NeurIPS 2026 Papers

Referring segmentation grounds natural-language queries to pixel-level masks, but extending it to complex scenarios with multiple instances, cross-category groups, or open-ended target sets remains challenging. Previous Large Vision Language Model (LVLM)-based methods represent referred targets with one or more special tokens sequentially, treating multiple targets as separate outputs rather than a coherent set and offering little incentive to capture set-level properties such as completeness and mutual exclusivity. We reformulate open-ended referring segmentation as explicit set-level concept prediction and propose Set-Concept Segmentation (SetCon), which uses LVLM-generated natural-language concepts, instead of segmentation-specific tokens, as semantic conditions for joint mask-set decoding. A hierarchical semantic decomposition first predicts a shared set-level concept defining the target scope and then refines it into fine-grained concept groups aligned with target subsets. To support this, a two-stage annotation pipeline augments existing reasoning segmentation datasets with hierarchical semantic supervision (236k samples, 784k concept phrases). SetCon achieves state-of-the-art results on image benchmarks (+3.3 gIoU on gRefCOCO, +12.1 gIoU on MUSE), with margins that grow as the number of referred targets increases. The concept interface also transfers to video under a detect-and-track setting, yielding new state-of-the-art results on seven referring video benchmarks, including +10.9 J&F on MeViS and +12.4 J&F on Ref-SeCVOS. The code, model checkpoints, and dataset annotations will be released.

Origin-destination (OD) flow forecasting supports decisions about where and how many people move through a city. Most existing public OD benchmarks provide only a partial view of citywide mobility, covering a single transport service or at most two modes. With partial mode coverage, observed demand changes are difficult to separate into shifts across transport modes and changes in total travel. We introduce SeoulMMOD, a large-scale public benchmark for citywide multimodal mobility in Seoul. SeoulMMOD provides three years of hourly OD flows estimating total mobility for 6 urban travel modes across 25 districts and 426 sub-districts, together with travel-time, travel-distance, calendar, rainfall, and point-of-interest (POI) signals. Using SeoulMMOD, we benchmark 14 forecasting baselines under a common protocol and study spatial scaling, joint training versus training one model per mode, and cross-year generalization. Results show that several spatio-temporal models that work at district level do not scale to sub-district OD forecasting, and that naive joint multi-mode training does not consistently improve accuracy. Together, the dataset and benchmark establish a reproducible testbed for city-scale multi-mode OD forecasting across multiple years. Project page: https://anonymous.4open.science/r/SeoulMMOD

When an omnimodal large language model accepts a question whose textual premise contradicts what it actually sees or hears, does the failure lie in perception or in action? Recent omnimodal models are positioned as perception-grounded agents that jointly process video, audio, and text, yet a basic form of grounding remains untested: catching a textual claim that conflicts with the model's own sensory input. We introduce IMAVB, a curated 500-clip benchmark of long-form movies with a 2x2 design crossing target modality (vision, audio) and premise condition (standard, misleading), which lets us measure conflict detection separately from ordinary multimodal comprehension. Across eight open-source omnimodal LLMs and Gemini 3.1 Pro, we document a Representation-Action Gap: hidden states reliably encode premise-perception mismatches even when the same models almost never reject the false claim in their outputs. Behaviorally, models fall into two failure modes: under-rejection, in which they answer misleading questions as if the false premise were true; and over-rejection, in which they reject more often but also reject standard questions, sacrificing ordinary comprehension accuracy. The gap is modality-asymmetric (audio grounding underperforms vision) and prompt-resistant across seven variants. As an initial diagnostic intervention, a probe-guided logit adjustment (PGLA) re-injects the encoded mismatch signal into decoding and consistently improves rejection behavior. Together, these results suggest the bottleneck for omnimodal grounding lies in translation, not perception.

Diffusion Transformers (DiTs) achieve state-of-the-art video generation quality, but their substantial memory and computational footprints hinder edge deployment. Quantization can reduce these costs, yet existing methods often degrade video quality due to high activation variation and the difficulty of preserving semantic and temporal coherence. We propose SemanticDialect, which advances block-wise mixed-format quantization. In this framework, each block selects an optimal format (dialect) from a candidate set (formatbook), which is augmented with lookup tables that store quantization errors and quantized indices, enabling efficient per-block format selection and quantization with minimal online overhead. We further introduce attention-guided activation decomposition, which reduces quantization error via residual quantization, and semantic-aware dialect assignment (SeDA), which reduces cross-token quantization inconsistency by enforcing format uniformity among semantically correlated tokens. Experiments demonstrate that SemanticDialect outperforms prior quantization methods and block-wise formats (MXFP4, NVFP4) while approaching FP16 quality on Open-Sora 2.0. We also validate hardware deployability through RTL design and GPU kernel implementation.

Multimodal face anti-spoofing (FAS) improves spoof detection by combining complementary RGB, infrared, and depth cues, yet its generalization to unseen domains remains fragile. We revisit multimodal domain-generalized FAS from the perspective of representation freedom. In a controlled CLIP-based post-fusion baseline, increasing visual encoder depth does not monotonically improve cross-domain performance, suggesting that higher-capacity representations may introduce additional degrees of freedom that are not necessarily aligned with real-vs-spoof semantics. To inspect this effect, we introduce the Semantic Dominance Ratio (SDR), a diagnostic statistic that measures the relative proportion of feature variation along the text-defined real-vs-spoof semantic axis versus off-axis residual directions. Motivated by this perspective, we propose Semantic Freedom Bottleneck (SFB), which reparameterizes multimodal features around CLIP text geometry. SFB decomposes each representation into a semantic component along the real-vs-spoof axis and a bounded low-rank residual component for modality-specific evidence. The residual basis is regularized to be orthogonal to the semantic axis, encouraging the residual space to retain complementary modality-specific cues without duplicating the task-semantic direction. This yields a conservative multimodal constraint that preserves discriminative real/spoof semantics while limiting unconstrained non-semantic variation. Experiments on WMCA, CASIA-CeFA, PADISI, and CASIA-SURF under fixed-modality, missing-modality, and limited-source protocols demonstrate consistently strong cross-domain performance.

In the computer vision world, vision-language and self-supervised encoders represent two unique paradigms characterized by global language alignment and high-quality vision-only patch representations, respectively. Multimodal large language models (MLLMs) often use the former because of language alignment, despite the recent rise in vision-centric tasks resembling traditional dense vision tasks that would likely benefit from the high-quality patch representations of the latter. This raises the question whether self-supervised models could aid MLLMs in vision-centric tasks. To investigate this, we define the concept of semantic (in)consistency of patch tokens produced by language-aligned encoders and identify two sets of tokens, semantically consistent (SCTs) and semantically inconsistent (SITs). Through systematic probing via the removal of SCTs or SITs from the input of modern MLLMs, we show higher reliance on SCTs than SITs, as removal of the former induces larger performance drops. This demonstrates that MLLMs benefit from semantic consistency of vision tokens. With this in mind, we devise a simple yet effective extension of the MLLM training objective that combines traditional language modeling with semantic guidance via minimization of semantic inconsistency of vision tokens. This strategy, named Semantically-Guided Training (SGT), combines the signals of both loss terms to push the vision encoder towards providing semantically consistent tokens, while preserving language alignment. Through extensive evaluation, we show that SGT drastically outperforms all baselines and MLLMs with specialized vision encoders on all vision-centric benchmarks while keeping strong scores on text-centric benchmarks, thus setting state of the art when averaged across all tasks. Our results highlight the importance of combining language alignment and semantic consistency through the lens of MLLMs rather than independently from them and pave the way towards the design of better MLLM vision encoders.

Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment. It mainly consists of Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA). Specifically, CSR first adaptively extracts visual and acoustic representations by corresponding adapters to form a unified multimodal prefix with language in the frozen LLM embedding space. It then iteratively produces continuous discriminative semantic states through a token-efficient latent refinement process without decoding explicit text. Next, CSA simultaneously aligns the refined semantics with all modalities by enhancing the dominant spectral component of their kernel Gram matrix. This captures global nonlinear dependencies among all representations without relying on a predefined anchor modality. In addition, an instance-level spectral separation constraint preserves cross-sample discriminability and mitigates representation collapse. Extensive experiments on SIMS, MOSI, and MOSEI benchmarks demonstrate that SemMSA achieves state-of-the-art performance.

Vision-Language-Action (VLA) models offer a promising paradigm for end-to-end autonomous driving by connecting visual scene understanding, linguistic reasoning, and low-level trajectory generation. However, existing driving VLAs typically use language only as static commands or supervised rationales, while treating trajectory prediction as an open-loop generation problem. As a result, they lack an explicit mechanism for understanding the consequences of their own actions, which limits robustness under closed-loop distribution shift and makes reinforcement learning inefficient in long-tail scenarios. We propose SelfCritic-VLA, a self-evaluative VLA framework that unifies trajectory generation, language-grounded action critique, and trajectory refinement. Given a driving scene, the model first generates an initial trajectory, then produces a language-grounded critique that describes the potential safety, progress, and rule-compliance consequences of executing that trajectory, and finally refines the trajectory conditioned on this critique. To train this capability, we construct a large-scale counterfactual trajectory dataset with multi-source candidate trajectories and semantic critiques, and perform hybrid supervised fine-tuning for planning, action evaluation, and refinement. Building on this initialization, we introduce a self-critic reinforcement learning procedure based on GRPO, where critique-conditioned refinements are filtered by grounded driving rewards and distilled back into the base policy. Experiments on NAVSIM and Bench2Drive show that SelfCritic-VLA consistently improves both open-loop and closed-loop driving performance over strong baselines, demonstrating the effectiveness of language-guided self-refinement for driving policy optimization.

Multimodal LLMs often produce fluent yet unreliable reasoning, exhibiting weak step-to-step coherence and insufficient visual grounding, largely because existing alignment approaches supervise only the final answer while ignoring the reliability of the intermediate reasoning process. We introduce SR-MCR, a lightweight and label-free framework that aligns reasoning by exploiting intrinsic process signals derived directly from model outputs. Five self-referential cues—semantic alignment, lexical fidelity, non-redundancy, visual grounding, and step consistency—are integrated into a normalized, reliability-weighted reward that provides fine-grained process-level guidance. A critic-free GRPO objective, enhanced with a confidence-aware cooling mechanism, further stabilizes training and suppresses trivial or overly confident generations. Built on Qwen2.5-VL, SR-MCR improves both answer accuracy and reasoning coherence across a broad set of visual benchmarks; among open-source models of comparable size, SR-MCR-7B achieves state-of-the-art performance with an average accuracy of 81.4%. Ablation studies confirm the independent contributions of each reward term and the cooling module.

Developing generalist GUI agents capable of robust long-horizon execution remains a key challenge. While Reinforcement Learning (RL) offers a promising path for improvement, it is severely bottlenecked by the lack of reliable reward signals in open-ended environments. Ground-truth verification typically relies on expensive human annotation, which is prohibitively scarce at the scale required for training, while existing VLM-based judges frequently hallucinate success on superficial visual cues. This scarcity of trustworthy supervision fundamentally limits the scalability of current methods. In this work, we propose I-Judge, a novel framework for Self-Calibrated Reward Modeling. We leverage Inverse Dynamic Modeling (IDM) as a self-supervised training objective to learn the causal dynamics of GUI interactions from massive unlabeled trajectories, effectively bypassing the bottleneck of scarce human labels. We then introduce a runtime calibration mechanism that weights reward signals by the IDM's action consistency, filtering out spurious successes. Extensive end-to-end RL experiments on OSWorld, ScienceBoard and AndroidWorld demonstrate that our method significantly accelerates convergence and improves final agent performance. Notably, I-Judge demonstrates strong generalization to unseen domains. All the code, data and models will be made publicly available to foster further research.

Rotation invariance is a fundamental requirement across many computer vision tasks. Historically, this inductive bias has been encoded through hand-crafted rotation-invariant representations. These are compact, interpretable, and fast to compute, but they come at the cost of descriptive power. More recently, architectures achieve inductive bias through learned representations. These are highly descriptive and achieve strong empirical performance, at the cost of efficiency and interpretability. In this work, we propose an alternative at the intersection of both paradigms. We introduce the selective disk bispectrum (SDB), a complex-valued rotation-invariant vector that preserves all information about the image except its orientation. Our key theoretical contributions are the selective disk bispectrum, its inversion, its (reduced) spatial and computational complexities (compared to the full disk bispectrum), and its expectation and variance under noise. Furthermore, we propose a numerical SDB approximation and provide theoretical guarantees for its accuracy and rotation invariance. Empirically, we validate SDB's invariance and robustness to noise classification tasks. We test our reconstruction algorithm on multi-reference alignment of rotated images.

Medical visual question answering is a safety-critical decision problem in which abstaining on inputs the image cannot support matters as much as answer accuracy. Existing inference-time agentic methods address hallucination by adding a verification step, but they collapse the final decision at a single language-model judge that integrates all evidence in one prompt and is systematically biased toward declaring inputs answerable. We propose PICV (Parallel Independent Claim Verification), which reformulates selective medical VQA as independent claim verification with transparent aggregation: the question is decomposed into a small set of typed visual claims, each is verified by an isolated prompt with no access to the other verdicts, and the verdicts are combined by a deterministic rule rather than another model, yielding an answer/abstain decision together with a typed unanswerability reason. We evaluate PICV efficiently on an automated answerability benchmark we construct over two public radiology datasets via three reproducible corruption procedures, requiring neither heavy VLM judging nor human annotation at evaluation time. Across four VLM backbones, PICV consistently outperforms strong prompting and agentic baselines; ablations attribute the gains specifically to per-claim isolation and rule-based aggregation rather than to agentic decomposition alone.

Radar-camera depth estimation must turn an ultra-sparse, all-weather, metric radar signal into a dense per-pixel depth map. Existing methods --- concatenation, confidence-aware gating, sparse supervision, graph-based extraction --- combine radar and image features outside the backbone's sequence operator, and even cross-modal Mamba variants leave the selection mechanism itself unimodal. We argue that the selection mechanism is the right place for radar to enter. We introduce Radar-Modulated Selection (RMS), a minimal and principled way to inject radar into Mamba's selective scan: radar modulates the scan from within, adding zero-initialised perturbations to the step size $\boldsymbol{\Delta}$ and readout $\mathbf{C}$ while leaving the input projection $\mathbf{B}$ and state dynamics $\mathbf{A}$ image-only. The construction is exactly equivalent to a pretrained image-only Mamba at initialisation, ensuring radar only influences the model where it improves accuracy. Two further properties follow that out-of-scan fusion cannot offer: linear-cost cross-modal coupling at every recurrence step, and a natural fallback to the image-only backbone when radar is absent. We deploy RMS in a Multi-View Scan Pyramid (MVSP) that matches the fusion operator to radar's spatial reach at each scale. SemoDepth achieves state-of-the-art performance on nuScenes, reducing MAE by 34.0%, 29.9%, and 29.9% over the previous best at 0-50, 0-70, and 0-80 m, while attaining the lowest single-frame latency (26.8 ms). A further ablation shows that out-of-scan feature blending adds no accuracy on top of RMS, providing empirical validation that in-scan selection can replace out-of-scan fusion.

3D vision-language segmentation aims to segment target objects in 3D scenarios according to the linguistic instructions and visual observations. Prior art heavily relies on the coarse superpoint representation to reduce the computation complexity , which suffers from poor segmentation quality and messy object boundaries. In this paper, we propose the SEGment-And-select (SEGA3D) paradigm for 3D vision-language segmentation that directly operates on the fine-grained visual information and is free from the superpoint dependency. Specifically, we first leverage a mask candidate generator to provide fine-grained categorical mask candidates, substantially improving the quality of candidate masks over the superpoint counterparts. Then, a Large Language Model (LLM) is utilized to generate the semantic and spatial information based on the linguistic description and visual features. The LLM output and visual features are fed to the Semantic-Spatial Selector (SSS) to produce the top-ranking mask candidates. Eventually, the Loopback Verification Module (LVM) is designed to yield the segmentation mask from the selected candidate masks. Our SEGA3D attains competitive performance on ScanRefer, ScanNet and Matterport3D benchmarks. Notably, our SEGA3D surpasses the top-performing counterpart by 8.3 mIoU and 5.3 mIoU on ScanNet and Matterport3D, respectively. Codes will be available upon publication.

Egocentric video generation aims to synthesize first-person visual experiences, enabling applications in filmmaking, virtual reality, and embodied AI. Generating egocentric videos from exocentric observations is particularly challenging, as it requires reasoning across large viewpoint changes, limited visual overlap, and substantially different camera motions. To address these modeling challenges, we propose **EgoEye**, a framework that generates egocentric videos from a single exocentric input video. EgoEye integrates reward-guided egocentric context reasoning for inferring unseen first-person content, multi-stage motion alignment for enforcing cross-view temporal consistency, and egocentric pretraining for improving first-person realism. To support training and evaluation under diverse and distortion-free exo-ego settings, we further introduce **EgoScape**, a large-scale dataset comprising 19.5K time-aligned exo-ego video pairs and 24.2K in-the-wild egocentric videos. EgoScape provides paired cross-view supervision while enriching the first-person visual priors needed for realistic egocentric synthesis. Extensive experiments demonstrate that the proposed EgoEye generates realistic and temporally consistent egocentric videos across diverse scenarios.

3D multimodal language models are becoming a foundation for embodied agents, indoor robotics, and human–AI interaction, but their answers and explanations often remain weakly tied to the specific objects and spatial relations observed in a 3D scene. This paper aims to make 3D reasoning more faithful by reducing language-prior rationalization and forcing reasoning chains to depend on verifiable scene evidence. We propose Anti-Rationalization Chain-of-Thought (AR-CoT), a plug-in training and decoding framework that represents each reasoning step with an explicit evidence pointer and scores candidate answer-chain pairs by their scene-evidence gain. AR-CoT contrasts scene-conditioned rationales with a frozen text-only prior, verifies local object and relation claims, and uses decoy-twin scene pairs to encourage reasoning chains to change when the relevant spatial evidence changes. Experiments on standard 3D QA and grounding benchmarks, as well as MSQA, Beacon3D, and shortcut-sensitive stress tests, show that AR-CoT consistently improves multiple 3D MLLM backbones while strengthening chain grounding, decoy-twin contrast, and scene-swap sensitivity. These results suggest that anti-rationalization offers a practical and verifiable route toward more accurate, interpretable, and scene-faithful 3D multimodal reasoning.

Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively from multiple embodied viewpoints remains largely unexplored. We study this problem through multi-robot cooperative dynamic spatial reasoning, where a model must answer spatial, temporal, visibility, and coordination questions by integrating synchronized egocentric videos from a team of moving robots. To support this setting, we introduce CoopSR, the first benchmark for this task, together with EgoTeam, a multi-robot egocentric QA dataset. EgoTeam contains 114,227 QA pairs spanning 19 question types, four difficulty tiers, and three team sizes in Habitat and iGibson, along with a real-world test set of around 2,326 QAs collected using two quadruped robots. We further propose SP-CoR (Spectral and Physics-Informed Cooperative Reasoner), an MLLM framework for fine-grained cooperative spatial reasoning. SP-CoR combines dynamics-aware multi-robot frame sampling, spectral- and physics-guided view fusion, and physics-aligned prompt distillation, enabling the model to benefit from privileged robot-pose supervision during training while requiring only egocentric videos at test time. Across 22 MLLM baselines, SP-CoR consistently improves cooperative reasoning, outperforming the strongest fine-tuned baseline by +3.87% on Habitat and +7.12% on iGibson. It also shows stronger generalization to unseen team sizes and real-world robot tests. We will release the benchmark and code.

We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine learning methods. Because human designers rely heavily on spatial reasoning to arrange components on the chip canvas, we hypothesize that VLMs with strong visual reasoning abilities can effectively complement existing placement algorithms. We introduce VeoPlace (Visual Evolutionary Optimization Placement), a novel framework that uses a VLM—without any fine-tuning—to guide the actions of a base placer by constraining them to subregions of the chip canvas. The VLM proposals are iteratively optimized through an evolutionary search strategy with respect to resulting placement quality. Using global half-perimeter wirelength (gHPWL) after standard-cell placement and legalization as the evaluation metric, VeoPlace boosts ChiPFormer performance on 9 of 10 benchmarks with peak reductions exceeding 32%. We further demonstrate that VeoPlace generalizes to analytical placers, improving DREAMPlace on all 8 evaluated Superblue benchmarks with gains up to 4.3%. Our approach opens new possibilities for electronic design automation tools that leverage foundation models to solve complex physical design problems.

Three paradigmatic forms of inference recur across scientific reasoning: deduction, induction, and causal abduction. Reliably evaluating LLMs on these in scientific settings is currently out of reach: scientific benchmarks built on human annotations are costly and lack mechanistic ground truth, while synthetic logical-reasoning benchmarks do not resemble real scientific documents. We introduce SciR, a benchmark that combines multi-paradigm reasoning with controllable scientific rendering, anchored on three paradigmatic scientific problems. Tasks are generated from formal objects (deduction tree, inductive rule hypothesis, causal graph) to guarantee verifiable answers, then rendered into multi-document scientific discourse via per-track domain-tuned genres. The construction lets us independently vary two difficulty axes: how hard it is to extract the key information needed for inference, and how hard the principled inference itself is. We test six models. Both axes hurt every model, and their effects compound. The rendering even hurts neuro-symbolic pipelines, which hand inference to a verified solver. The two axes yield a per-model extraction-vs-inference profile: for instance, reasoning models like deepseek-r1 mostly surpass non-reasoning instruct models on the inference axis. To our knowledge, SciR is the first multi-paradigm scientific-reasoning benchmark with parametric control on both extraction and inference difficulty.