NeurIPS 2026 Papers

Goal recognition is the task of inferring an agent's intended goal from its observed behavior. Existing work has largely focused on high-level symbolic recognition, abstracting away the continuous motion through which agents reach their goal. Methods that do reason over real-world trajectories, rely on an explicit dynamics model that is hard to acquire and rarely transfers across settings. Neither approach explicitly learns how agents move toward each candidate goal, limiting accuracy on ambiguous trajectory prefixes where such knowledge is most needed. We present WoRD (Waypoint Recognition with Diffusion), which uses a learned trajectory prior to infer the agent's goal from partial observations. At each step, WoRD runs one warm-started denoising chain per candidate goal, steers each chain toward the observed trajectory through a closed-form log-likelihood gradient, and reweights the resulting samples by Bayes' rule into a calibrated posterior over goals. We evaluate WoRD on simulated robot settings, pedestrian trajectories, and vehicle trajectories using four metrics targeting distinct failure modes. WoRD is the only method that improves all four metrics jointly, including the safety-critical confident-and-wrong rate. WoRD runs under a single inference procedure and preserves calibrated multimodal. beliefs in complex, ambiguous environments where filter- and classifier-based observers collapse to a single mode or grow overconfident.

Transformer-based time series forecasting models often suffer from block-wise attention collapse, where temporal smoothness and cross-variable correlations concentrate attention locally and restrict global modeling capacity. We argue that this collapse is partly a pre-attention representation-conditioning problem: attention logits inherit the spectral concentration and low effective rank of the input representation. We propose Widely Linear Frequency-Domain (WILD) conditioning, a lightweight, model-agnostic module inserted before self-attention. WILD maps inputs to the frequency domain, applies data-dependent spectral preconditioning to redistribute over-dominant frequency energy, and then uses asymmetric real/imaginary projections to realize a widely linear transform beyond fixed time-domain channel mixing. The preconditioning step is essential: it constructs the usable normalized frequency basis on which the widely linear branch can operate. Thus, WILD uses frequency-domain modeling to condition an existing attention layer, not to replace it with a new forecasting architecture. Extensive experiments on eight benchmark datasets show positive gains on 105 of 112 model-dataset-metric configurations across seven backbones. Attention diagnostics show that WILD substantially increases effective rank over the baseline, and a W_r=W_i ablation identifies spectral preconditioning as the primary mechanism, with widely linear mixing providing a secondary, dataset-dependent gain.

Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft tissue contrast and anatomical detail. However, its high cost and operational burden make it invasive and difficult to obtain at scale. Ultrasound (US), in contrast, is cheap, safe, and routinely acquired, and as a result it has produced substantially larger datasets and a growing ecosystem of pretrained models. This asymmetry raises a natural question: Can we teach a US-only model to understand fetal MRI from only a limited set of examples? The standard recipe, training a foundation model on subject-to-subject paired MRI-US scans, is not viable since no such paired fetal dataset is publicly available. In this paper we address this gap with Weakly Aligned Spatiotemporal Pairs (WASP), a framework that formulates cross-modal correspondence as an entropic Optimal Transport problem driven by clinical metadata — in particular, Gestational Age (GA) and diagnostic planes — enabling the fitting of a lightweight alignment head that lifts MRI representations into the US latent space, without fine-tuning the backbone. Empirically, WASP yields its largest gains when MRI is unseen by the model during pretraining (on USFM, GA estimation error drops from $21.9$ to $16.3$ days and standard plane classification accuracy climbs from 61.9\% to 74.5\%), while still providing refining improvements for backbones pretrained on both modalities (e.g., BioMedParse GA estimation error from $6.5$ to $5.6$ days and SAM-Med2d plane accuracy from $83.3$\% to $88.1$\%).

We introduce a new task and benchmark dataset, VisEditBench, for understanding visual instructions in vector-format diagram editing. While recent advances have made progress in generating vector graphics from text or images, editing such diagrams remains relatively underexplored. Moreover, existing approaches predominantly rely on textual instructions, which are often unintuitive for specifying edits in structured visual content, particularly when users need to identify specific elements or indicate their spatial positions in a diagram. We propose visual instructions as a more intuitive interface for diagram editing, and construct a dataset of 694 samples with human-curated annotations overlaid on TikZ diagrams. The dataset covers multiple edit types as well as instructions with multiple editing operations. We evaluate 32 recent state-of-the-art multimodal models and find consistent performance degradation, particularly for edits involving global layout or diagram structure, compared to local operations such as element addition or deletion. Furthermore, instructions that include multiple editing operations tend to result in lower performance than single-edit cases. Even recent models struggle to achieve consistently accurate edits, highlighting the inherent difficulty of the task. VisEditBench provides a valuable benchmark for advancing research on visually grounded diagram editing and structured visual understanding.

Protein language models (pLMs) have shown strong potential for zero-shot prediction of missense variant effects, yet systematic benchmarking on viral proteins remains limited, a critical gap given the need for proactive tools that can anticipate emerging mutations ahead of experimental validation. Here we introduce ViroGym, a comprehensive benchmark evaluating pLMs across three tasks: 79 deep mutational scanning (DMS) assays covering eukaryotic viruses with 552,065 mutated sequences across 7 phenotypic readouts, 21 influenza neutralisation tasks, and a real-world pandemic prediction task for SARS-CoV-2. We benchmark well-established pLMs on fitness landscapes, antigenic diversity, and pandemic forecasting, and find that the ProGen2 family consistently achieves the strongest performance across all three tasks. Crucially, DMS and neutralisation performance reliably identifies models that generalise to real-world emergence, even though the mutation sets they surface barely overlap, revealing that complementary in vitro benchmarks capture the evolutionary constraints needed for real-world mutation forecasting.

Viewpoint dictates the photo composition and plays a vital role in AI photography. Existing 3D viewpoint recommendation methods are often limited in viewpoint adjustments, which is mainly due to their lack of high-quality training data with accurate and large 3D view changes. Ideally, the training data should contain suboptimal images and expert-selected optimal viewpoints, but this can be very expensive to curate. To solve this, we build an automatically-generated 3D viewpoint recommendation dataset from expert photos. Specifically, we first consider these expert photos as optimal and outpaint them to provide more information on scene layouts. Next, we explicitly reconstruct the 3D scene. We then render suboptimal images from random viewpoints. Lastly, we introduce a hierarchical data filtering scheme and acquire high-quality data pairs for our ViewRecDB-100K dataset. On top of this, we further introduce a view recommendation model, ViewRecNet, that can predict expert viewpoints from any suboptimal image inputs using a ray-based dense supervision. The proposed method achieves strong results both quantitatively and qualitatively, supported by LLM-based viewpoint preference and human studies. Data and code are available at https://github.com/anonymous1-submission/ViewRec3D.git.

Conventional text-based person re-identification (ReID) approaches mainly rely on visible-spectrum images captured from ground-level viewpoints. However, with the increasing prevalence of UAVs and multispectral imaging systems, pedestrians can be captured from both ground and aerial viewpoints, and different spectral sensors provide complementary cues that reveal diverse pedestrian characteristics. As a result, conventional text-based ReID methods become inadequate for such retrieval scenarios. To address this limitation, we introduce a novel task termed Text-based Multispectral Aerial-Ground Person Re-Identification (TMAG-ReID), which aims to retrieve pedestrian images across aerial and ground viewpoints under multispectral scenarios using textual descriptions. To facilitate this task, we construct the T-MSAG dataset, which consists of multispectral pedestrian images captured from aerial and ground viewpoints, paired with corresponding textual descriptions. Furthermore, we propose a novel View-Spectral Reconciliation Learning framework (VSRL) that leverages textual guidance to interweave discriminative semantics across distinct views and spectral modalities. This fusion yields robust cross-view and cross-modal pedestrian features, thereby enabling high-performance retrieval. Extensive experiments on the T-MSAG dataset demonstrate the effectiveness of VSRL.

Test-time scaling is an important mechanism for improving large language models, especially on tasks with deterministic verifiers. Code translation is a canonical example: the source program constrains valid outputs, while compilers, type check- ers, and behavioral checks provide exact pass/fail feedback. Existing approaches typically apply these verifiers only after generation, which is inefficient because early errors corrupt the autoregressive context and are rarely corrected later. We introduce Decoding Time Verification (DTV), a framework that integrates deter- ministic program verification directly into the decoding loop. DTV interleaves generation with verifier calls under a state-machine controller that enforces valid prefixes, using structural-boundary checks and structure-aware rollback to prevent error propagation while reducing wasted tokens. We evaluate DTV on C-to-Rust and JavaScript-to-TypeScript translation. Using Qwen3-4B as the primary genera- tor under matched token budgets, DTV improves pass rates from 72.3% to 82.0% on C-to-Rust and from 33.3% to 46.0% on JavaScript-to-TypeScript relative to matched self-refinement baselines, while using fewer tokens per case; the same trend largely transfers to Gemma-4-E4B. In the evaluated cost-matched grid, DTV achieves a more favorable pass-rate-cost tradeoff than post-hoc verification or sampling-based scaling. These results show that verifier-guided decoding is an effective use of inference-time compute for code translation.

Flow matching trains a continuum of velocity regression problems but usually reports a loss averaged over interpolation time, obscuring which regions are limited by intrinsic ambiguity and which remain data-limited. We introduce the Velocity Ambiguity Profile (VAP), $\mathcal{A}_v(t)$, the time-local Bayes-risk floor of velocity prediction, and decompose time-resolved loss as $L_N(t)=\mathcal{A}_v(t)+R(t;N)$, where only the excess term $R(t;N)$ is reducible by more data or a different estimator. We prove two statistical benchmarks. In an intrinsic local-regression benchmark, $\mathcal{A}_v(t)$ enters the time-dependent noise factor of the standard nonparametric rate. In separated isotropic Gaussian mixtures, the Bayes velocity becomes locally affine, and a label-oracle residual-regression benchmark has parametric excess-risk scale $K\mathcal{A}_v(t)/N$, matched within the same separated residual Gaussian-location subproblem. Controlled Gaussian-mixture experiments validate this diagnostic through a dense transition sweep, calibrated oracle-to-blind and empirical-proxy audits, and a local controlled time-sampler stress test in which a frozen reducible-error proxy reduces reducible loss while cross-cell sensitivity delimits aggressive sampling. VAP turns an averaged flow-matching objective into a time-resolved diagnostic for locating ambiguity-limited and data-limited regions.

Pretrained diffusion models increasingly serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution. The teacher gradients these pipelines consume are Monte Carlo expectations over noise levels and Gaussian noise; their estimator variance dominates compute cost because each draw requires expensive upstream work, such as rendering, simulation, or encoding. We introduce CARV, a compute-aware variance-accounting framework that motivates a hierarchical Monte Carlo estimator: amortize the expensive upstream computation over cheap diffusion-noise resamples, sharpened by timestep importance sampling and a stratified inverse-CDF construction. Across diffusion-guided workloads, we obtain 2–3× effective compute multipliers, most from amortized reuse and approximately 25% additional gain from importance sampling plus stratification, without changing the objective. We also map regimes where these gains translate into improved downstream metrics versus regimes where they do not, such as DMD.

Activation steering can change an LLM's behavior without updating its weights, but a direction intended to change safety or value stance often also shifts topic, phrasing, and task content. We study a narrower and directly testable question: can a frozen residual-stream state be equipped with an editable interface that separates semantic content from value framing well enough to reduce this collateral damage? We propose a lightweight dual-code interface with a one-way semantic$\rightarrow$value path. Semantic information may ground value recognition, while stop-gradient gating, topic de-confounding, and swap consistency discourage value supervision from rewriting the semantic factor. At inference time, we edit only the value code through a residual delta update. Across two instruction-tuned backbones, the learned interface improves the steering--damage trade-off over dense and sparse steering baselines: it preserves semantics better, reduces topic leakage and benign false refusals, and remains stable under noise, distribution shift, and recomposer controls.

Flow-based vision-language-action (VLA) policies provide a compelling recipe for robot control: they inherit broad behavioral priors from large-scale imitation and generate smooth, temporally extended continuous action chunks. The same properties, however, make offline post-training delicate. Mixed offline robot datasets contain expert behavior as well as partial completions and failures, requiring improvement without drifting far from the data support. An offline critic can in principle provide useful directions, but actor-side critic optimization may suffer from extrapolation and push high-dimensional action chunks off-support. In contrast, advantage-conditioned or reweighting-based methods are more stable, but tend to be conservative and restricted to actions already present in the dataset. We propose VISTA, a post-training framework that turns value estimates into support-anchored distillation targets for pretrained flow policies. VISTA queries a critic only at dataset actions, computes a normalized action-gradient direction, and uses this bounded direction to shift clean-action endpoints and velocity targets. A frozen flow teacher preserves the pretrained action prior, while a behavior-cloning anchor keeps the adapted targets close to the data distribution. The shaped supervision is distilled into a time-indexed action denoising (TAD) student, which predicts time-indexed clean-action estimates with a single network evaluation and obtains the executed action by an analytic final readout. We treat the induced target shift as conservative local guidance rather than a global policy-improvement guarantee. Across five BEHAVIOR-1K simulation tasks and five real-world bimanual manipulation tasks, VISTA achieves the best average success rate in our evaluation against behavior cloning (BC), advantage-conditioned BC, IDQL, and flow Q-learning, while reducing action-expert computation by $15\times$ (and end-to-end policy inference by $1.8\times$) relative to the $20$-step flow teacher.

Voice-based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic languages. We present Project VAANI, a large-scale multimodal dataset designed to represent India’s linguistic landscape across 165 districts. Speech data is collected using image-based prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The dataset undergoes a rigorous multi-stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of speech, and 2,043 hours of transcribed audio spanning 105 languages from 28 states and 3 union territories. Many of these languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive speech technology. The dataset enables the development of robust, multilingual, and multimodal models, and supports research in speech recognition, language understanding, and cross-modal learning for underrepresented languages.

Modeling ultra-long user behavior sequences is an essential task for capturing evolving preferences in modern recommender systems, and this research direction has contributed solid gains in the past several years. However, recommendation systems always serve enormous traffic, and extending user sequences sharply increases computational cost, which creates a difficult trade-off between efficiency and effectiveness. To scale to longer user sequences while keeping lightweight serving computation, existing works can be divided into two paradigms: (1) $\textit{Search-based Top-$K$ selection}$, which constructs an $\textbf{item-specific}$ subsequence for each candidate item to avoid facing the ultra-long sequence directly; and (2) $\textit{pre-trained user-interest compression}$, which maps an ultra-long user sequence into a small group of $\textbf{item-agnostic}$ user interest memories so that the online model can perceive user long-term interests via this highly compressed dense memory. Besides the two technical routes (totally item-specific or item-agnostic), we argue that there exists an intermediate path not well explored: preserving partial relevance between the user sequence and the target item, while exposing only limited signals to guide the direction of interest compression. This design does not pursue item-specific user interest compression, but seeks semantic-group shared general user interest memory according to item attributes, where semantically similar items share the same compressed user interest memory. Motivated by this, we propose $\textbf{UxSID}$, a novel framework that bridges this gap by facilitating a target item semantic-aware interaction between $\textbf{U}$ser histories and candidate $\textbf{S}$emantic $\textbf{ID}$s (SIDs). Specifically, UxSID employs a dual-level attention strategy: it first extracts item-agnostic user interests from raw sequences, and then performs a semantic-specific query over global behaviors and those agnostic interests to generate semantic-specific preferences. By adopting this end-to-end architecture, UxSID generates offline embeddings that balance computational parsimony with target items' semantic awareness, while strictly preserving parity with online inference in constant time. Extensive public benchmarks and large-scale A/B tests demonstrate that UxSID achieves state-of-the-art performance, driving a 0.337\% revenue lift in advertising. Open source link: $\url{https://anonymous.4open.science/r/UxSID/}$

Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks. Existing safety techniques---including external guardrails, inference-time guidance, and post-training alignment---each face limitations in balancing safety, utility, and controllability. In this work, we propose UpSafe℃, a unified framework for enhancing LLM safety through safety-aware upcycling. Our approach first identifies safety-critical layers and upcycles them into a sparse Mixture-of-Experts (MoE) structure, where the router acts as a soft guardrail that selectively activates original MLPs and added safety experts. We further introduce a two-stage SFT strategy to strengthen safety discrimination while preserving general capabilities. To enable flexible control at inference time, we introduce a safety temperature mechanism, allowing dynamic adjustment of the trade-off between safety and utility. Experiments across multiple benchmarks, base model, and model scales demonstrate that UpSafe℃ achieves robust safety improvements against harmful and jailbreak inputs, while maintaining competitive performance on general tasks. Moreover, analysis shows that safety temperature provides fine-grained inference-time control that achieves controllable trade-off between utility and safety. Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control. Code: https://anonymous.4open.science/r/UpCycle-70B3.

In reinforcement learning from limited feedback (RLLF), only a small fraction of an offline dataset can be labeled with rewards, and the central question is *which* samples should be labeled to learn a strong policy from the resulting partially labeled dataset. Prior work formalized this as a reward-selection problem by focusing on the selection stage while treating downstream policy learning as a black box, in a regime where queried rewards are not retained for reward-model training. We instead study the retained-label setting, where queried rewards can be stored and used to fit a reward model before policy learning. We bound the suboptimality of the learned policy by two sources of error: one from offline RL on an offline dataset, and one from reward-model uncertainty. Since reward selection cannot change the offline dataset, the limited labeling budget must be used to strategically reduce reward uncertainty. Motivated by RLLF's observation that useful rewards tend to keep the agent on high-return trajectories, we propose successor-guided uncertainty reduction (SURE), which uses successor features to select rewards that are both reachable to high-valued states and uncertainty-reducing. Theoretically, we derive SURE from a bound-induced design objective and characterize its exact one-step marginal gain. Empirically, SURE reaches near full-feedback performance with few reward labels across a variety of domains, yielding a strong method for feedback-efficient reinforcement learning.

The generalized orthogonal Procrustes problem (GOPP) aims to recover rigid transformations that best align multiple point clouds and has broad applications in 3D geometry, computer vision, and biomedicine. Despite its importance, the problem is challenging due to the nonconvex orthogonality constraints. Fortunately, the generalized power method (GPM) has proven highly effective, theoretically guaranteed to converge to the global least squares (LS) estimator under reasonable conditions. Yet, the statistical inference of this global LS estimator remains largely an open problem. Specifically, the absence of an exact second-order analytic expansion prevents rigorous uncertainty quantification (UQ), which is vital for constructing confidence regions and evaluating estimator reliability. While recent UQ frameworks developed for orthogonal group synchronization offer a potential paradigm, they severely lack universality, often failing to accommodate the complex anisotropic distortions inherent in GOPP shape matrices. To bridge this gap, we propose a more general theoretical framework with which we successfully derive the exact second-order analytic expansion of the GOPP global LS estimator under additive Gaussian noise. Leveraging this theoretical foundation, we further construct confidence regions. Finally, extensive numerical experiments empirically validate the exactness of our theoretical expansion and demonstrate the robust coverage of the proposed confidence regions across varying conditions.

Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance. However, existing uncertainty metrics have practical constraints, such as being designed only for specific modalities, reliant on external tools, or computationally expensive. We introduce UMPIRE, a training-free uncertainty quantification framework for MLLMs that works efficiently across various input and output modalities without external tools, relying only on the models' own internal modality features. UMPIRE computes the incoherence-adjusted semantic volume of sampled MLLM responses for a given task instance, effectively capturing both the global semantic diversity of samples and the local incoherence of responses based on internal model confidence. We provide theoretical analysis motivating UMPIRE's design. Extensive experiments show that UMPIRE consistently outperforms baseline metrics in error detection and uncertainty calibration across image, audio, and video-text benchmarks, including adversarial and out-of-distribution settings. We also demonstrate UMPIRE’s generalization to non-text output tasks, including image and audio generation.

Large Language Diffusion Models (LLDMs) are emerging as an alternative to autoregressive models, offering faster inference through higher parallelism. Similar to autoregressive LLMs, they remain prone to hallucinations, making reliable uncertainty quantification (UQ) crucial for safe deployment. However, existing UQ methods are fundamentally misaligned with this new paradigm: they assume autoregressive factorization or use expensive multiple sampling, negating the efficiency of LLDMs. In this work, we present the first systematic study of UQ for LLDMs and propose lightweight, zero-shot uncertainty signals derived from the iterative denoising process, leveraging intermediate generations, token remasking dynamics, and denoising complexity. We further adapt a state-of-the-art UQ method to LLDMs by combining masked diffusion likelihoods with trajectory-based semantic dissimilarity. We provide theoretical grounding, proving that expected trajectory dissimilarity bounds the masked diffusion training objective. Comprehensive experiments across three tasks, eight datasets, and two models show that our method achieves a great cost-performance trade-off: it approaches the strongest sampling-based baselines while incurring up to one hundred times lower computational overhead. Our work demonstrates that LLDMs can deliver both fast inference and reliable hallucination detection simultaneously.