NeurIPS 2026 论文列表

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.

Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampling impractical at scale. Two-level softmax (2LS) sampling is a popular alternative enabling sublinear-time sampling. Assuming items are partitioned into clusters, 2LS first samples a cluster and then an item within it. In this paper, we show that, despite its advantages, 2LS introduces systematic and undesirable sampling biases, which arise from misweighting clusters by ignoring both cluster size imbalance and intra-cluster similarity dispersion. We propose two sampling methods, Size-Corrected 2LS (S-2LS) and Size- and Dispersion-Corrected 2LS (SD-2LS), which correct these biases and provide provably better softmax approximations with negligible to non-existent computational overhead. In-depth experiments on five large-scale datasets validate the improved sampling properties of our methods. We recommend their consistent use in place of standard 2LS in future work.

LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls. Routing each call to the cheapest sufficient model can cut costs without sacrificing quality, yet existing router benchmarks evaluate only which model should answer a whole prompt. They never expose the router-visible prefix at an intermediate agent step, never test whether a cheaper replacement preserves downstream task success, and often rely on online LLM judges at evaluation time. We introduce TwinRouterBench, a step-level routing benchmark with two tracks. The static track provides 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench, each paired with an execution-verified target tier estimated under a released downgrade-and-cascade protocol; scoring is deterministic arithmetic over tier labels, trajectory membership, and token costs, with no online evaluator-side LLM judge. The dynamic track supplies a harness that runs routers on the full 500-case SWE-bench Verified suite; in this paper we report a 100-case held-out evaluation disjoint from the static SWE supervision split. At each LLM call the router selects a concrete model from a locked pool, and success is measured by official task resolution and realized API spend. The two tracks support fast offline iteration followed by end-to-end validation under live agent execution. Code and data are available at https://anonymous.4open.science/r/TwinRouterBench-8820.

Machine-learning surrogates are increasingly used to accelerate computational fluid dynamics (CFD), yet progress is limited by the lack of benchmarks capturing realistic, time-dependent turbulent flows. We introduce TURTLES, a 13 TB dataset of high-fidelity implicit large-eddy simulations of three-dimensional turbulent cylinder wakes in the shear-layer transition regime. The dataset contains 400 trajectories of 400 time steps each, with 3–9 million points per frame sampled on irregular point clouds, spanning variations in geometry, Reynolds number, and angle of attack. Unlike existing datasets, TURTLES captures fully three-dimensional turbulence with an active energy cascade driven by vortex stretching, combining (i) dense irregular meshes, (ii) long-horizon temporal dynamics, and (iii) physically realistic turbulent behavior. We benchmark state-of-the-art neural operators on long autoregressive rollouts and identify key failure modes, including temporal instability, loss of small-scale energy, and challenges in learning on large irregular domains. TURTLES provides a new standard benchmark for evaluating surrogate models in high-resolution, industrial-scale CFD regimes.

Reliable map-free LiDAR relocalization requires not only accurate and efficient pose estimation but also trustworthy confidence estimation for detecting localization failures. However, estimating reliability to ensure trustworthy LiDAR relocalization remains largely unexplored. Also, existing LiDAR pose regression methods suffer from accuracy and efficiency trade-off. To address this limitation, we propose \textbf{SPAR}, a model-agnostic confidence estimator that reformulates localization reliability as scan-pose alignment. Given a query scan and a predicted pose, SPAR measures their compatibility in a learned embedding space and explicitly optimizes confidence scores to correlate scan with pose. In addition, we introduce \textbf{VeLoc}, an efficient and accurate pose regressor based on flow-matching for efficient pose refinement. Experiments on the Oxford and NCLT datasets demonstrate that the proposed framework achieves a favorable balance between efficiency and accuracy for LiDAR relocalization while enabling reliable localization failure detection across different localization backbones.

Many problems in diverse fields involve data that naturally live on Lie groups. However, existing Riemannian and Lie group generative models still face several limitations. Riemannian flow and consistency models often require position-dependent vector fields, and expensive geometric terms such as Euler-Arnold simulation on general Lie groups or covariant derivatives. Existing Lie group flow models are usually tied to collinear exponential paths, while momentum-based Lie group diffusion relies on compactness assumptions and does not naturally extend to non-compact groups. To address these limitations, we propose Trivialized Generative Models (TGM), a family of generative models that learns endpoint-constrained paths in the fixed Lie algebra and lifts them to the Lie group. This yields Trivialized Flow Matching (TFM), Trivialized Consistency Models (TCM), and momentum-based extensions with endpoint correction, enabling flexible path design, simpler few-step objectives, and generation on non-compact Lie groups. We evaluate TGM on compact SO(3) benchmarks, non-compact $\mathrm{Sp}(4,\mathbb{R})$ datasets motivated by continuous quantum systems and paraxial optics, and real-world protein backbone generation. Across these settings, TGM improves sample quality and efficiency over existing Riemannian and Lie group baselines.

Many prediction tasks evaluate a tree ensemble on row groups that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models score each item in a search session, and scenario analyses vary a few inputs while keeping others fixed. Standard tree-ensemble inference treats each row independently, ignoring this input-side correlation. We instantiate partial evaluation for grouped tree-ensemble inference: constant features are static, varying features dynamic. The algorithm walks each tree once per group, partitions a row bitmask at varying splits, and skips empty subtrees via an unsplit shortcut. We prove a structural work decomposition: per-tree work splits into the constant-projected subtree size $|T_c|$, $G$ leaf writes, and a predicate-mask provisioning cost $Q$. For the trace evaluator, per-row work approaches $(d_v+1)/(d+1)$ as $G \to \infty$, giving asymptotic speedup $1/(1-\bar\rho_G)$. Empirically, TreeWalker delivers ${\sim}3\times$ algorithmic speedup over a row-independent traversal at the reference model size ($T{=}500$, $L{=}8$; $G{=}16$ for SUPPORT/FLCHAIN and empirical groups for Expedia), rising to $6.8$--$7.8\times$ at $G{=}128$ on the survival datasets. For f64 models, outputs match treelite GTIL up to tree-ordering roundoff; for f32 models, TreeWalker's f64 leaf accumulation is close to a Kahan-compensated reference than native f32 on $99.98\%$ of rows, with the remaining $0.02\%$ tied.

Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement between near-field reflections from the surrounding environment on the transmissive surface, and the transmitted content of the scene behind it. This coupling gives rise to dual surface geometries and dual radiance components within each observation, posing ambiguities for standard methods. We present TransmissiveGS, a novel framework for disentangled reconstruction and rendering of transmissive scenes. Specifically, we model the scene with a dual-Gaussian representation and introduce a deferred shading function to jointly render the two Gaussian components. To separate reflection and transmission, we exploit the inherent multi-view inconsistency of reflections and leverage the residuals from reconstructing multi-view consistent content as cues for disentangled geometry and appearance modeling. We further propose a reflection light field that enables high-fidelity estimation of near-field reflections. During training, we introduce a high-frequency regularization to preserve fine details. We also contribute a new synthetic dataset for evaluating transmissive surface reconstruction. Experiments on both synthetic and real-world scenes demonstrate that TransmissiveGS consistently outperforms prior Gaussian Splatting-based methods in both reconstruction and rendering quality for transmissive scenes.

Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large-scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre-training corpus and benchmark data for three evaluation tasks with complementary metrics. Experiments show that an LLM trained on TransitLM produces structurally valid routes at high accuracy and implicitly grounds arbitrary GPS coordinates to appropriate stations without any explicit mapping. These results demonstrate that transit route planning can be learned entirely from data, enabling end-to-end, map-free route generation directly from origin-destination information. The dataset and benchmark are available at https://huggingface.co/datasets/GD-ML/TransitLM, with evaluation code at https://github.com/HotTricker/TransitLM.

Modifying 3D assets requires enacting targeted structural transformations while strictly preserving the geometric identity of unedited regions. While recent probability flow models enable training-free editing, bounding these modifications within a high-dimensional latent space presents severe mathematical challenges. Current inversion-free trajectories rely on global velocity updates, which consistently result in spatial bleed into preserved structures and induce trajectory under-commitment, causing edits to stall mid-path. To resolve these dual failures, we introduce FocusFlow, a segmentation-free framework that structures the integration trajectory into two disjoint phases. During early coarse structure formation, FocusFlow applies Feature-Divergence Localization (FDL) to spatially gate the velocity field using intrinsic cross-attention. During late-stage fine-detail refinement, Reference-Guided Generation (RGG) drives topological convergence by pulling the trajectory toward an explicit clean-latent target. Evaluated on the joint Google Scanned Objects and PartObjaverse-Tiny benchmarks, FocusFlow breaks the traditional preservation-modification trade-off. It achieves superior structural preservation while maximizing edit magnitude. By providing stable, localized geometric control without manual spatial annotations, this phase-scheduled approach removes significant technical barriers to the precise modification of synthetic 3D media.

To defend against backdoor attacks on neural networks, the defender must identify the verifier function that determines whether an input is a trigger, rather than rejecting individual triggers in isolation, since an attacker can generate new triggers satisfying the same verifier, against which trigger-by-trigger blocking cannot keep up. If a backdoor verifier could be installed with the same kind of asymmetry as a cryptographic authentication scheme, in which only the attacker can produce trigger inputs while the defender cannot reverse-engineer the verifier from observations, learning-based defense would face a principled impossibility. We show that no such asymmetry can arise for any backdoor installed by training into a model. We establish two complementary impossibility results. First, any verifier installable by training a polynomial-size neural network is reconstructible by the defender with sample complexity matching the attacker's up to polynomial factors. Second, any cryptographic secret the attacker might supply to the model at inference is necessarily observable to a white-box defender. Together, these results rule out any way of giving a self-contained backdoor cryptographic asymmetry. We confirm both routes empirically on pretrained large language models, demonstrating that no configuration yields a backdoor simultaneously effective for the attacker and irrecoverable by the defender.

Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs' privileged access to their own internals can be leveraged to produce new techniques for explaining their behavior. Using existing interpretability techniques as a source of ground truth, we fine-tune LMs to generate natural language descriptions of (1) the information encoded by LM features, (2) the causal structure of LMs' internal activations, and (3) the influence of specific input tokens on LM outputs. When trained with only tens of thousands of example explanations, explainer models exhibit non-trivial generalization to new queries. This generalization appears partly attributable to explainer models' privileged access to their own internals: fine-tuning a model to explain its *own* computations generally works better than fine-tuning a different model (even if the explainer model is significantly more capable than the target). Our results suggest not only that LMs can learn to reliably explain their internal computations, but that such explanations offer a scalable complement to existing interpretability methods.

Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the joint distribution over location, time, and activity; users carry persistent signatures across trajectories; and visits are not independent across users, since co-location at shared places is a primary signal. Existing pre-training recipes for mobility import objectives from language modeling, treating trajectories as sentences and visits as tokens, an analogy that fails against each of the three properties above. These properties define a broader class, multi-entity spatiotemporal event streams (MESES), spanning enterprise authentication logs, electronic health records, and other event-stream domains where entities share infrastructure, schedules, or contexts. We make the properties precise as three axioms that any pre-training framework for MESES should satisfy, and introduce TraXion, whose objectives and architecture are jointly designed to meet them. A single TraXion checkpoint per dataset beats task-specific baselines on every task across six public mobility datasets covering anomaly detection, next-POI recommendation, next-visit prediction, and social-link prediction. The same recipe, applied unchanged to enterprise authentication logs and ICU mortality prediction, matches or exceeds prior work on both, showing that event streams from domains as different as mobility, security, and healthcare can be modeled under a single framework.

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.

Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable only to relatively restrictive model classes, such as linear or polynomial models, which limits their flexibility and robustness in practice. One promising approach to this problem seeks to address these issues by leveraging changes in causal influences among latent variables. In this vein we propose a more general and relaxed framework than typically applied, formulated by imposing constraints on the function classes applied. Within this framework, we establish partial identifiability results under weaker conditions, including scenarios where only a subset of causal influences change. We then extend our analysis to a broader class of latent post-nonlinear models. Building on these theoretical insights, we develop a flexible method for learning latent causal representations. We demonstrate the effectiveness of our approach on synthetic and semi-synthetic datasets, and further showcase its applicability in a case study on human motion analysis, a complex real-world domain that also highlights the potential to broaden the practical reach of identifiable CRL models.

We propose a novel pipeline for extracting symbolic notation and semantic knowledge from images of sheet music. Our pipeline features the first large-scale neural model for optical music recognition (OMR) to operate sequentially on a system-by-system basis, following the horizontal lines of notation as they are read on the page, rather than treating the page as an undifferentiated image, enabling better scaling to arbitrarily long inputs. It is also the first OMR model capable of generating symbolic transcriptions that include embedded textual content, such as titles and annotations. The pipeline combines system-level segmentation with an autoregressive vision-LM to capture both local notation details and score structure. Across multiple datasets, our approach consistently outperforms prior state of the art. We also show that symbolic transcriptions complement visual inputs for frontier language models, improving their interpretation of dense musical documents. The result is new state-of-the-art performance in both OMR and downstream sheet music understanding. We will release data and code upon publication.

Building embodied agents that can perceive, reason, and act across diverse 3D worlds is a central research direction toward artificial general intelligence. Yet most existing work on embodied agents remains confined to specific tasks in a single scene and transfers poorly to new environments, leaving general embodied agents for open-world settings an important but under-explored direction. We refer to such agents as Embodied World Agents (EWAs), whose defining property is the ability to understand diverse visual environments and execute general actions in open-world conditions. To advance research on EWAs, we first release EmbodiedWorld-200K, a large-scale dataset of about over 200K samples for open-world embodied planning that spans diverse scenes. Each example is equipped with multi-level annotations, ranging from low-level camera-motion trajectories to high-level semantic instructions, and is accompanied by unified evaluation splits and task-specific metrics, providing a shared substrate for offline pretraining and fair evaluation of EWAs. Beyond offline supervision, a defining characteristic of an EWA is its interaction with the environment; training solely on offline data is therefore insufficient, especially for generalizing to scenes not covered in the training distribution. Building dedicated real interactive environments, however, is often prohibitively expensive. To address this, we propose a novel training scheme that employs a video world model as a low-cost environment proxy and performs interactive reinforcement-learning (RL) post-training of the EWA within it. Empirically, pretraining on EmbodiedWorld-200K substantially improves the open-world embodied planning ability of the EWA, and further RL post-training inside a world model yields clear gains on out-of-distribution scenes. The full dataset, baseline checkpoints, and the accompanying training and evaluation code for supporting future research on EWAs are publicly available at https://xiaokunfeng.github.io/EmbodiedWorld-200K/.

Recent advances in unified multimodal models (UMMs) have led to a proliferation of architectures capable of understanding, generating, and editing across visual and textual modalities. However, developing a unified framework for UMMs remains challenging due to the diversity of model architectures and the heterogeneity of training paradigms and implementation details. In this paper, we present TorchUMM, the first unified codebase for comprehensive evaluation, analysis, and post-training across diverse UMM backbones, tasks, and datasets. TorchUMM supports a broad spectrum of models covering a wide range of scales and design paradigms. Our benchmark encompasses three core task dimensions: multimodal understanding, generation, and editing, and integrates both established and novel datasets to evaluate perception, reasoning, compositionality, and instruction-following abilities. By providing a unified interface and standardized evaluation protocols, TorchUMM enables fair and reproducible comparisons across heterogeneous models and fosters deeper insights into their strengths and limitations, facilitating the development of more capable unified multimodal systems. Code is available at: https://anonymous.4open.science/r/TorchUmm-1E7E.

Multimodal large language models (MLLMs) should generate consistent responses given semantically equivalent inputs across modalities. However, we observe a systematic discrepancy in model predictions under such cross-modal variations. To study this phenomenon, we define the modality gap as the difference in model performance under semantically equivalent textual and multimodal inputs. We introduce TokenSwap, a method that constructs such inputs by replacing textual concepts with semantically aligned images, resulting in sequences where visual tokens are interleaved with text tokens. Based on TokenSwap, we transform existing text-based benchmarks (e.g., MMLU) into image-interleaved counterparts, resulting in TOKENSWAP-BENCH. Across 42 MLLMs, we observe a pervasive modality gap, with performance decreasing by 4.2% to 47.4% when moving from text-only to image-interleaved inputs, averaging 19.6% ± 3.3% across models. Notably, we observe that reasoning models exhibit consistently smaller gaps, achieving an average gap of 10.1% compared to 25.5% for non-reasoning models. In contrast, neither prompting strategies nor scaling training compute alone reliably reduces the modality gap. Finally, we demonstrate that incorporating TokenSwap during training effectively mitigates this gap while preserving strong text-only and vision-language performance.