NeurIPS 2026 Papers

Partial Least Squares (PLS) regression extracts a few outcome-aligned directions in a high-dimensional $\mathbf{X}$ and is widely used across applied science, but inference on the resulting fit is either expensive (CV-permutation-$Q^{2}$), biased and discouraged (jackknife $t$), or absent sklearn ships no test; the corresponding statsmodels feature request has been open since 2019). We close this gap by reducing inference to held-out OLS refits of the supervised subspace, a primitive shared by PLS, supervised PCA, linear probes, and sparse-autoencoder concept directions. We supply two tests on it: a Nadeau-Bengio corrected asymptotic $t$-test (NB-asymptotic) as the default, and a permutation-referenced variant (NB-permutation) for near-singular designs where the Fisher-$z$ asymptotic drifts. A rotation-invariance result shows the held-out predictions, and hence the test statistic, are unchanged under any orthogonal rebasing of the supervised span, so the same test applies to varimax-rotated word-readable axes. We validate on PLS (synthetic geometries, Tecator NIR chemometrics, cross-lingual GloVe valence prediction in EN/PL/ES at $K=3$, $r^{2}\approx0.63/0.69/0.55$, all $p<.001$); transfer to the other OLS-refit pipelines is argued but not tested here. NB needs $\sim20\%$ fewer samples than CV-permutation-$Q^{2}$ for matched power and is $50-100 \times$ cheaper at common, already compromised, defaults. We release a Rust library with Python, R, and Julia bindings, plus a Python text pipeline.

Charts convey dense quantitative and relational information, yet general chart parsing remains challenging due to fragmented evaluation and limited structural reasoning. Existing benchmarks focus on narrow sub-tasks with inconsistent output formats (e.g., Markdown, CSV, JSON, SVG, code), hindering fair comparison and often overlooking real-world scenarios such as printed or hand-drawn photos. Meanwhile, end-to-end multimodal models tend to learn shallow pixel-to-text mappings without explicit structure understanding, making them fragile under visual variations. To address these issues, we present a unified framework that connects evaluation and modeling. On the evaluation side, we introduce **ChartArena**, a comprehensive bilingual benchmark covering eight chart families, including both numeric charts (bar, line, pie, radar, box plot, combination) and diagrammatic structures (flowchart, mind map). The dataset is constructed via a human-agent collaborative annotation pipeline, where model-assisted drafts are refined through multi-stage human verification to ensure structural consistency and annotation reliability. Furthermore, we design a format-agnostic evaluation protocol that maps different outputs into two canonical semantic spaces: a normalized triple view and a directed graph view, and evaluates them with structure-aware metrics. On the modeling side, we propose **Chart Atomic Primitives (CAP)**, a lightweight semantic scaffold that introduces structural inductive bias during supervised fine-tuning. By guiding the model to infer structure before generating outputs, CAP leads to more consistent and robust parsing behavior. Extensive experiments show that models trained with CAP achieve reliable performance on both rendered charts and real-world images. Code, benchmark, and models will be made publicly available.

Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual properties models actually converge on and which factors may underlie this convergence. To address this, we decompose the object similarity structure of 162 diverse vision models into a small set of non-negative dimensions. To determine universal versus model-specific dimensions, we then estimate how often each dimension reappears across models. In contrast to model-specific dimensions, universal dimensions are more interpretable and more strongly driven by conceptual image properties, indicating the relevance of interpretability and semantic content as implicit factors driving universality across models. Differences in architecture, objective function, training data, model size, and model performance do not explain the emergence of universal dimensions. However, models with more universal dimensions also better predict macaque IT activity and human similarity judgments, suggesting that universality reflects representations relevant to biological vision. These findings have important implications for understanding the emergent representations underlying deep neural network models and their alignment with biological vision.

Channel-adaptive Vision Transformers (ViTs) struggle to scale to non-RGB images with many uncorrelated or weakly correlated channels. Existing approaches roll out each channel into its own patch embedding, so the token sequence length grows linearly with the number of channels and self-attention cost grows quadratically. We introduce \emph{ChannelMixer}, a multi-channel tokenizer that aggregates all channels within each spatial patch into a single channel-mixing token, decoupling the token sequence length from the number of channels. ChannelMixer is pretrained once with a lightweight autoencoder in a channel-agnostic manner and the resulting tokenizer can be easily integrated with ViTs trained under both supervised and self-supervised objectives. On multi-channel imaging benchmarks spanning 3 to 29 channels, ChannelMixer matches or outperforms channel-adaptive state-of-the-art methods while using approximately $20\times$ fewer FLOPs and $29\times$ fewer tokens than rolled-out tokenizers, measured on 29-channel images with a ViT-S/16.

High-content multichannel imaging, from Cell Painting assays to remote sensing, is central to modern scientific pipelines. Because these channels are highly heterogeneous and experimental configurations frequently evolve, a practical vision backbone must be fundamentally channel-agnostic. Current channel-adaptive vision transformers rely on global self-attention with learnable channel embeddings, which can be computed only for previously known channels. Attempts to overcome this limitation of new channels either discard the embeddings, losing channel identity, or process each channel independently, thereby eliminating critical cross-channel interactions. To address this, we introduce ChanSFormer, an efficient Vision Transformer that replaces rigid embeddings with disentangled spatial-channel attention and individual channel CLS tokens. This architecture natively preserves both distinct channel identities and cross-channel information flow. Furthermore, the individual channel class token design unlocks more informative feature representations for self-supervised learning and enables representation-based channel sampling while remaining channel agnostic. We evaluate ChanSFormer on two biology multi-channel datasets, CHAMMI, JUMP-CP and a satellite dataset, So2Sat. Experimental results show that ChanSFormer outperforms state-of-the-art methods by up to 4.27% in classification accuracy. It also demonstrates exceptional cross-dataset transferability under a self-supervised setting, outperforming previous methods by up to 2.72% in accuracy in the same classification tasks. Furthermore, the disentangled attention reduces the quadratic complexity of channel $\times$ spatial sequence length of the global attention baseline, thus improving throughput by 55%--313%.

Spatial intelligence requires foundation models to maintain coherent spatial state across interactions with the physical world. However, existing data-centric approaches typically treat spatial reasoning as independent question-answer instances, enabling shortcut-based answering and providing limited supervision for persistent spatial understanding. To address this, we introduce ChainSpace, a chained-reasoning paradigm that structures spatial reasoning as a state-preserving multi-round process. In this paradigm, spatial questions are organized into logically constrained and jointly consistent chains, where later questions depend on spatial constraints established in earlier rounds. Following this principle, we instantiate ChainSpace-Bench, a manually annotated real-world multi-round benchmark with a Chain-Aware Metric, and ChainSpace-Pipeline, a simulator-based chain-structured supervision generation framework for spatial intelligence training. Experiments show that ChainSpace-Bench exposes chain-level failures that are not captured by isolated question accuracy. Additionally, with a relatively small amount of simulator-generated chained data, models trained by ChainSpace-Pipeline achieve the best performance among open-source models on ChainSpace-Bench and transfer competitively to multiple external spatial intelligence benchmarks. These results establish ChainSpace as an effective paradigm for more faithful evaluation and more data-efficient learning of spatial intelligence.

Imitation learning policies for robotic manipulation inherently suffer from compounding errors. To mitigate this, inference-time policy steering introduces predictive reasoning to evaluate and refine action proposals before execution. However, existing methods typically anticipate consequences in unstructured pixel or latent spaces, leading to physically inconsistent predictions and unreliable error detection. Furthermore, their blindness to global progress often yields locally plausible corrections that fail to complete the overall task. To address these limitations, we propose Chain-of-Correction (CoC), an inference-time steering framework driven by a single VLM. Shifting away from unstructured state predictions, CoC explicitly leverages anchor-grounded scene graphs to track exact 3D physical relations. Built upon this representation, a progress-aware dual-check mechanism verifies historical execution and predicts future task advancement to systematically intercept myopic action proposals. Through a failure-augmented supervised fine-tuning pipeline, CoC seamlessly unifies scene graph extraction, progress evaluation, and precise kinematic correction, eliminating the need for task-specific world models. Extensive experiments across the COLOSSEUM benchmark, a newly introduced offline protocol for predictive reasoning, and real-world tasks demonstrate that CoC significantly outperforms state-of-the-art baselines in fine-grained error detection and robust error recovery.

As state-of-the-art neural networks are deployed on reasoning and algorithmic tasks, exactness guarantees become increasingly important. However, high average-case accuracy can still mask inconsistent behaviors. This motivates exact certification, which asks for the smallest set of labeled examples needed to certify that a learned hypothesis equals the target. We show that while some hypotheses are easy to certify, even minimal overparametrization can make certification exponentially hard across several hypothesis classes. For threshold circuits of depth $\ge 2$, adding a single extra gate can force certificate sizes exponential in the input dimension. We show an analogous hardness result for log-precision Transformers with only constant architectural overhead. We also characterize approximate certification, showing that allowing only polynomially many mistakes still requires exponentially large certificates, whereas constant relative-error guarantees can hide exponentially many failures. Empirically, we study certification for circuits and Transformers trained to recognize binary addition and find that imperfect models can evade detection unless the certificate is exponentially large.

Cellular networks are critical infrastructure supporting billions of worldwide users and safety- and mission-critical services. Vulnerabilities in cellular networks can therefore cause service disruption, privacy breaches, and broad societal harm, motivating growing efforts to analyze 3GPP specifications that define required device and operator behavior. However, existing benchmarks for this domain share three systematic deficiencies: they rely on LLM-generated entries without expert verification, they do not require faithful interpretation of normative language, reasoning across cross-referenced clauses, and verifiable conclusions grounded in multimodal evidence such as tables and figures, and they do not evaluate security reasoning over end-to-end protocol behavior. To address these gaps, we present CellularS2-Bench, a staged benchmark. CellularS2-Bench covers core control-plane specifications, processing over 1,760 paragraphs, 13,800 sentences, 424 figures, and 340 tables. It comprises 7,851 questions and instances derived from 3GPP specifications and 43 real-world vulnerabilities across three progressive stages. Beyond newly constructed tasks, CellularS2-Bench integrates and corrects subsets from 7 prior datasets to broaden task coverage. To establish a reproducible baseline on CellularS2-Bench, we introduce CellularS2-ARI, a unified Adapt-Retrieve-Integrate framework for systematic understanding and specification-driven security analysis of 3GPP specifications. Together, they establish an accessible and reproducible foundation for quantifying progress in specification understanding and security reasoning in cellular network security.

Vision-Language-Action (VLA) models for autonomous driving produce natural-language reasoning alongside predicted trajectories, but whether this reasoning reflects the causal structure of the scene remains untested. We introduce CausalDriveBench, an evaluation framework grounded in Pearl's Causal Hierarchy (PCH) that tests causal reasoning in driving-specific VLAs through structured visual question answering (QA) and alternative-trajectory prediction. To this end, we construct causal scene graphs over nuScenes that distinguish causally active, dormant, and distractor entities, separating perceptual salience from causal relevance. The benchmark spans all four rungs of PCH (association, intervention, and counterfactual along with causal discovery) for QA generation. For the higher rungs, we additionally provide reference trajectories under specified scene modifications, enabling action-level verification that complements reasoning-level evaluation. In total, the benchmark contains 7,285 verified causal QA pairs and 1,000 counterfactual trajectories derived from nuScenes. We evaluate 10 driving-specific VLAs and 3 general-purpose VLMs, and report three findings. First, the best model reaches only 70.6\% QA accuracy, and 4 of 13 models score below random chance. Second, comparing each driving VLA to the general-purpose VLM that shares its language backbone, the cost of driving fine-tuning ranges from 2 to 34 percentage points on causal QA, with post-training design explaining the spread. Third, causal QA and trajectory accuracy are statistically uncorrelated across models: under counterfactual prompts, predicted trajectories either over-react or collapse onto the observed-scene baseline. Taken together, these results show that neither fluent rationales nor accurate observed-scene trajectories constitute evidence of causal understanding.

Dense video captioning describes multiple events in long videos. However, each event is predicted independently, ignoring the causal relationships with the other events in the video. These causal relationships are important for human-like descriptions of long videos. Our experiments show that exisiting vision-language models fail at predicting causal relationships in long-form videos. No prior work jointly generates captions and event timestamps and predicts causal relationships over multi-event sequences in long-form videos. We address this through two contributions. First, we create a benchmark dataset with 21.6K videos and 85K events by using a large language model to generate annotations from dense captions in YouCook2 and ActivityNet. We validate this benchmark dataset through multimodal alignment, vision-language models, and human judgment. Second, we propose a novel architecture for dense causal captioning that not only generates captions but also learns causal relationships. This novel architecture not only predicts causality effectively (0.73 F1 ActivityNet, 0.64 F1 YouCook2) but also improves the vision-language model's core capabilities: caption quality increases by +6.3 CIDEr on ActivityNet and +7.4 on YouCook2, while temporal grounding becomes accurate as causal predictions force precise event boundary localization.

This position paper argues that causal benchmarks for multimodal AI should measure categorical difference, not capability gap. The field reports machine causal performance against human performance and interprets progress as gap-closing along a shared scale. The shared-scale assumption rests on a flattened reading of Hume's regularity theory in which causation reduces to constant conjunction across instances and the temporal experience of the observer has no role. When understood in terms of its presuppositions, Hume's regularity is inseparable from a temporally extended subject who undergoes the conjunctions over time. Human and machine causal cognition therefore have different temporal architectures. The two are grounded in time as substrate and time as dimension, a categorical difference that scale or additional intervention machinery cannot close. Evaluation paradigms that treat the two as commensurable produce inflated assessments of machine causal competence that independently scored probes cannot capture. We propose \textit{Anthropomorphic Competence Inflation} as a construct the field can develop into a broader measurement instrument and formalize it through the \textit{Inflation Score}. Preliminary observations on two recent vision-language models match the pattern predicted by the categorical claim. The implications affect evaluation practice, deployment in causal-decision domains, and the next phase of AI causality research.

Recent AI regulations increasingly emphasize the need for mechanisms that preserve the utility of data for AI innovation while preventing misuse, particularly by enforcing purpose limitation in downstream AI applications. In practice, enforcing this principle remains challenging, as released data can be trivially fed into arbitrary models beyond its declared intent. Existing approaches attempt to mitigate this risk by either perturbing data or retraining models to limit unintended use. These strategies, however, offer no protection against inference by unknown or externally trained models, or fundamentally rely on control over the training or deployment. In this work, we introduce non-transferable examples (NTEs), recoded data that act as a task-level "ciphertext" decodable only by a designated model. Where adversarial examples exploit sensitive input directions, NTEs use the complementary insensitive subspace: a training-free, data-agnostic recoding within a model-specific low-sensitivity subspace preserves the authorized model's outputs while degrading unauthorized ones through subspace misalignment. We establish formal bounds certifying output fidelity for the authorized model and showing unauthorized degradation scales with measurable spectral misalignment between models. Empirically, NTEs preserve authorized-model performance across diverse vision backbones and vision-language models, while unauthorized models collapse even under adaptive reconstruction attacks. These results establish NTEs as a practical means to preserve intended data utility while preventing unauthorized exploitation. Our source code and visual demos are available at: https://github.com/model-specific/non-transferable-examples.

Many real-world problems demand exact constraint satisfaction to be meaningfully solved, yet existing neurosymbolic methods face a stark trade-off: those that guarantee satisfaction fail to scale, while those that scale must resort to approximation. Meanwhile, traditional deep learning approaches offer no satisfaction guarantees at all. In this paper, we propose Casper, a neurosymbolic layer that simultaneously (i) guarantees constraint satisfaction, (ii) scales at inference time, (iii) is fully automated, requiring no problem-specific engineering, and (iv) is fully differentiable, making it composable with any neural predictor both at training and at inference time. Casper casts constraint satisfaction as a closed-form Euclidean projection onto the constraint-feasible region, computable in a single forward pass. Experiments across MNIST arithmetic (up to 1024 digits), Sudoku solving, and Warcraft pathfinding show that Casper is the only method that produces guaranteed-valid outputs at scale: on 30×30 Warcraft grids, exact baselines time out and approximate ones produce valid paths less than 7% of the time, while Casper produces them 100% of the time.

Active computer vision promises efficient, biologically plausible perception through sequential, localized glimpses, but lacks scalable general-purpose architectures and pretraining pipelines, leaving Active-Vision Foundation Models (AVFMs) underexplored. We introduce CanViT, the first task- and policy-agnostic AVFM. CanViT uses scene-relative RoPE to bind a retinotopic Vision Transformer backbone and a spatiotopic scene-wide latent workspace, the canvas. Efficient interaction with this high-capacity working memory is supported by Canvas Attention, a novel asymmetric cross-attention mechanism. We decouple thinking (backbone-level) and memory (canvas-level), eliminating canvas-side self-attention and fully-connected layers to achieve fast sequential inference and scalability to high output resolutions. We propose a label-free active vision pretraining scheme, policy-agnostic passive-to-active dense latent distillation: reconstructing scene-wide DINOv3 embeddings from sequences of low-resolution glimpses with randomized locations, zoom levels, and lengths. We pretrain CanViT-B from a random initialization on 13.2 million ImageNet-21k scenes--an order of magnitude more than previous active models--and 1 billion random glimpses, in 166 hours on a single H100. On ADE20K segmentation, a frozen CanViT-B achieves 38.5% mIoU in a single low-resolution glimpse, outperforming the best active model's 27.6% with 20x fewer inference FLOPs as well as its FLOP- or input-matched DINOv3 teacher. Given additional glimpses, CanViT-B reaches 45.9% ADE20K mIoU. On ImageNet-1k classification, CanViT-B also sets a new active-vision state of the art, with 84.5% top-1 accuracy after fine-tuning. CanViT generalizes to longer rollouts, larger scenes, and new policies. Our work narrows the wide gap between passive and active computer vision, demonstrating the potential of task- and policy-agnostic AVFM pretraining.

Relational out-of-distribution errors are easy to describe and hard to evaluate cleanly. An image may be natural, a caption may be fluent, and all mentioned objects may be common, yet the caption may describe the wrong relation in the image. Many evaluations intended to test this behavior can be solved without checking the pairing at all, because negatives differ in caption style, template, length, object frequency, or image statistics. We propose counterfactual pairing cycles, a blocked evaluation primitive that reuses the same two images and the same two captions on both sides of the comparison, changing only which image is paired with which caption. This makes image-only, text-only, and additive component shortcuts cancel exactly in finite samples. The same contrast also gives a training loss for relational outlier exposure and a diagnostic for bilinear vision-language scores. Across synthetic controls, GQA, and COCO, cycles separate coarse image-caption matching from stricter role-binding failures. In a shortcut-contaminated COCO outlier-exposure stress test, a model selected by a perfect shortcut validation AUROC transfers poorly to exact relational cycles, while cycle training improves CycleAcc from \(0.576\) to \(0.911\). The main lesson is simple: to test whether a model understands a pairing, the benchmark should keep the parts fixed, change the pairing, and ask only about coupling.

Legal judgment prediction has made substantial progress in recent years, yet existing work evaluates whether models reach correct conclusions rather than whether they reason correctly. The core of legal decision-making is the mapping of case facts to the elements of an applicable legal provision, a step that prior benchmarks neglect. We introduce the first benchmark for evaluating legal reasoning, built on 375 judgments of the European Court of Human Rights (ECtHR) discussing Art. 11 ECHR on freedom of assembly. We exploit that the court routinely splits the decision in a stage reporting the facts of the case, and an evaluation stage in which it uses these facts selectively for justification. We give the (preprocessed) list of facts to 10 downstream LLMs and evaluate their justifications. The pipeline is validated by three expert legal annotators. Downstream models are reasonably accurate in predicting whether Art. 11 has been violated, and which elements of doctrine are critical, but perform substantially worse at attributing specific facts to doctrinal elements. Essentially they make the right decision, but for the wrong reasons. This weakness is invisible under standard evaluation metrics. Our benchmark, pipeline, and evaluation framework are released. They can easily be adapted to any legal rule that follows the Civil law tradition, i.e. to legal reasoning that requires the mapping of facts to an abstract legal rule.

Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask $y$ for a given image $x$. In this work, we first reveal the latent nature of artifacts and thus reinterpret IML as a latent-variable problem, $P(y| x)=\int P(y|z) P(z|x)dz$, where $z$ denotes the artifacts. Following this interpretation, we pinpoint the cause for the current IML models' insufficiency as their implicit artifacts modeling strategy, highlighting the necessity of modeling $z$ in an explicit manner. Without direct labels, feature disentanglement is the most appropriate solution for this explicit modeling. Accordingly, we propose a two-stage learning paradigm with the Pairwise Artifacts Learning (PAL) and Standard Localization (SL) phases to estimate $P(z|x)$ and $P (y|z)$ via edit relations. To support our edit-relation-based learning, we further curate EditGroup-45K, a source-anchored dataset organized into edit groups for pair construction. Extensive experiments show that our PAL paradigm yields consistent improvements across diverse IML architectures, and empirical analyses further verify that PAL does capture artifacts explicitly through feature disentanglement. Code and dataset will be publicly available.

Since Vision-Language Models (VLMs) are the core of the perceptual and reasoning capability in Vision-Language-Action models, which are deployed in environments shared with humans, evaluating VLMs' safety reasoning from video is critical for preventing human harm. Current video-based benchmarks, however, evaluate only whether a model can recognize dangers that are already visible, and overlook two capabilities that VLMs require: reasoning under hierarchical principles in which human safety takes precedence over obedience to user instructions, and anticipating harm rather than identifying it after it has occurred. We introduce VESPER, a video benchmark that targets both capabilities by scenarios in which a robot is executing a user-assigned task when a person unexpectedly moves into harm's way, requiring the model to predict the potential harm and decide whether to continue or stop. The benchmark comprises 458 videos covering matched safe and dangerous variants, built through a human-in-the-loop pipeline from diverse taxonomies. We evaluate 21 VLMs, and the results indicate that the current VLMs fail to properly decide the actions under such a scenario.