NeurIPS 2026 Papers

Open-weight models are often released, fine-tuned, aligned, merged, and re-released, making provenance audits ask not only whether checkpoints are related, but also which checkpoint came first. Many existing model-provenance methods are designed for a base-known audit setting: given a victim or source model, they test whether a suspect model is related to it. Although these audits are framed as source-to-suspect tests, their underlying evidence is often symmetric, relying on representation similarity, weight similarity, behavioral fingerprints, or correlation statistics. Symmetric pairwise comparisons can detect relatedness, but they cannot by themselves orient A versus B. We therefore introduce a local geometric comparison: instead of comparing two checkpoints directly, we add a third same-family checkpoint as a witness and compare the geometry around A with the geometry around B. Direction is inferred by asking which candidate behaves more like a branching parent. Motivated by this idea, and by the empirically observed asymmetry between parent-anchored and child-anchored witness-overlap distributions, we propose Witness Overlap, a prompt-free, training-free white-box test for directional provenance. On 176 LLM checkpoints from 16 families, our one-witness test orients 95.3\% of parent–child decisions using Frobenius cosine. We further evaluate root identification, sibling discrimination, generalizations to VLM and diffusion families, and chain-structured ordering. The signal is robust to weight noise and sparse pruning, with a proposed SVD weight reduction variant showing greater robustness than Frobenius cosine.

We introduce WildBox, a dataset and benchmark for monocular 3D detection of wildlife from drone video, comprising 237,505 3D bounding box annotations across seven African savanna species grouped into six benchmark classes. Annotations follow a KITTI/Omni3D-compatible format in a per-segment scale-normalised camera frame, with instance identities maintained across each segment. We evaluate two open-vocabulary monocular 3D architectures, OVMono3D-LIFT and DetAny3D, under zero-shot, ground-truth 2D box prompt, and supervised fine-tuning protocols. Open-vocabulary 2D foundation models provide usable zero-shot wildlife localisation (50.55 AP@50), but zero-shot 3D detection collapses to 0.00 AP across both architectures and every 2D-input condition tested, including ground-truth 2D box prompts—isolating the failure to the 3D stage. Fine-tuning on WildBox recovers performance to 8.68$\pm$0.47 AP-BEV@0.50 and 13.17$\pm$0.69 AP3D macro. Depth contributes 84\% of normalised Hausdorff distance after fine-tuning and over 99\% in zero-shot, identifying monocular aerial depth as the dominant open problem in this regime. A coarse-to-fine curriculum—pretraining on a merged zebra class before fine-tuning on the Grévy's/plains split—improves macro 3D performance with less total compute, with the largest gains on the two zebra subclasses. WildBox is released with video-level splits, evaluation code, and baseline checkpoints to enable progress in 3D wildlife perception from drone video.

Tokenizer transplant in cross-vocabulary model composition reconstructs donor-only embedding rows as weighted combinations over shared lexical anchors and reuses those coefficients on the base. We identify a structural geometric property of this reconstruction: the same coefficient vector reaches different sets in the donor and base anchor spans, an asymmetric realizability gap. Across 65 donor-base pairs under OMP, with cross-operator validation on CLP, WECHSEL, and FOCUS, we construct breaker tokens: single coefficient vectors that remain statistically inert in the donor anchor span while producing a high-salience reconstruction in the base. The same Gemma-2-2B donor checkpoint admits this construction against 13 different downstream bases drawn from five model families. The planted direction passes weight-merging with a clean reference unchanged. In a deployer case study, standard LoRA fine-tuning suppresses the breaker primarily on prompts whose distribution matches the training corpus and is not a sufficient mitigation against this attack family in our setting. The tested spectral filters miss the asymmetry. We discuss potential misuse in the open-weight composition supply chain.

Open World Object Detection (OWOD) requires a detector to recognize known categories, detect unknown objects, and incrementally learn new classes. Recent work such as OW-OVD adapts a pre-trained Vision-Language Object Detector (VLOD) to OWOD through fine-tuning. However, this approach demands costly backpropagation and often degrades the detector's own zero-shot performance on known categories, calling into question whether training is necessary. In this paper, we propose CAVE (Clustering in Attribute-Visual Embedding space), a training-free framework that adapts a VLOD to OWOD without any parameter updates. From a single forward pass, CAVE collects per-class visual and attribute statistics via von Mises-Fisher-based clustering, along with attribute co-occurrence patterns, without any gradient computation. At inference, CAVE fuses the original VLOD's text-based scores with MAS (Mixture-Aggregate Score) derived from the collected statistics for known-class detection, while unknown objects are identified by AOS (Attribute Objectness Score) that combines superclass responses with attribute co-occurrence patterns. Experiments on M-OWODB show that CAVE outperforms OW-OVD by +16.2 known-class mAP (Task 4) and +28.0 average unknown recall, while surpassing the zero-shot VLOD across all incremental tasks.

Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations: (1) different network parameters need to be trained independently for each "N-point" benchmark dataset, and (2) a model trained on an "N-point" dataset reliably outputs only the N landmarks. In our work, we first conceptualize Face Part-Anchored Landmark Positions (FPALPs), wherein each landmark is treated as a progression value between zero (start) and one (end) along a face part's contour. Every landmark can be expressed in the FPALP format, irrespective of its source dataset, hence unlocking the ability to unify all "N-point" datasets into a single dataset. Secondly, we represent each landmark with an FPALP-based query, refine it progressively with a cross-modality decoder, and predict its coordinates based on the final representation. Our approach, called Unified Dynamic FLD, embodies these two design choices and streamlines the landmark detection pipeline by enabling (1) a single model to learn on any number of "N-point" datasets, and (2) yield any number of specific landmark predictions by loading the designated landmark queries at runtime. Extensive experiments on multiple benchmark datasets show that our method delivers these benefits while remaining competitive with, and in several cases outperforming existing state-of-the-art methods.

Open‑vocabulary segmentation (OVS) enables models to segment a variety of concepts specified in natural language, removing the need for fixed label sets and fine-tuning. While recent OVS approaches have shown strong performance on natural images, they transfer poorly to Earth observation (EO) data due to differing semantics, scales, sensing modality, and geographic context. To date, large‑scale EO‑specific datasets and benchmarks for OVS remain scarce. We, therefore, introduce TerraMesh-Masks, a large-scale open-vocabulary segmentation dataset for EO, comprising 16 million binary masks aligned with nine million multimodal TerraMesh samples and annotated using OpenStreetMap and Overture-derived semantics. In addition, we provide an expert-reviewed evaluation set of 523 samples covering over 200 classes to enable robust evaluation. To demonstrate and analyze the impact of TerraMesh-Masks, we build an open-vocabulary version of TerraMind. Our results demonstrate significantly improved performance over general-purpose state-of-the-art models reaching 35.15% IoU versus 16.29% for DINOv3.txt and 11.33% for SAM-3. We release the dataset publicly under a permissive license at https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh-Masks.

Adapting CLIP for open-vocabulary video recognition necessitates a delicate balance between newly acquired video knowledge and the pretrained generalization. While existing studies pursue this generalization-specialization trade-off with additional regularizations or constraints, we argue that they overlook the deviation of representations beyond the fine-tuning data distribution, resulting in suboptimal adaptation effects. We believe such deviation is inherited from the inconsistency between the fine-tuning and evaluation objectives, where model optimization is restricted to the known training distribution but evaluated on unseen ones. In this paper, we introduce \emph{TACO}, a simple yet effective framework to mitigate the potential negative effects induced by this inconsistency. Our key insight is that adaptation should preserve OOD-relevant alignment beyond the training distribution. To this end, we propose \emph{Relative Structure Distillation}, which regularizes the relative geometry of the representation space and suppresses harmful alignment shift during training. We further decouple the representation space from the optimization space with a lightweight specialization projection, allowing task-specific adaptation without directly overspecializing the representations used at test time. \emph{TACO} establishes state-of-the-art performance on diverse benchmarks under cross-dataset and base-to-novel settings. Code will be released.

Although many OOD detection methods have been proposed, no clear winner can be identified as winners are benchmark-specific. One problem with evaluating OOD detectors is the imprecise definition of the "out" distribution, as many commonly used OOD datasets designed to test for 'semantic shift' contain a substantial proportion of 'covariate shift' samples. Moreover, recent work finds that, on carefully crafted benchmarks focused on semantic shift, modern OOD detectors yield little improvement over the baseline MSP. We analyzed a vast number of post-hoc OOD detectors on these benchmarks. We found that they consistently misclassify a common, substantial subset of samples across modern CNN and ViT architectures. We showed that failures across all OOD detectors on this benchmark are primarily due to learned feature representations: we observe that feature extractors map many semantically distant OOD samples closer to ImageNet classes than some in-distribution samples. This misalignment raises safety concerns about DNNs operating in open-set recognition scenarios. We publish a new benchmark, along with a reproducible construction protocol, that focuses on shared hard examples drawn from semantically distinct datasets, including NINCO and ImageNet-OoD. Our new benchmark can reveal fundamental security gaps in current DNNs, as they fail to reliably distinguish semantically distant OOD samples from in-distribution samples.

Deep neural networks have achieved significant success on in-distribution (ID) tasks, but out-of-distribution (OOD) samples during inference can lead to unreliable predictions. To address this issue, distance-based methods exploit the geometric structure of feature space for OOD detection. However, methods based on static information geometry fail to address geometry distorted by ill-distributed samples. Although prior work reduces the misclassification of OOD samples as ID by adjusting the residual space with real-time features, it fails to address the misclassification of ID samples as OOD. In addition, existing distance-based methods ignore class-specific characteristics when computing deviation feature and rely on the nearest prototype for uncertainty estimation, leading to suboptimal OOD detection performance. To address these issues, we propose Adaptive Mahalanobis Gap (AMG). Specifically, we first strengthen the principal space along real-time feature directions to reduce the misclassification of ID samples as OOD. Subsequently, we incorporate class variance and prototype norms to mitigate bias of deviation feature from class-wise distributional differences. Finally, we introduce the Mahalanobis gap scoring for uncertainty scoring to overcome bias from single-prototype reliance. Experiments on CIFAR-10/100 and ImageNet-1k show that AMG consistently outperforms state-of-the-art methods in FPR@95 and AUROC. Our code is available at https://anonymous.4open.science/r/AMG_AR.

Visual context is a powerful cue for object recognition: scenes inform what, where, and at what scale objects are likely to appear. Yet over-reliance on context becomes a failure mode when models predict objects based on their typical neighbours rather than the objects' own visual features, a phenomenon we refer to as typicality bias. While this issue has been extensively studied in the pre-Transformer era, it has often been assumed that architectural and scale improvements would naturally solve it. Despite recent attempts in isolated settings, a systematic diagnostic across the modern vision stack remains absent. In this work, we provide such a diagnostic framework. Specifically, we measure typicality bias via normalized pointwise mutual information (NPMI) over object pairs, and introduce STRANGE-Bench (Synthetic Typicality-RANked Grounded Evaluation Benchmark): a photorealistic, layout-controlled benchmark spanning 11 typicality tiers from typical to never-co-occurring object pairs. Across \five model families (specialist detectors, open-vocabulary detectors, self-supervised, vision-language, and multimodal large language models) and three recognition tasks}(supervised detection, open-vocabulary detection, and classification), we find the bias to be highly prevalent: gaps of 5--11\% persist when objects appear outside their typical company, and they do not consistently shrink with scale. This persistence motivates a complementary question: how to mitigate it? Data augmentation is a natural lever, since it directly shapes the training distribution that produces the bias; yet existing augmentations are designed for visual diversity, not for breaking co-occurrence shortcuts as they sample from the very distribution that produces typicality bias, preserving rather than perturbing it. We therefore propose Anchor-Atypicality Sampling (AAS), a simple context-aware variant of paste-based augmentations that uses the NPMI signal to deliberately tilt the training-time co-occurrence distribution toward atypical partners. AAS reduces the typicality gap by up to 3.3 pp on natural-image COCO splits and 17.4 pp on synthetic STRANGE-Bench across three representative model families (specialist detection, open-vocabulary detection, self-supervised classification), without sacrificing overall performance.

Accelerating stomata measurement is essential for developing drought-resilient plants. However, most automated stomata detectors are tested under narrow imaging and species conditions. Their behavior under broader ecological distribution shift remains unclear. We introduce StomataBench, a benchmark for stomatal object detection across species, acquisition protocols, and biological density regimes. The benchmark uses a curated multispecies training set of 3,154 images with 134,802 annotated stomata from 21 woody species. It is evaluated on four test datasets totaling 19,657 images, covering diverse woody taxa and heterogeneous public crop sources. Unlike standard detection benchmarks that report only AP, StomataBench evaluates measurement reliability. We measure COCO AP, F1, score-threshold sensitivity, image-level count error, and stomatal density agreement. We benchmark sixteen detectors spanning two-stage, one-stage, transformer, open-vocabulary, and promptable foundation models. The results show that model rankings change with the biological objective. GDINO-SB provides the strongest average AP generalization, while SAM3-OD gives reliable operating point for count and density estimation. Conventional detectors remain competitive on near-domain woody plants, but foundation models are robust under harder ecological shifts. Failure analysis shows that far-OOD detection is often limited by missing stomata. These results identify cross-lineage proposal generation as a key unsolved problem for reliable stomatal phenotyping under ecological distribution shift.

AI-generated image detectors often generalize poorly to unseen generators because the classifier latches onto generator-specific feature directions that are predictive only on the training set. We show that simply projecting away generator-discriminative LDA directions fails: these directions also carry genuine real-vs-fake signal, so post-hoc erasure removes evidence along with shortcuts. We introduce StaR, a training-time regularizer that scores each LDA direction by leave-one-generator-out stability and penalizes the classifier only on unstable directions; stable directions are left available for classification. On ResNet-50, StaR reduces the across-seed standard deviation of the four-OOD average AUC by 24x relative to ERM (F-significant on every OOD) while preserving in-distribution-like performance; on CLIP ViT-L/14, StaR improves the four-OOD average AUC by 1.7 points over matched CLIP-ERM and 4.5 over EFFORT, with the largest gains on platform-shift benchmarks. Mechanistic analyses show that StaR rotates the classifier away from unstable generator directions while amplifying generator-discriminative structure in the features---a rotational, not erasive, effect that post-hoc projection cannot reproduce. We additionally find that the conventional ``argmax in-domain validation'' epoch-selection rule is sub-optimal: a deployable ``stop at first in-domain 0.99'' rule beats it by sim0.7 AUC points on average.

Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-intensive tasks, where the content appears coherent but contains errors like logical flaws and unreliable intermediate results. While step-level analysis is commonly used to detect internal hallucinations, it suffers from limited granularity and poor scalability due to its reliance on step segmentation. To address these limitations, we propose TokenHD, a holistic pipeline for training token-level hallucination detectors. Specifically, TokenHD consists of a scalable data engine for synthesizing large-scale hallucination annotations along with a training recipe featuring an importance-weighted strategy for robust model training. To systematically assess the detection performance, we also provide a rigorous evaluation protocol. Through training within TokenHD, our detector operates directly on free-form text to identify hallucinations, eliminating the need for predefined step segmentation or additional text reformatting. Our experiments show that even a small detector (0.6B) achieves substantial performance gains after training, surpassing much larger reasoning models (e.g., QwQ-32B), and detection performance scales consistently with model size from 0.6B to 8B. Finally, we show that our detector can generalize well across diverse practical scenarios and explore strategies to further enhance its cross-domain generalization capability.

While large-scale visual foundation models (VFMs) exhibit strong generalization across diverse visual domains, their potential for infrared small target (SIRST) detection remains largely unexplored. To fill this gap, we systematically introduce frozen VFM representations into the SIRST task and propose a Foundation-Driven Efficient Paradigm (FDEP), a general framework compatible with diverse VFMs and SIRST networks, which improves detection accuracy without additional VFM-related inference overhead. Specifically, a Semantic Alignment and Modulated Fusion (SAMF) module is designed to achieve dynamic alignment and deep fusion of the global semantic priors from VFMs with task-specific features. Meanwhile, to avoid the inference-time overhead introduced by VFMs, we propose a Collaborative Optimization-based Implicit Self-Distillation (CO-ISD) strategy, which enables implicit semantic transfer between the main and lightweight branches through parameter sharing and synchronized backpropagation. In addition, to unify the fragmented evaluation system, we construct a Holistic SIRST Evaluation (HSE) metric that performs multi-threshold integral evaluation at both pixel-level confidence and target-level robustness, providing a stable and comprehensive basis for fair model comparison. Extensive experiments demonstrate that the SIRST detection networks equipped with our FDEP framework achieve state-of-the-art (SOTA) performance on multiple public datasets. Our code will be open source.

Monocular 3D detection (Mono3D) hinges on recovering depth from a single image, for which the geometric formulation $z \approx f \cdot H / h_{2D}$ is widely adopted due to its simplicity and direct grounding in projective geometry. We show that this formulation harbors a systematic, projection-induced bias: substituting the 2D bounding-box height $h_{2D}$ recovers the depth of an object's visible front surface rather than its center, causing size-dependent underestimation that persists even under ground-truth inputs. We address this front-surface bias with MonoPCA, a unified framework built on a projection-consistent reformulation. At its core, PCDepth retains the measurable $h_{2D}$ and closes the gap to center depth through an orientation- and size-aware correction derived from perspective geometry, eliminating the bias without introducing ill-posed regression targets. Because PCDepth couples depth to predicted object size and orientation, attribute perception becomes essential. We therefore introduce GeoAlign, a teacher--student framework that transfers geometric representations from Vision Foundation Models into a lightweight student, strengthening attribute perception at no inference cost. Across four challenging benchmarks, MonoPCA establishes new SoTA performance, including a $+1.44\%$ AP3D gain on the KITTI leaderboard and $+2.80\%$ on Omni3D Out.

Open-vocabulary 3D scene understanding provides an important interface for querying and interacting with reconstructed scenes through natural language. Although recent 3DGS methods have enabled text-driven object selection and open-vocabulary segmentation, they still struggle with compositional queries involving attributes, spatial relations, and part-whole relations. Most existing approaches learn per-Gaussian or per-cluster language features, construct query-conditioned referring fields, or perform spatial reasoning only at inference time, but they do not organize the scene itself into a reusable relation-aware representation. As a result, object relations are not explicitly stored as typed and confidence-aware 3D structures, limiting compositional reasoning and multi-hop querying. To address this issue, we propose RelGS, a relation-aware 3D Gaussian Splatting framework for open-vocabulary scene understanding. RelGS learns semantic and instance embeddings for Gaussians under multi-view supervision, groups them into cross-view consistent 3D semantic nodes, and enriches each node with structured attributes verified by reverse CLIP consistency. It further constructs a confidence-weighted relation graph, where 3D spatial cues propose candidate relations and an LLM verifies their semantic plausibility. At query time, RelGS combines attribute matching, CLIP retrieval, and graph traversal to localize the target object. Extensive experiments demonstrate strong performance on word-level selection, sentence-level referring segmentation, multi-hop relational queries, and point-level semantic segmentation.

Monocular 3D object detection spans two regimes: closed-set detectors operating within a fixed category vocabulary, and open-vocabulary detectors that localize arbitrary categories by leveraging depth foundation models for 3D geometry. We find that current depth foundation models, despite their strong zero-shot generalization, lack the object-level precision 3D detection demands: substituting a state-of-the-art depth foundation model for a strong detector's predicted depth degrades accuracy, even falling below the detector's own prediction. Rather than pushing detectors or depth models to be more accurate end-to-end, we treat object-level depth refinement as a stand-alone task and present RefineAny3D, a vision-language model that corrects depth without ever predicting a numerical value. Our key insight is that depth error has a direct visual signature in image space: when projected onto the image, a correctly placed box tightly encloses the object, while a too-far box projects too small and a too-close box projects too large. Depth refinement thus reduces to a visual alignment problem rather than a metric regression problem, which we instantiate by extending the VLM's vocabulary with action tokens that replace numerical depth output with categorical decisions, and by supervising the model on a large-scale chain-of-thought dataset that grounds each decision in explicit visual evidence. Applied as a single post-hoc step, RefineAny3D delivers consistent gains across closed-set detectors, open-vocabulary detectors, and 3D auto-labeling tools, and generalizes to novel categories, scenes, and cameras without retraining.

Driver monitoring concerns determining not only what a driver is doing, but whether the behavior violates a driver-behavior safety rule under the current driving context. Existing distracted-driving benchmarks largely reduce this problem to closed-set action recognition, often missing cases where the same behavior changes meaning with road scene, vehicle state, duration, and rule-specific exceptions. We present **ROAD**, a rule-grounded context-aware open-world framework for driver anomaly detection. ROAD represents driver-monitoring constraints as structured rule cards and jointly reasons over in-cabin driver evidence, road-context video, optional vehicle-state metadata, and open-world semantic cues to produce structured anomaly judgments: driver action, violated rule, anomaly score, supporting evidence, exception status, and explanation. Unlike generic MLLM prompting, ROAD separates perception, rule grounding, and semantic generalization. Road context modulates driver-centric evidence, rule-card encodings select rule-relevant evidence, and a LoRA-adapted MLLM extracts open-vocabulary safety cues from compressed video evidence. This design targets two forms of open-world generalization: recognizing unseen driver behaviors beyond a fixed action taxonomy and adapting to new or revised rule cards. We further introduce ROAD-Bench, a rule-grounded benchmark with context, exception, evidence, anomaly-score, and explanation annotations. With a three-stage curriculum from action primitives to context-rule alignment and anomaly adaptation, ROAD outperforms evaluated baselines across driver-distraction recognition, open-world behavior recognition, and rule-grounded anomaly detection, using an 8B multimodal backbone. It achieves 92.01\% average accuracy over seven driver-recognition benchmarks, 77.9\% AUROC and 46.9\% unseen-class F1 in behavior recognition, and 73.6\% AUROC, 68.3\% rule accuracy, and 54.5\% Expl.-Rule F1 on ROAD-Bench.

Robust 3D lane detection requires accurate metric road geometry recovery from visual inputs, yet conventional RGB-based approaches suffer from fragile lane evidence under adverse illumination, motion blur, and long-range perspective compression. Event cameras offer complementary high-temporal-resolution, high-dynamic-range structural cues that are robust to these challenges, but their sparse, motion-dependent responses cannot be directly fused into consistent 3D geometry. In this paper, we present the first attempt to introduce event cameras into 3D lane detection. To enable systematic research, we first build two multimodal benchmarks with metric 3D lane annotations: DSEC-3DLD (real-world sequences) and Ev-OpenLane (large-scale simulated sequences). We further propose RETR, a structure preserving transformer that converts complementary RGB and event observations into coherent 3D lane geometry. RETR first aligns modality consistent evidence through Reciprocal Context Flow Fusion, then preserves thin and uncertain lane structures with Uncertainty Aware Structural Consolidation, and finally decodes ordered lane hypotheses using a Geometry State Decoder with proposal conditioned initialization, reference conditioned query evolution, and diversified geometric inquiry. Extensive experiments show that RETR achieves state-of-the-art performance on both benchmarks, especially showing strong improvements in challenging lighting and complex road geometry scenarios.