recent audio-language models are increasingly evaluated on recognizing, describing, and reasoning about audio, but expert-facing applications require a different capability: producing feedback that identifies problems and suggests corrective actions grounded in the input. We introduce VocalCoachBench, a benchmark for evaluating audio-language models on expert vocal coaching feedback for singing. VocalCoachBench contains 515 recordings annotated by 18 professional vocal trainers, yielding 1,056 expert submissions and 12,051 atomic coaching claims. It comprises a same-song subset for controlled comparison and a diverse-song subset for segment-grounded feedback across varied songs and recording conditions. To accommodate the open-ended nature of expert feedback, VocalCoachBench separates deterministic structured targets from claim-based assessment of free-form diagnosis and corrective guidance. Human annotation analysis shows that expert agreement varies strongly with label granularity, motivating hierarchical structured metrics and claim-based evaluation of open-ended feedback. Experiments with 12 recent audio-language models reveal a consistent gap: while models can compare performances and identify broad issue domains in free-form feedback, Top-3 fine-grained issue-label identification remains below label-prior baselines and strict diagnosis alignment stays below 7%. To our knowledge, VocalCoachBench provides the first public testbed for evaluating audio-grounded expert feedback for singing, moving audio-language evaluation beyond description toward analytic feedback.

A fundamental limitation of Text-to-Code is that no guarantee can be obtained about the correctness of the generated code. Therefore, to ensure its correctness, the generated code still has to be reviewed, tested, and maintained by developers. However, parsing through LLM-generated code can be tedious and time-consuming, potentially negating the productivity gains promised by AI-coding tools. To address this challenge, we present Viverra, a system that automatically produces *formally verified annotations* alongside generated code to aid user's understanding of the generated program. Given a natural-language task description, Viverra prompts an LLM to synthesize a C program together with candidate assertions expressing safety and correctness properties. It then verifies those assertions in a compositional and best-effort manner via a portfolio of bounded model checkers. Evaluation on 18 diverse programming tasks suggests that Viverra can efficiently generate code with verified assertions, and that these assertions improve users' performance on code-comprehension tasks in a user study with more than 400 participants.

Procedural generation is well-suited for modeling 3D buildings, as architectural structures are naturally composed of repetitive and parameterized components. It provides a compact and editable representation by encoding geometry through executable rules. However, constructing such procedural representations typically relies on manually designed grammars, making them difficult to obtain in practice. We study image-based procedural facade reconstruction, where the goal is to recover an editable building procedural representation from an in-the-wild building image. We propose a hierarchical neuro-symbolic framework that decomposes the reconstruction into two coupled levels: a global structure layout tree for split-and-repeat facade organization, and a component parameterization representation for local architectural. A fine-tuned layout model recovers and refines the global structure through rendered visual feedback, while a tile-level predictor maps representative component crops to structured procedural attributes. The two representations are then deterministically compiled into executable CGA programs. Experiments show that our method achieves strong performance on both regular facades and challenging in-the-wild images, while maintaining high structural consistency and procedural executability. In addition, the tile-level predictor improves fine-grained architectural detail, enabling accurate and controllable component synthesis.

Diffusion-based multimodal large language models (dMLLMs) decode by iteratively predicting tokens at multiple masked positions in parallel. This turns each decoding step into a position-selection problem: the model must choose not only which predictions are reliable in isolation, but also which positions should be committed together as context for later decoding steps. Existing confidence-based decoding ranks masked positions independently and commits the top-$K$ positions, largely ignoring whether the committed tokens provide complementary visual grounding. We identify a step-level limitation of this strategy in multimodal settings: high-confidence tokens selected in the same step can rely on overlapping visual grounding, introducing visual redundancy among the committed tokens and leaving less complementary visual grounding available for later decoding. To quantify this effect, we introduce the Visual Redundancy Index (VRI), which measures visual grounding overlap among tokens committed in parallel. To control this redundancy during decoding, we propose Visual-Redundancy-Controlled Decoding (VRCD), a training-free inference-time decoding method that uses token-to-image attention to prioritize visually complementary positions. Across diverse multimodal benchmarks, VRCD reduces visual redundancy and remaining-position entropy with modest runtime overhead. In longer decoding experiments, it also achieves relative accuracy gains of up to $18.8\\%$ on M$^3$CoT and $6.9\\%$ on MMBench over confidence-based decoding.Code is included in the supplement.

Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fidelity. While recent Large Vision Language Models (LVLMs) achieve strong results via supervised fine-tuning, reinforcement learning remains challenging due to misaligned reward signals. Existing rewards either rely on textual rules or coarse visual embedding similarity, both of which fail to capture fine-grained visual discrepancies and are vulnerable to reward hacking. We propose Visual Equivalence Reward Model (Visual-ERM), a multimodal generative reward model that provides fine-grained, interpretable, and task-agnostic feedback to evaluate vision-to-code quality directly in the rendered visual space. Integrated into RL, Visual-ERM improves Qwen3-VL-8B-Instruct by +8.4 on chart-to-code and yields consistent gains on table and SVG parsing (+2.7, +4.1 on average), and further strengthens test-time scaling via reflection and revision. We also introduce VisualCritic-RewardBench (VC-RewardBench), a benchmark for judging fine-grained image-to-image discrepancies on structured visual data, where Visual-ERM at 8B decisively outperforms Qwen3-VL-235B-Instruct and approaches leading closed-source models. Our results suggest that fine-grained visual reward supervision is both necessary and sufficient for vision-to-code RL, regardless of task specificity.

Visual text compression (VTC) promises efficient long-context processing by rendering text into an image and re-encoding it with a vision-language model, often producing $3$--$20\times$ fewer decoder tokens than subword tokenization. Yet token savings do not translate predictably into downstream utility: on some tasks the visual path matches or exceeds the text path, on others it collapses, and the compression ratio itself does not predict which regime will occur. The missing quantity is therefore not another summary of efficiency, but a principled measure of task-relevant information loss induced by visual encoding. We address this problem by formulating VTC in the language of measure transport. Treating text and visual tokens as empirical probability measures, we show that the ViT patch encoder induces a push-forward map whose transport cost decomposes into a precision cost from within-patch aggregation and a coverage cost from cross-patch fragmentation. Both terms are estimable from downstream-label-free probes. This formulation yields two operational consequences: a downstream-label-free routing criterion that selects whether to use the visual path for a given input or benchmark instance, and a transport-informed foveation mechanism that re-encodes high-cost regions at higher resolution. Across $24$ NLP datasets at Qwen3-4B, our label-free rule matches the per-dataset oracle on $17/24$ datasets ($70.8\%$), and improves the average task score by $+3.3\%$ with $-10.3\%$ average tokens relative to a pure-LLM.

Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environmental changes, maintaining stable performance even under degraded visual input with minimal exposure. Inspired by this gap, we established the Mouse vs. AI: Robust Foraging Competition, a novel bioinspired visual robustness and neural alignment benchmark in which agents and mice perform the same naturalistic 3D foraging task. The benchmark consists of two tracks: (1) a robustness track evaluating generalization to unseen visual perturbations, and (2) a neural alignment track measuring how well model representations predict large-scale neural recordings from mouse visual cortex. We provide a public benchmark suite comprising a Unity-based environment for training and evaluating virtual agents, centralized evaluation protocols, and an extensive neural dataset with approximately 178 minutes of multimodal recordings of behavior, visual input, and neural activity from more than 50,000 neurons in mice performing the task. As part of the NeurIPS competition, we received submissions from 22 teams proposing diverse architectures and training strategies. By embedding evaluation in a shared sensorimotor task performed by both biological and artificial agents, the benchmark enables a class of evaluative claims not supported by existing robustness or neural alignment benchmarks in isolation: namely, whether robust, brain-like visual representations emerge spontaneously from behavior-driven learning, and whether robustness and neural alignment reinforce or trade off against one another. Access to the dataset as well as the benchmarking infrastructure will remain online at https://robustforaging.github.io.

Multimodal large language models (MLLMs) have made significant strides in natural vision-language understanding, yet their potential in specific domains, such as healthcare, remains largely untapped. Since existing medical models often lack the reasoning capabilities needed for complex decision-making, adapting general MLLMs for medical reasoning has attracted increasing interest, with reinforcement fine-tuning (RFT) emerging as a promising approach. However, medical reasoning tasks require both precise thinking processes and generalization of well-justified answers, presenting unique challenges due to the inherent scarcity of annotated data and nuanced visual complexity of medical images. Current visual RFT methods prioritize answer correctness through verifiable rewards while neglecting the reasoning process, leading to limited reasoning capabilities and sub-optimal performance, which are essential in high-stakes scenarios like healthcare. To address these issues, we propose MedR2FT, a Medical Reasoning Reinforcement Fine-Tuning framework that enhances medical reasoning through rationale bootstrapping and reasoning rewarding. Specifically, MedR2FT leverages distilled rationales from fundamental training phases to supervise subsequent reasoning processes with semantic rewards, alleviating the challenge of scarce reasoning data and enhancing model reasoning. Extensive experiments demonstrate that our method consistently outperforms baselines on visual question answering and lesion detection with a significant margin. MedR2FT explicitly attends to the reasoning process, significantly enhancing MLLM adaptability for medical reasoning. The code will be available.

Multimodal agents struggle with physical-world reasoning, particularly spatial reasoning from partial views, as the physical world is inherently complex. We think multimodal agentic reasoning about the physical world should ground its actions in deterministic feedback, rather than rely solely on internal imagination. Concretely, we propose to equip vision–language models (VLMs) with a physics engine (e.g., MuJoCo, UE5), which simulates the consequences of the agent's actions. However, granting the agent raw access to a physics engine is not sufficient, as the agent must first reconstruct the scene inside the physics engine. We therefore introduce the Visual Harness System, an orchestration system that decouples perception from reasoning with a physics engine as the backend. The perception module composes off-the-shelf perception skills, such as camera-pose estimation, open-vocabulary detection, and metric depth, into a structured scene map. The reasoning module then issues multi-turn tool calls that the engine executes over the map, and composes the final answer from the grounded feedback it receives. Experiments on spatial reasoning benchmarks show that our approach consistently outperforms strong baselines across both open- and closed-weight VLMs, with up to $16.57\%$ improvement over prior methods on the MindCube benchmark.

LLMs have shown strong in-context learning (ICL) capability, but extending it to Multimodal LLMs (MLLMs) remains challenging. Prior multimodal ICL methods often rely on ICL-specific datasets for additional training or task vector extraction, which can improve performance on the same ICL benchmark used for adaptation. However, they do not generalize well to other benchmarks and often induce forgetting in previously well-solved tasks. For this reason, we identify `visual grounding' as the key bottleneck in multimodal ICL; MLLMs often fail to attend to task-relevant visual evidence in demonstrations, instead over-relying on textual cues and producing hallucinated outputs. To address this, we propose ProCoRe to enhance visual grounding through contrastive reinforcement learning on multimodal contrastive data for improving multimodal ICL. ProCoRe generates captions for contrastive image pairs, decomposes them into propositions, and optimizes contrastive rewards that encourage alignment with corresponding images while discouraging mismatched ones. Despite never seeing ICL-formatted training examples, ProCoRe improves average multimodal ICL classification accuracy by 5.6% and captioning ROUGE-L by 13.8% over the off-the-shelf Qwen-VL-3-8B model, outperforming ICL-data-dependent state of the arts across benchmarks. Finally, we introduce PICL, a personalized multimodal ICL captioning benchmark that tests whether MLLMs genuinely understand visual demonstrations rather than relying on pre-trained semantic priors.

Multimodal latent reasoning has emerged as a promising paradigm that replaces explicit Chain-of-Thought (CoT) decoding with implicit feature propagation, simultaneously enhancing representation informativeness and reducing inference latency. By analyzing token-level gradient dynamics during latent training, we reveal two critical observations: (1) visual tokens exhibit significantly smaller gradient norms than their textual counterparts due to inherent language bias, resulting in systematic visual under-optimization; and (2) semantically simple tokens converge rapidly, whereas complex tokens exhibit persistent gradient instability constrained by fixed architectural depths. To address these limitations, we propose a visual replay module and routing depth scaling to collaboratively enhance visual perception and refine complicated latents for deeper contextual reasoning. The former module leverages causal self-attention to estimate token saliency, reinforcing fine-grained grounding through spatially-coherent constraints. Complementarily, the latter mechanism adaptively allocates additional reasoning steps to complex tokens, enabling deeper contextual refinement. Guided by a curriculum strategy that progressively internalizes explicit CoT into compact latent representations, our framework achieves state-of-the-art performance across diverse benchmarks while delivering substantial inference speedups over explicit CoT baselines.

Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimodal reasoning, yet current multimodal large language model (MLLM) evaluations do not explicitly enforce this alignment. Existing benchmarks assess either textual answer correctness or pixel-level localization in isolation, leaving the coupling of reasoning and grounding an open challenge. We introduce VistaQA, a comprehensive benchmark for joint evaluation of free-form answer correctness and pixel-level evidence grounding in visual question answering. VistaQA comprises 1,157 expert-curated samples spanning six task types and six visual domains, ranging from direct perception to compositional and relational reasoning. VistaQA requires models to not only answer correctly, but to also provide precise segmentation masks that support their answers. It also includes hallucination-aware examples where no valid visual evidence exists. To support this enhanced evaluation, we introduce GROVE, a unified evaluation metric that enforces joint correctness by combining textual accuracy and grounding quality via a per-sample geometric mean, ensuring neither dimension can compensate for deficiencies in the other. Comprehensive experiments across grounding-aware models and hybrid pipelines with general-purpose MLLMs reveal that even the strongest systems achieve limited performance under GROVE, highlighting a substantial gap between answer accuracy and visual evidence alignment.

Native visual-generation agents now handle long-horizon, multi-image workflows, but face two practical bottlenecks during reinforcement learning (RL) training. First, the inability to accumulate experience across rollouts prevents the agent from leveraging past successes and failures, slowing the evolution of its agent behaviors. Second, the reliance on black-box visual-generation tools introduces tool-induced reward noise, as the policy is unfairly penalized for failures rooted in a tool's inherent capability boundary, leading to unstable and inefficient training. To address these challenges, we propose VisionCreator-S1, an agent that casts skill-augmented RL as a nested optimization problem through two coupled innovations. First, Dual-Skills co-evolves an agent-skill set to reuse self-reflection patterns from historical rollouts, and a tool-skill set that dynamically maps each tool's capability boundary through world-reflection. Second, Skill-GRPO treats discrete text skills as learnable variables. To overcome the non-differentiability of text skill, it gracefully alternates between numerical gradient descent for the policy weights and textual gradient descent for the discrete skill sets. By contrasting high- and low-advantage trajectories, it approximates a direction-consistent textual gradient in the agent's behavior feature space. Extensive experiments on VCR-Bench, GEdit-Bench, and MultiBanana demonstrate that VisionCreator-S1 consistently outperforms the strongest skill-free baseline, VisionCreator-R1. Ablation studies further confirm that this aligned co-evolution significantly improves both training stability and the agent's long-term decision-making capabilities.

Visual content generation has moved from single-image to multi-image workflows, yet existing agents are largely plan-driven and lack a systematic reflection mechanism for correcting mid-trajectory visual errors. To address this gap, we propose VisionCreator-R1, a native visual generation agent with explicit reflection, paired with a Reflection-Plan Co-Optimization (RPCO) training methodology. Through extensive experiments and trajectory-level analysis, we uncover a reflection-plan optimization asymmetry in reinforcement learning (RL): planning can be reliably optimized via plan rewards, while reflection learning is held back by noisy credit assignment. Motivated by this finding, we further abstract a general Decouple-then-Fuse paradigm for co-optimizing capabilities with asymmetric reward variance, of which RPCO is the visual-generation instantiation. Our RPCO first trains on the self-constructed VCR-SFT dataset, which covers reflection-strong single-image trajectories and planning-strong multi-image trajectories, and then co-optimizes on the VCR-RL dataset via RL. This yields our unified VisionCreator-R1 agent, which beats Gemini2.5Pro on existing benchmarks and on our VCR-Bench covering single-image and multi-image tasks.

Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We observe a regional-to-global perception gap: the same MLLM answers fine-grained questions more accurately when conditioned on evidence-centered crops than on the corresponding full images, suggesting that many failures stem from difficulty to focus on relevant evidence rather than insufficient local recognition ability. Motivated by this observation, we propose Vision-OPD (Vision On-Policy Distillation), a regional-to-global self-distillation framework that transfers the model's own privileged regional perception to its full-image policy. Vision-OPD instantiates two conditional policies from the same MLLM: a crop-conditioned teacher and a full-image-conditioned student. The student generates on-policy rollouts, and Vision-OPD minimizes token-level divergence between the teacher and student next-token distributions along these rollouts. This enables the model to internalize the benefit of visual zooming without external teacher models, ground-truth labels, reward verifiers, or inference-time tool use. Experiments on multiple fine-grained visual understanding benchmarks show that Vision-OPD models achieve competitive or superior performance against much larger open-source, closed-source, and "Thinking-with-Images" agentic models.

Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region. This setup assumes that the relevant linguistic unit is already known, overlooking a more basic challenge in grounded communication: determining which parts of the text are visually referential and how they correspond to entities in the image. We formulate grounding as bidirectional concept correspondence over an image-text pair. Given an image and its paired text, the goal is to recover all correspondences between visually referential text spans and instance-level image segments, without assuming that the relevant text spans are provided. This formulation unifies common grounding tasks, including phrase grounding, referring expression grounding, and open-vocabulary detection, by treating text segmentation, image segmentation, and cross-modal alignment as a single correspondence prediction problem. To address this task, we introduce our model, a grounding model built on top of a pretrained vision-language model. It uses learnable bridge tokens to represent candidate image-text correspondences and predicts, for each token, a text mask, an image mask, and a correspondence presence score. To train and evaluate this task, we convert diverse grounding and segmentation datasets into a unified correspondence format. Experiments show that our model consistently outperforms baselines, improving correspondence F1 by 59% on the long-caption dataset and by 73% on zero-shot LVIS, where the large category list serves as the text input.