NeurIPS 2026 论文列表

Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different components to address each part, making them well-suited for problems with inherent compositional structure. However, existing H-GP frameworks typically employ one-way information sharing — either top-down or bottom-up — which limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), which establishes continuous two-way communication. BIF retains the modular structure of hierarchical models — the parent conditions its own posterior on child summaries, treating them as structured priors — while introducing top-down feedback to softly decompose environment observations from the parent into sub-responses. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. We prove analytically the regret of a GP with a learned kernel scales linearly with the mismatch to the true kernel, tightening in the hierarchical case to the sum of child-level errors. Ablation shows removing the downward pathway collapses child $R^2$ by up to 58\%. Across synthetic, neurostimulation, and HPO benchmarks, BIF achieves up to 4× higher parent $R^2$ and $\sim$100\% AUC improvement over vanilla GPBO, and outscores all hierarchical state-of-the-art methods on child $R^2$ given the correct acquisition function, while supporting modular child transfer to novel composite tasks.

Large Language Models (LLMs) are increasingly deployed as automated judges to scale supervision for data curation, reinforcement learning (RL), and agentic systems. While existing works have extensively explored bias in pretrained LLMs, the origins of such inherent biases remain largely untraced. We trace these biased tendencies to cognitively biased patterns (e.g., Authority, Bandwagon) latent in training corpora, and expose that such patterns are naturally prevalent in large-scale pretraining corpora yet remain entirely undetectable by existing data cleaning pipelines. To demonstrate that this underexplored threat can be deliberately exploited, we introduce BiasTrojan, a framework that concentrates and injects these naturally occurring bias patterns into training samples via context-aware bias cues and contrastive preference pairs, augmented with counterfeited reasoning chains for efficient injection. Experiments across several LLMs from 7B to 70B on human-preference and fact-related datasets show that mere hundreds of deliberately biased samples suffice to compromise LLMs into biased evaluators, overriding their factual knowledge. The injected biases generalize robustly out-of-domain and persist despite massive continual post-training. These findings reveal that this underexplored latent threat poses far greater risks than commonly recognized: biased LLM judges whose evaluations propagate irreversibly downstream, underscoring the critical need for bias-aware auditing and strict scrutiny of LLM training data.

Large reasoning models (LRMs) generate reasoning traces before producing final answers, yielding strong performance on multi-step reasoning tasks. However, preference alignment for LRMs remains underexplored. The ideal answer-level preference objective marginalizes over reasoning traces, but this trace-marginalized objective is computationally intractable. As a result, practical methods rely on single-trace surrogates, which can induce high gradient variance due to stochastic trace sampling. We cast this challenge as a gradient-estimator design problem and propose Bias-Variance Optimized Preference Optimization (BVPO), which combines a standard trace-based gradient estimator with an empty-trace gradient estimator from a separately sampled empty-trace branch. Under our fixed-prompt, fixed-answer conditional view, the empty-trace branch is deterministic with respect to the trace randomness studied in our analysis. We show that the composite estimator contracts the conditional reasoning-trace-noise component, admits a conditional MSE-optimal mixture, and appears in the estimator-dependent term of a standard biased-SGD bound. Empirically, BVPO improves alignment over baselines on alignment benchmarks, including AlpacaEval 2 and Arena-Hard. Although trained on general conversational data, BVPO also generally preserves reasoning performance on six math reasoning benchmarks. Empirical diagnostics further show that BVPO yields lower total gradient variance during training.

Graph Neural Networks (GNNs) are an important tool for fMRI-based prediction, where empirical functional connectivity matrices serve both as node features and as the basis for graph construction. However, recent studies (and our own experiments) show that these methods often fail to outperform simpler graph-agnostic baselines. We propose a statistical explanation for this failure by formulating connectome prediction as an errors-in-variables (EIV) problem: the true connectome is latent, while the observed covariance or correlation matrix is a noisy finite-time-series estimate. In a linear asymptotic setting, we decompose the effect of graph convolution into three mechanisms: smoothing-induced bias and variance reduction, graph estimation error, and a double-dipping effect arising when the same noisy matrix determines both topology and features. Under a latent community model adapted to this neuroscience problem, we identify a narrow favorable regime in which message passing can improve prediction: the regression target must be community-smooth, and the time-series length must lie in a window between the graph-estimation and raw-noise limits. Simulations validate this decomposition by isolating oracle, independently estimated, and same-sample graphs, while real-data experiments illustrate that standard connectome GNN pipelines often fall outside this regime. Our results clarify when graph convolutions can help brain connectome prediction, and why they often do not.

Operating and maintaining (O&M) large-scale online engine systems (search, recommendation, advertising) demands substantial human effort for release monitoring, alert response, and root cause analysis. While LLM-based agents are a natural fit for these tasks, the deployment bottleneck is not reasoning capability but *orchestration*: selecting, for each operational event, the relevant *data* (metrics, logs, change events) and the applicable *operational knowledge* (handbook rules and practitioner experience). Feeding all signals indiscriminately causes dilution and hallucination, while manually curating the event-to-(data, knowledge) mapping is intractable under dozens of daily releases. We present **Bian Que**, an agentic framework with three contributions: (i) a **unified operational paradigm** abstracting day-to-day O&M into three canonical patterns: release interception, proactive inspection, and alert root cause analysis; (ii) **Flexible Skill Arrangement**, where each Skill specifies which data and knowledge to retrieve for a given business-module context and can be automatically generated and updated by LLMs or iteratively refined through natural-language instructions from on-call engineers; (iii) a **unified self-evolving mechanism** in which one correction signal drives two parallel pathways, case-memory-to-knowledge distillation and targeted Skill refinement. Deployed on the e-commerce search engine of a major short-video platform in China, **Bian Que** reduces alert volume by 75%, achieves 80% root-cause analysis accuracy, and cuts mean time to resolution by over 50%. Our framework achieves 99.0% pass rate on offline evaluations.

Text-to-motion generation and motion-to-text captioning are two fundamental tasks in human motion modeling, both grounded in the same underlying motion-text correspondence. Existing unified approaches mostly rely on autoregressive modeling, which imposes a fixed generation order and is therefore poorly suited to the bidirectional dependencies between language and motion, allowing early prediction errors to persist as fixed context and degrade both temporal coherence and cross-modal consistency. Masked discrete diffusion, which models sequences through iterative bidirectional prediction, offers a natural remedy. We therefore propose BiMoGen (Bidirectional Motion-text Generation), a unified masked discrete diffusion framework for bidirectional motion-text modeling. To stabilize training, we design Decoupled Uni- and Cross-Modal Training, in which masked pretraining first establishes cross-modal correspondence on paired motion-text sequences, after which supervised fine-tuning specializes the model for bidirectional generation. Masked diffusion nonetheless introduces its own source of error, as the model is trained on clean ground-truth context yet encounters self-generated and potentially erroneous context at inference, with errors committed under heavily masked states propagating through subsequent steps. We further introduce Generation-Aware Self-Correction that exposes the model to its own predictions during training and applies correction passes at early sampling steps to revise unreliably committed tokens. Extensive experiments on HumanML3D and KIT-ML demonstrate competitive performance on both tasks, validating the effectiveness of the proposed two-stage training and self-correction designs.

Post-training quantization (PTQ) to FP4 has emerged as a key technique for reducing large language model inference cost, especially on Blackwell GPUs which offer native FP4 tensor core support. Direct PTQ to FP4, however, still leaves a noticeable accuracy gap. Recent work on SVDQuant mitigates the errors introduced through FP4 quantization for diffusion models by adding high-precision low-rank corrections obtained via SVD. However, this high-precision low-rank correction requires many bits per rank, and moreover competes on tensor core utilization with the main FP4 GEMM. We introduce BiLoCo, a correction whose left and right factors are stored as binary ${\{\pm 1\}}$ sign vectors rather than as BF16 numbers. The signed format uses $16{\times}$ less storage per rank-one term, and since the correction now consists of additions/subtractions, this can be run on CUDA cores concurrently with the FP4 tensor-core GEMM. On 4--32B LLMs, BiLoCo matches or improves memory-matched SVDQuant for PTQ. We implement BiLoCo on B200 with a correctness-preserving CUDA schedule that runs end-to-end decode at $1.13$--$1.15\times$ FP4-only, with the residual gap coming from the final output combine rather than from one-bit arithmetic.

In large-scale outdoor LiDAR localization, Scene Coordinate Regression (SCR) achieves sub-meter accuracy, but its deployment in high-precision autonomous driving is hindered by a "last-mile" problem: the point-wise prediction paradigm causes unphysical trajectory jittering, rendering localization kinematically discontinuous despite low mean errors. To bridge the gap between high-precision localization and temporal kinematic consistency, we propose BiLi, a spatiotemporal synergistic relocalization framework. Spatially, BiLi utilizes Implicit Manifold Regularization (ISR) driven by an offline neural implicit field. Formulated as a novel point-to-manifold projection constraint, ISR strictly anchors predicted coordinates to continuous physical zero-level sets. Temporally, to bypass computationally heavy implicit optimization, we distill the teacher's kinematics into a lightweight feed-forward network, compelling it to learn robust, equivariant physical priors. Finally, a Differentiable Robust Manifold Gating mechanism dynamically fuses the global spatial predictions with the temporal kinematic states via adaptive state updating, facilitating efficient real-time deployment. Extensive experiments demonstrate that BiLi fundamentally resolves the "last-mile" challenge. Our method outperforms state-of-the-art approaches by 25% and 65% on the Oxford and NCLT benchmarks, respectively, delivering drift-free, kinematically coherent, and highly accurate real-time localization.

Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and interleaving of foreground and background. This causes traditional detectors and attention mechanisms to be hindered by background, resulting in inefficiency and inaccuracy. To tackle this problem, we propose BiHashFormer, a dual sparsified branch transformer built on hashing. RoI Selector will predict the foreground proportion for top-k selecting target-containing windows. The compression branch processes windows selected and refines attention into HashAttention by discarding half of the key–value pairs, reducing cost and improving efficiency. The compensation branch uses HashMiner to perform low-cost hash searches in the remaining regions, recovering features of missed objects. Experiments confirm that the selection relationship in HashAttention is one-way and reveal the preference of different queries for keys. In experiments on the gigapixel benchmark PANDA, BiHashFormer reduces 37.5\% backbone FLOPs while improving $\text{AP}_{50}$ to 81.0\%. Moreover, this parallel dual-branch structure allows the model to avoid inference delays caused by inter-branch dependencies, while achieving much greater stability than single-branch models, specially with low rates of RoI selection.

Understanding how a prediction model will perform in a new environment before deployment is essential to preventing harm when algorithms inform decision-making. Two common sources of model performance degradation are (i) covariate shift, where the target covariate distribution differs from the source, and (ii) selective labels, where the observability of outcomes depends on historical decisions. We study pre-deployment model evaluation under the joint presence of covariate shift and labeling of outcomes selectively based on observed features. In particular, we present a double machine learning procedure for estimating the target risk of an arbitrary black-box prediction model under a general loss function. We show identification of this estimand under standard assumptions and derive a bias-corrected estimator based on the influence function of the target risk. Finally, we evaluate our estimator through experiments using the eICU electronic health records database, showing that it tracks the true target risk more accurately than methods that address either selective labels or covariate shift alone, as well as baselines that combine standard plug-in approaches.

Diffusion language models (dLLMs) generate text by iteratively denoising masked response positions, exposing hidden states over future response slots before any token is finalized. This creates a detection surface that is absent from standard autoregressive decoding: even when a jailbreak is difficult to identify from the prompt alone, the initial masked response states may already reflect the model's emerging completion. We test this hypothesis on LLaDA-8B-Instruct by training lightweight linear classifiers on two frozen representations: prompt hidden states and pre-decoding masked-response hidden states. Empirically, the two views are complementary: neither classifier strictly dominates the other, and each recovers attacks missed by the other view. We then introduce $\texttt{ReFuse}$ (Representation Fusion), an inference-time detector that fuses prompt and pre-decoding response classifier scores without modifying model weights or the decoding procedure. Across transferred and dLLM-targeted jailbreaks, $\texttt{ReFuse}$ reduces average ASR from 63.29\% for the undefended model to 3.31\%, while keeping average benign refusal on standard utility benchmarks below 1\%. These results suggest that pre-decoding response states provide a complementary safety signal for detecting jailbreaks in dLLMs.

Online bipartite matching, where agents are known in advance but items arrive sequentially and must be irrevocably assigned, is fundamental to problems ranging from ride-sharing to online advertising. When agents belong to classes such as demographic groups or geographic regions, fairness demands equitable treatment across these groups. Recent work introduced class envy-freeness (CEF), a natural extension of the classical fair division notion: an algorithm is $\alpha$-CEF if each class receives value at least an $\alpha$ fraction of what it could extract from any other class's bundle. However, all known algorithms achieving constant-factor CEF guarantees attain utilitarian social welfare (total matching value) of at most $\frac{1}{2}$ times the optimum, far below the $1-\frac{1}{e} \approx 0.632$ achievable without fairness constraints. We resolve the open question of whether fairness necessitates this efficiency loss, by introducing threshold-based algorithms parameterized by $\gamma \in [0,1]$ that equalize allocations across classes until threshold $\gamma$, then maximize efficiency. For divisible matching, this yields simultaneous $(1-e^{-\gamma})$-CEF and $(1 - \frac{e^{\gamma-1}}{\gamma+1})$-USW guarantees; for indivisible matching, $\frac{\gamma}{2}$-CEF with the same USW. Setting $\gamma > 0$ produces the first algorithms beating $\frac{1}{2}$-USW while maintaining constant CEF. We complement this with a novel upper bound construction, proving no non-wasteful $\alpha$-CEF algorithm can exceed $\frac{1 +\alpha - e^{\alpha-1}}{1+\alpha}$-USW and correcting prior bounds that were vacuous for $\alpha < 0.58$. Our upper bound nearly matches our algorithms' performance, giving the first substantive characterization of the price of fairness in online class matching.

Deep learning models achieve high accuracy in signal processing but remain difficult to interpret. Classical matching pursuit and sparse coding offer better transparency but suffer from spectral leakage and grid mismatch when signal frequencies do not align with discrete dictionary atoms. We introduce Continuous Dictionary Pursuit (CDP), a framework for interpretable signal decomposition that learns the parameters of analytical functions directly from data. Unlike traditional methods, CDP optimizes over a continuous manifold of differentiable atoms to resolve the limitations of fixed grids. The algorithm employs a greedy iterative strategy using data-driven priors and gradient-based optimization to isolate individual signal components. We provide a library of parametric atoms that covers periodic, trend, and transient structures, including discontinuous functions modeled through spectral annealing. To ensure a sparse atom set for reconstruction, the framework utilizes a stopping criterion based on a predefined atom budget and residual error convergence. Evaluations on synthetic and real-world datasets show that CDP resolves spectral leakage for the parameterized atom families considered and recovers exact mathematical components. Beyond decomposition, we demonstrate that the extracted atoms can serve as structured curriculum targets for training sequence models, leading to consistent improvements in forecasting accuracy compared to standard end-to-end training. The method offers a glass-box alternative for signal decomposition by providing high reconstruction fidelity alongside direct physical interpretability.

Multinomial logit models (MNLs) are ubiquitous in machine learning, particularly in recommendation systems. While these models are typically evaluated using global metrics over a full catalog of items (such as $\ell_2$-error), practical applications often involve making predictions on small, curated subsets or ``slates'' of items. A model that minimizes global error may nevertheless yield very poor predictions on specific sub-universes. Yet, evaluating performance across all possible subsets remains computationally challenging. In this paper, we present a set of new efficient algorithms to evaluate the small-slate performance of MNL models. Using these tools, we conduct an empirical study comparing standard non-adaptive and adaptive learning algorithms. We demonstrate that while methods that compute the Maximum Likelihood Estimate (MLE) suffer from poor worst-case performance on small slates, specialized adaptive algorithms that dynamically sample difficult subsets can mitigate this issue. Conversely, we find that non-adaptive algorithms often achieve competitive average-case performance; we provide a theoretical model explaining this phenomenon. We conclude with recommendations for practitioners regarding when to utilize adaptive sampling based on the specific robustness requirements of their application.

Reinforcement learning (RL) has emerged as a paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks. A fundamental limitation remains *the curse of diversity collapse*, where the objective formulation and optimization landscape inherently collapse the policy to a Dirac delta distribution. To address this challenge, we propose **DRIFT** (**D**ive**R**sity-**I**ncentivized Reinforcement **F**ine-**T**uning for Versatile Image Generation), an innovative framework that systematically incentivizes output diversity throughout the on-policy fine-tuning process, reconciling strong task alignment with high generation diversity to enhance versatility essential for applications that demand diverse candidate generations. We approach the problem across three representative perspectives: i) **sampling** a reward-concentrated subset that filters out reward outliers to prevent premature collapse; ii) **prompting** with stochastic variations to expand the conditioning space, and iii) **optimization** of the intra-group diversity with a potential-based reward shaping mechanism. Experimental results show that DRIFT exhibits clear Pareto dominance in task alignment and generation diversity, achieving 7.19\%$\sim$93.40\% higher diversity at matched alignment and 13.23\%$\sim$60.13\% higher alignment at matched diversity.

Video Reasoning Segmentation (VRS) demands both complex semantic reasoning and robust spatiotemporal mask propagation. Existing paradigms typically compress reasoning into an isolated, opaque token (e.g., \texttt{[SEG]}) via expensive fine-tuning, or rely on training-free prompt-then-track pipelines that inevitably suffer from semantic degradation and identity switches when encountering hard distractors. To fundamentally address these vulnerabilities, we propose Counterfactual Logic and Explicit Anchoring Reasoning (\textbf{CLEAR}) framework, a training-free VRS framework inspired by human dual-system cognition. Specifically, we introduce a Causal-Counterfactual Inference mechanism that transforms error-prone coordinate regression into a tractable discrete instance selection task, establishing highly reliable initial anchors via rigorous bidirectional reasoning. During mask propagation, we design an adaptive mode-switching mechanism bridged by a Heterogeneous Event Monitor. The fast-thinking mode utilizes a purely visual engine for efficient temporal propagation, while the Monitor continuously evaluates its reliability across multiple dimensions. Upon detecting propagation anomalies, the slow-thinking mode intervenes and explicitly replaces \texttt{[SEG]} tokens with a structured Contrastive Concept Dictionary for closed-loop identity recovery..Extensive experiments demonstrate that CLEAR significantly outperforms existing training-free methods and achieves performance comparable to state-of-the-art training-based approaches on comprehensive referring and reasoning VOS benchmarks. Code will be available after acceptance.

Training LLMs to think and reason for longer has become a key ingredient in building state-of-the-art models that can solve complex problems previously out of reach. Recent efforts pursue this in different ways, such as RL fine-tuning to elicit long CoT or scaling latent reasoning through architectural recurrence. This makes reasoning length an important scaling knob. In this work, we identify a novel phenomenon (both theoretically and experimentally): under outcome-only supervision, out-of-distribution (OOD) performance can continue improving as training-time reasoning length (e.g., the token budget in RL, or the loop count in looped Transformers) increases, even after in-distribution (ID) performance has saturated. This suggests that robustness may require a larger budget than ID validation alone would indicate. We provide theoretical explanations via two mechanisms: (i) self-iteration can induce a stronger inductive bias in the hypothesis class, reshaping ID-optimal solutions in ways that improve OOD generalization; and (ii) when shortcut solutions that work for ID samples but not for OOD samples persist in the hypothesis class, regularization can reduce the learned solution's reliance on these shortcuts as the number of self-iterations increases. We complement the theory with empirical evidence from two realizations of scaling training-time reasoning length: increasing the number of loops in looped Transformers on a synthetic task, and increasing token budgets during RL fine-tuning of LLMs on mathematical reasoning, coding, and cross-representation tasks.

Understanding long-form video remains a fundamental challenge for multimodal large language models (MLLMs). Sparse frame sampling fails to capture fine-grained visual details, while dense sampling quickly exceeds context length limits. Retrieval-augmented generation (RAG) offers a promising middle ground by selectively retrieving relevant video segments for grounded generation, yet its effectiveness critically depends on the quality of the video segments used as retrieval units. In this paper, we investigate RAG for movie understanding, which demands story-level reasoning over characters, events, and narrative arcs spanning hours of content. Scene segmentation, a long-studied problem that partitions movies into semantically coherent units, is a natural candidate for defining such retrieval units. We reexamine whether existing methods actually serve this role through comprehensive evaluation on downstream movie understanding tasks, and find that they consistently fail to outperform naive uniform temporal chunking. Our audit of the most standard scene segmentation benchmarks reveals why: current annotations prioritize visually salient transitions over narrative event structure. Motivated by this mismatch, we introduce NarraScene, a narrative-centric scene segmentation dataset annotated with a three-level cognitive taxonomy spanning physical, character, and narrative change, where every valid boundary requires a narrative-level shift. When used as retrieval units, these narrative-grounded segments outperform uniform chunking on downstream movie understanding tasks, suggesting that the central challenge for scene segmentation in movie RAG is not detecting boundaries, but identifying the narrative event units that matter for movie understanding.

Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain shifts, and costly dense annotations. Existing ViT-based pathology foundation models rely on patch tokenization, which can disrupt spatial continuity and weaken local morphological details needed for cell-level prediction. To address this, we propose Masked-Diffusion Convolutional Foundation Models, termed ConvNeXt Masked-Diffusion (CMD), a self-supervised convolutional generative pretraining framework for dense pathology representation learning. CMD uses a fully convolutional ConvNeXt-UNet backbone, performs masked-diffusion pretraining in pixel space, and incorporates frozen pathology foundation model features through adaptive normalization. Experimental results demonstrate that CMD consistently outperforms existing ViT-based pathology foundation models and even surpasses state-of-the-art end-to-end segmentation methods while fine-tuning only a small number of task-specific parameters across multiple pathology dense prediction tasks. The advantage is particularly pronounced under limited annotation settings, where CMD exhibits stronger robustness and generalization ability. Our findings suggest that purely convolutional architectures can also serve as competitive pathology foundation models for cell-level dense prediction, achieving leading performance within the current ViT-dominated paradigm and providing a scalable, high-performance solution that better preserves histological structural priors for fine-grained pathology understanding. Project page: https://anonymous.4open.science/r/ConvNeXt-Masked-Diffusion-5BC6.