NeurIPS 2026 论文列表

Vision-Language Models (VLMs) exhibit remarkable zero-shot generalization, yet they often encode unwanted or hazardous stylistic domains such as idealized textbook diagrams in medical AI or cartoon vehicles in autonomous driving. Approximate Domain Unlearning (ADU) aims to selectively erase a model's recognition of a target visual domain while preserving accuracy on the remaining domains. However, existing ADU methods operate under a flawed closed-vocabulary assumption: they evaluate unlearning solely on the specific object classes seen during the unlearning fine-tuning phase. Consequently, these methods do not unlearn the domain itself; they merely overfit to seen class-domain pairs, leaving the domain easily recognizable for unseen classes and providing a false sense of removal. We argue that true domain erasure must be class-agnostic. To address this, we formalize Open-Vocabulary Domain Unlearning (OVDU), a rigorous protocol that mandates domain forgetting must transfer to held-out classes. To solve the OVDU challenge, we propose a surgical parameter-editing framework. First, a Fisher Information mask isolates domain-sensitive weights, mathematically protecting foundational zero-shot generalization. Second, our Targeted Manifold Scattering (TMS) objective uses preference-based mining to locally scatter the forget domain's stylistic geometry. Evaluated across PACS, OfficeHome, and DomainNet, our method vastly improves open-vocabulary generalization over existing baselines. Crucially, it delivers exceptional sample efficiency, outperforming peak 8-shot baseline results with only 4 shots.

While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incompleteness. Across four widely used datasets (COCO, Pascal VOC, Cityscapes, KITTI), re-annotation reveals substantial increases in annotated objects (e.g., up to +60% on KITTI and +40% on COCO), driven primarily by previously unlabeled small, occluded, or densely packed instances. While some differences arise from dataset-specific annotation conventions, we consistently find that missing annotations are the main source of label errors across all datasets. To achieve high data quality, we introduce a scalable annotation pipeline that emphasizes high recall and captures ambiguity through soft labels aggregated from at least 11 annotators per object. The resulting annotations improve coverage and align well with human calibration. We show that benchmark performance is highly sensitive to annotation quality, although model rankings remain largely stable. We introduce two large-scale benchmarks: (i) an uncertainty-aware object detection benchmark, and (ii) a label error detection benchmark grounded in real label errors. We show that current detectors are strongly depended on annotation quality and are misaligned with human perception. Current label error detection methods, which have been shown to perform well on synthetic noise, struggle to achieve high recall and precision on real label errors. Our results highlight the need for future object detection benchmarks to move beyond deterministic annotations toward high-recall, uncertainty-aware evaluation that maximizes valid instances and better reflects real-world ambiguity.

Textual cues provide important navigation-relevant semantic information like room names, store signs, and floor labels. However, conventional optical character recognition (OCR) systems and datasets are designed to detect all visible text, including advertisements and price tags, regardless of their relevance to navigation tasks. This characteristic of OCR can produce noisy semantic cues, making it difficult to identify which text is useful for robot navigation. A straightforward solution is to curate existing OCR datasets by retaining only navigation-relevant text and removing irrelevant annotations, but this process requires substantial manual effort. To address this problem, we introduce NavOCR, a dataset generator for navigation-relevant text detection that does not require manual curation. By leveraging conventional OCR, open map resources, and filtering with a vision-language embedding model, NavOCR identifies which text instances serve as place identifiers for robot navigation and constructs datasets from images available on the web. NavOCR aims to complete the entire process within a single day, from data collection to model training, enabling rapid adaptation to diverse environments and languages. Compact models trained on datasets generated by NavOCR achieve higher detection accuracy and substantially better computational efficiency than vision language models and open vocabulary detectors. We further show that detecting navigation-relevant text improves the quality of semantic cues used for localization and mapping in robot navigation applications.

3DGS-based open-vocabulary 3D scene understanding requires identifying reconstructed 3D instances that correspond to a natural-language query. Existing methods typically compress multi-view observations of an instance into a single semantic descriptor, which is insufficient to represent a complete 3D instance. To retain rich semantic information across diverse viewpoints, we propose MSR-3D, a framework built around a Multi-Mode Semantic Representation. Specifically, MSR-3D derives multi-view observations from both 2D segmentation masks and rendered masks of each segmented 3D instance. These observations are organized into groups. Each group is summarized as a semantic mode through a representative feature and associated statistical attributes. Based on the multi-mode semantic representation, we further introduce a lightweight learnable mode-aware matcher for querying. The matcher employs linear scoring modules to project the multi-mode representation of each query-instance pair into a three-dimensional score space. Calibrated decision boundaries are then learned in the resulting score space to separate true query-instance matches from semantically related distractors. This structured and decomposable design further improves the interpretability and generalization of MSR-3D. Experiments on open-vocabulary scene-understanding benchmarks show that MSR-3D achieves state-of-the-art results, improving mIoU on LERF-OVS by 7.52% over the baseline and achieving the best overall performance on 3DOVS.

Open-vocabulary dense perception requires grounding language-specified concepts in local regions for recognition, localization, and segmentation beyond closed category sets. CLIP learns image-level vision-language alignment, whereas dense prediction requires spatially precise and semantically reliable local features. Although recent dense CLIP adaptation methods reduce this gap through region-level semantic transfer and spatial correlation guidance, maintaining stable local semantics under contextual variation remains challenging. Specifically, the global contextual modeling in CLIP may introduce undesirable dependencies into local features. Changes in background, scale, viewpoint, or scene layout may cause identical object regions to yield inconsistent dense representations, leading to unstable correspondences and semantic drift. Moreover, spatial correlation guidance from visual foundation models captures visual relatedness between patches, but not whether coherent regions correspond to the queried textual concept, leaving concept-level dense alignment under-constrained. To address these issues, we introduce Semantic Spatial Consistency Learning $\textbf{(SSCL)}$, an adaptation method with two complementary objectives: 1) Context-Invariant Spatiotemporal Consistency (CISC) enforces semantic consistency for the same anchor region under varying contexts to mitigate context-induced feature drift; 2) Structure-Guided Semantic Refinement (SGSR) leverages spatial affinities from a frozen visual foundation model to refine CLIP text-patch responses, producing text-conditioned dense supervision with improved spatial coherence and concept-level alignment. Experiments on video instance segmentation, region classification, semantic segmentation, and object detection show consistent gains over the baseline methods, demonstrating the effectiveness and generalization capability of the proposed SSCL. Our code and models will be made publicly available.

Wearable motion sensing provides a continuous and scalable window into human behavior and health, making it a natural fit for foundation models, yet its pretraining and scaling principles remain poorly understood. Prior work studies isolated design choices, such as sensor placement or sampling frequency, often under fixed settings and narrow downstream tasks that fail to capture real-world sensing diversity. We introduce Inertia-1, a fully open exploration of wearable motion foundation models. Using massive corpora of accelerometer data from global sources spanning more than 18.2M hours, we build a controlled framework for studying the full lifecycle of wearable motion foundation models, covering data choices such as sensor modality, device placement, sampling rate, window length; model choices such as architectures and model size; and training choices such as pretraining objective and data scale. Extensive evaluations across 15 datasets spanning human activity recognition, freezing-of-gait detection, and disease prediction reveal intriguing findings for building motion foundation models that generalize across tasks and sensing conditions. Collectively, Inertia-1 not only presents state-of-the-art recipes for diverse downstream tasks, but also serves as a comprehensive, practical, and open cookbook for wearable motion representation learning.

Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems must be extended from one industrial component to another over time. In our setting, continual industrial anomaly detection is framed as a task-incremental problem: each component defines a task, while the learning objective remains semi-supervised. The goal extends beyond conventional anomaly detection, since models should preserve their performance on all components as they are sequentially exposed to new components. This framing is especially relevant because many existing industrial pipelines either assume fully supervised defect labels or study only isolated datasets, leaving open the question of how semi-supervised anomaly detectors behave across a sequence of heterogeneous components. We address this gap by organizing the benchmark as a sequence of related component-level tasks and by defining multiple scenarios, including random, easy-to-hard, and hard-to-easy. In addition to single-dataset continual scenarios, we propose challenging scenarios spanning across multiple datasets. We further evaluate continual performance from two complementary perspectives: image-level anomaly detection and pixel-level anomaly localization. By recasting industrial anomaly detection as a continual benchmark rather than a static train/test problem, our work establishes a concrete path toward studying knowledge retention, adaptation, and generalization in evolving industrial environments.

Fluorescence spot-detection benchmarks in biology and medicine often rely on reference annotations that are incomplete, ambiguous, or inconsistently defined. In such settings, detector performance is not determined solely by model predictions; it can also depend on which faint or ambiguous signals are counted as reference positives. We formulate this issue as a reference-audit problem: instead of treating the evaluation reference as fixed ground truth, we test whether benchmark outcomes remain stable under plausible changes in reference definition and annotation completeness. Using the public FISH_spots dataset, a benchmark for fluorescence in situ hybridization (FISH) spot detection, we construct Raw, Lenient, and Strict audit references and evaluate diverse classical and learning-based detectors under a unified point-matching protocol. We further apply annotation-retention stress tests that simulate random and structured missing-label mechanisms. Across these audits, changing only the evaluation reference can alter measured F1 scores and model rankings, including the selected top-1 detector, even when aggregate rank correlation remains high. These findings suggest that model selection in fluorescence spot-detection benchmarks should be tested across plausible reference definitions, rather than based on a single reference annotation set.

Open-vocabulary indoor scene synthesis aims to generate plausible layouts from arbitrary user instructions while ensuring physical feasibility and semantic consistency. Existing methods directly infer spatial relations between objects from instructions and solve for layouts based on predefined rules, achieving progress in physical feasibility. However, they often struggle to precisely align with complex instructions due to insufficient comprehension of spatial relations and limited capability to translate them into layouts. In this paper, we propose a Hierarchical Reasoning and Reflection agent framework (HierRR), which leverages a relational hierarchy to enable multi-granularity spatial relation comprehension and attributable layout reasoning, improving semantic consistency while maintaining physical feasibility. Specifically, HierRR introduces the spatial transformation chain‑of‑thought reasoning module that explicitly models the mapping from spatial relations to executable geometric placements, mitigating semantic drift during reasoning. Furthermore, the semantic- and vision-guided reflection module is employed to iteratively refine spatial relations and layout reasoning from local to global along the hierarchy, effectively bridging the gap between language instructions and geometric layouts. Extensive comparative experiments and ablation studies demonstrate that HierRR generates more plausible and semantically consistent indoor scene layouts than existing methods, while generalizing effectively to complex scenes with numerous objects and diverse spatial constraints.

Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tasks such as feature implementation, bug fixing, and competitive programming. Open-ended coding remains a weak spot for LLMs, largely because open-ended training problems are scarce and expensive to construct. Our goal is to synthesize open-ended coding problems at scale to train stronger LLM coders. We introduce FrontierSmith, an automated system for iteratively evolving open-ended problems from existing closed-ended coding tasks. Starting from competitive programming problems, FrontierSmith generates candidate open-ended variants by changing the problems’ goals, restricting outputs, and generalizing inputs. It then uses a quantitative idea divergence metric to select problems that elicit genuinely diverse approaches from different solvers. Agents then generate test cases and verifiers for the surviving candidates, and these problems then join the seed pool for the next round. On two open-ended coding benchmarks, training on our synthesized data yields substantial gains over the base models: Qwen3.5-9B improves by +8.82 score on FrontierCS and +306.36 (Elo-rating-based performance) on ALE-bench; Qwen3.5-27B improves by +12.12 and +309.12, respectively. Moreover, FrontierSmith-generated problems elicit long-horizon code-agent behavior comparable to human-curated open-ended tasks, causing agents to spend substantially more turns and tokens than on closed-ended problems.

Multimodal large language models (MLLMs) are increasingly used for explainable image forgery analysis, where a shared decoder is expected to predict authenticity, localize evidence, and generate natural-language explanations. Existing methods typically rely on final decoder states, leaving open whether these late, language-oriented representations preserve localized manipulation cues. We test this assumption with a token- and layer-wise probing diagnostic that uses pixel-level masks only for analysis to separate tampered-region and authentic-region vision tokens. Across three MLLM-based forgery backbones and multiple manipulation types, we identify a mid-layer forgery evidence peak: tampered-region tokens are most linearly separable at upper-middle decoder layers and become less separable afterward, whereas authentic-region tokens continue to strengthen. This region-conditioned asymmetry suggests competition between localized manipulation cues and late semantic aggregation. Motivated by this diagnostic, we propose Gated Forensic Memory (\method), a lightweight evidence relay that reads from the diagnosed peak layer and writes re-encoded vision-token states back to late vision-token states through a zero-initialized gated residual. \method keeps the LLM frozen, adds no extra vision tokens, and requires no pixel-level training supervision. Across three backbones and seven benchmarks, \method improves authenticity prediction, explanation quality, and spatial alignment without mask supervision; the best read layer matches the diagnosed peak, turning the diagnostic into a practical layer-selection criterion.

Cross-domain open-vocabulary learning poses a unique and underexplored challenge, requiring models to generalize across both domain shifts and category shifts. To tackle this, we propose MAP: a parameter-efficient Multi-source domain-Adaptive Prompt tuning framework that leverages multiple labeled source domains to improve learning in novel, unlabeled target domains with unseen categories. MAP consists of two key components: Multi-Source Prompt Learning (MSPL) and Unsupervised Target Prompt Learning (UTPL). MSPL disentangles domain-invariant category semantics from domain-specific visual patterns by jointly learning shared and domain-aware prompts. UTPL enhances generalization in the unlabeled target domain by enforcing prediction consistency under text-guided style augmentations, introducing a novel entropy-minimization objective without relying on pseudo-labels. Together, these components enable effective alignment of visual and textual representations across both domains and categories. In addition, we present a theoretical analysis of the proposed prompts, examining their behavior through the lenses of fidelity and distinction. Extensive experiments on challenging CDOV benchmarks demonstrate that MAP achieves state-of-the-art performance with significantly fewer additional parameters.

Recent DETR-based tiny object detectors adopt dynamic-query mechanisms to handle density imbalance. However, existing designs entangle two effects: how many and which queries are decoded. This entanglement obscures what actually drives the gains, thereby hindering principled dynamic-query design; additional learned predictors or heuristic filtering steps also introduce extra overhead and inevitable decision errors, which can undermine practicality and final gains. In this paper, we disentangle these effects and show that dynamic query primarily acts as budgeting---allocating decoder query capacity according to object density. Motivated by this view, we formulate a monotonic and conservative budgeting principle and propose a budgeting-based DETR (BUTR), a streamlined framework that implements dynamic query by using a lightweight, training-free budgeter to estimate an input-dependent query budget $K(x)$ from encoder confidences. BUTR applies standard Top-$K(x)$ query initialization, removing heuristic filtering, learned components, and complex selection logic. On AI-TOD-v2 and VisDrone, BUTR shows clearer density-aligned query allocation behavior than prior dynamic-query detectors and improves AP by more than 0.5, while using about half as many queries with fewer parameters and GFLOPs, requiring over 2$\times$ less training time, and delivering 4$\times$ faster inference. Additional COCO results further indicate its applicability across a broad spectrum of detection scenarios.

Open-vocabulary 3D Gaussian segmentation typically binds semantic features to per-Gaussian state via per-scene gradient training (1–4 h) or the more recent closed-form distillation. Both commit each primitive to a single feature; therefore, boundary Gaussians spanning multiple 2D instances are forced into a hard decision that degrades retrieval accuracy and yields unstable mask boundaries at render time. We present DIGS, a training-free framework with two stages, class-agnostic map- ping and multi-modal encoding, that decouples instance discovery from linguistic naming. At the Gaussian level, our distribution-informed representation maintains an explicit top-M label distribution per primitive, updated by a tempered Dirichlet rule; preservation of multi-peaked posteriors at boundary primitives allows a late-argmax renderer to recover clean instance boundaries. A bidirectional state machine aggregates these distributions into a persistent class-agnostic instance ontology. The instance-level encoding stage then fuses the CLIP visual embedding of each instance’s isolated 3D footprint with the CLIP-text embedding of an LLM-generated attribute description, disambiguating visually similar instances. DIGS maps a scene in 1–3 minutes on a single A100 and serves 3D-native retrieval in 5.4 ms, reaching 71.95% mIoU on LERF-OVS, surpassing the strongest training-free baseline SFS and the strongest training-based baseline LaGa, and setting new state-of-the-art performance on LERF-Mask (91.71%) and 3D-OVS(96.45%). Codes are at: https://anonymous.4open.science/r/DIGS-C4EF.

MLLM-based detection has shown promising end-to-end detection ability in recent years, but its generated detection results still lack reliable confidence for evaluation and interpretation. Since detection data itself do not provide human-annotated confidence labels, existing MLLM-based detection methods mostly obtain confidence implicitly from prompted self-assessment, generation probabilities, or external proxy scores. While these scores are available for evaluation, they are not explicitly connected with the actual detection quality. Inspired by conventional detectors that optimize confidence through classification, objectness, or quality estimation branches, we formulate confidence in MLLM-based detection as an explicit modeling target to be learned, optimized and calibrated. Specifically, we present ConfDet, a practical confidence framework that first uses discrete confidence tokens based supervised fine-tuning to enable stable and parsable confidence generation, then applies GRPO with detection and separation joint rewards to optimize confidence score separation and preserve detection quality, and finally performs condition-aware post-hoc calibration to align confidence with empirical accuracy. Experiments across different in-distribution and out-of-distribution benchmarks show that, ConfDet greatly improves AP through better confidence ranking and reduces confidence miscalibration across benchmarks, providing a reliable confidence for MLLM-based detection.

Out-of-Distribution (OOD) detection requires sensitivity to subtle shifts without overreacting to natural In-Distribution (ID) diversity. However, from the viewpoint of detection granularity, global representation inevitably suppresses local OOD cues, while patch-based methods are unstable due to entangled spurious-correlation and noise. And neither of them is effective in detecting compositional OODs composed of valid ID components. Inspired by recognition-by-components theory, we present a post-hoc Component-Based OOD Detection (CoOD) framework that addresses the existing limitations by decomposing inputs into functional components. To instantiate CoOD, we derive Component Shift Score (CSS) to detect local appearance shifts, and Compositional Consistency Score (CCS) to identify cross-component compositional inconsistencies. Empirically, CoOD achieves consistent improvements on both coarse- and fine-grained OOD detection.

Chemical reaction datasets such as USPTO suffer from substantial incompleteness, frequently missing byproducts, co-reactants, and stoichiometric coefficients. This limits their applicability and reliability in downstream applications. Here, we introduce CompleteRXN, a large-scale supervised benchmark for reaction completion under realistic missing-data conditions. We construct a dataset of aligned incomplete and atom-balanced reactions by mapping USPTO records to curated mechanistic reactions. We evaluate representative baselines, including a novel encoder-decoder reaction completion model with constrained decoding, the Constrained Reaction Balancer (CRB), and a recent algorithmic method, SynRBL. On our CompleteRXN benchmark, the CRB achieves high performance across splits of increasing difficulty, reaching 99.20% equivalence accuracy on the random split and 91.12% on the extreme out-of-distribution split. SynRBL produces many balanced and chemically plausible completions, but with lower accuracy on the benchmark test splits. Across all methods, performance degrades with increasing incompleteness. We observe a substantial drop when evaluating on reactions outside the benchmark (full uncurated USPTO), highlighting the gap between benchmark performance and practical robustness and motivating future work.

Open-vocabulary object detection (OVD) has made significant progress, enabling detectors to generalize from seen to unseen categories. However, real-world category spaces continually evolve, and existing OVD models still struggle with newly emerging concepts, while repeated full retraining is prohibitively expensive. To this end, we introduce a new task setting, termed Continual OVD with Novel Concept Injection (COVD), where models sequentially learn incoming novel concept groups while preserving prior concepts and original open-vocabulary knowledge, along with a new benchmark, Novel-114. Our key observation is that pretrained visual encoders often already perceive and represent many novel concepts, and the main bottleneck lies in the lack of stable semantic alignment between visual representations and textual concepts. Based on this, we propose NoIn-Det, an efficient continual injection framework without additional parameters. NoIn-Det freezes the visual encoder, preserves the text representation space using only texts of common concepts and previously injected concepts, and injects novel concepts by updating only a small subset of text-branch parameters beneficial to novel concept learning. Extensive experiments show that NoIn-Det effectively learns novel concepts, preserves old knowledge, and consistently outperforms existing continual learning methods for VLMs without introducing additional parameters.

Open-set 3D macromolecule detection in cryogenic electron tomography eliminates the need for target-specific model retraining. However, strict VRAM constraints prohibit processing an entire 3D tomogram, forcing current methods to rely on slow sliding-window inference over extracted subvolumes. To overcome this, we propose FullTilt, an end-to-end framework that redefines 3D detection by operating directly on aligned 2D tilt-series. Because a tilt-series contains significantly fewer images than slices in a reconstructed tomogram, FullTilt eliminates redundant volumetric computation, accelerating inference by orders of magnitude. To process the entire tilt-series simultaneously, we introduce a tilt-series encoder to efficiently fuse cross-view information. We further propose a multiclass visual prompt encoder for flexible prompting, a tilt-aware query initializer to effectively anchor 3D queries, and an auxiliary geometric primitives module to enhance the model's understanding of multi-view geometry while improving robustness to adverse imaging artifacts. Extensive evaluations on three real-world datasets demonstrate that FullTilt achieves state-of-the-art zero-shot performance while drastically reducing runtime and VRAM requirements, paving the way for rapid, large-scale visual proteomics analysis. All code and data will be publicly available upon publication.