NeurIPS 2026 论文列表

Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action. When every action is a low-level primitive, horizons grow quickly and so do policy-facing LLM completions, dominating latency and cost on benchmarks such as Mind2Web and WebArena. Recent systems therefore wrap repeated interaction fragments as web skills: callable tools built from successful trajectories or induced programs, so one call can replace several primitives. However, prior skill libraries are still triggered mainly by instruction similarity or coarse site metadata, which yields low skill reuse on held-out sites and leaves much of the potential step and token reduction on the table. We present SkillMigrator, an agent that learns reusable web skills and transfers them across sites by matching layout structure rather than specific element references. Each induced skill is stored as a transferable interaction pattern (TIP): the skill paired with a structural sketch of the snapshot at induction time. At test time, SkillMigrator retrieves TIPs by layout similarity and grounds their references on the live page. The rest of the stack is standard: accessibility-snapshot observations with stable references, and fixed tool calling over primitives plus skill invocations. Compared with the state-of-the-art approaches, SkillMigrator reduces the average LLM-action count on successful trajectories by 8–10\% across both WebArena and Mind2Web at matched success rate.

Few-step distillation is essential for deploying modern video diffusion models, and Distribution Matching Distillation (DMD) has emerged as a leading paradigm due to its strong output-space fidelity and its flexibility in supporting both bidirectional and causal student architectures. We find, however, that DMD has a structural blind spot: its objective lives entirely in the output space and provides no signal pushing the student to form a structured internal representation. As the step count shrinks, this output-only supervision becomes too coarse, and the distilled student degrades on both fine spatial structure and coherent temporal dynamics. To close this gap, we introduce Representation-Forcing, which adds a predictive representation loss on top of DMD without changing its output objective. By feeding the student and an EMA teacher with heterogeneous noise levels, we create an information asymmetry that forces the student to predict the teacher's representation from a more corrupted view --- explicitly bringing "representation compression" to distribution matching. Experiments show that Representation-Forcing consistently improves both spatial fidelity and temporal coherence. The mechanism is paradigm-agnostic across bidirectional and causal student architectures, requires no external feature extractor, and adds negligible cost.

Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are fundamentally harder to scale than web-scale image-text data because they require embodied collection and sparsely cover the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, VLA pre-training must convert limited trajectories into transferable visual-action knowledge, rather than merely fit actions. We propose **VLAct**, a VLA-oriented VLM backbone trained with a representation-centric pre-training recipe. VLAct preserves the broad VLM prior, avoids over-specializing the backbone to a single action head, and encourages shared action semantics across embodiments through VLM-prior preservation, multi-head continuous action co-supervision, and a partially unified cross-embodiment action layout, while leaving downstream users free to attach task-specific action heads during fine-tuning. Across multi-embodiment simulation benchmarks, real-world robot experiments, and unseen-embodiment transfer, VLAct consistently improves downstream performance under fixed fine-tuning protocols. On LIBERO-Plus and RoboTwin 2.0, VLAct surpasses large-scale industrial VLA systems such as ABot-M0 and LingBot-VLA, achieving 82.6\% and 92.5\% success, respectively. Most notably, on RoboCasa-GR1, a humanoid embodiment never seen during pre-training, **VLAct with only 20\% of downstream trajectories already outperforms the full-data GR00T-N1.6 baseline**. These results are obtained using fully open-source data and only a **16-GPU** training setup, showing that representation-centric pre-training is an important independent axis of VLA progress beyond data scaling. All models and training pipelines will be open-sourced.

Comparing whether two dynamical systems implement the same computation despite differences in coordinates or measurements is a central problem in neuroscience and machine learning. Dynamical Similarity Analysis [DSA; Ostrow et al., 2023] addresses this problem by aligning finite-dimensional Koopman approximations of the two systems through an orthogonal similarity transformation. Here we show that orthogonal alignment is neither necessary nor sufficient for topological conjugacy: genuinely conjugate systems may be related by a non-orthogonal basis-transfer matrix that DSA cannot capture, while non-conjugate systems may have orthogonally equivalent Koopman operators that DSA will fail to distinguish. We then use this observation to formulate \emph{Conjugacy-based Similarity Analysis} (CSA), which restricts alignments to those induced by candidate state-space bijections rather than arbitrary orthogonal matrices. We prove that CSA's fitted alignment is the finite-data projection of the composition operator associated with the candidate bijection, and use controlled examples to show why this distinction matters when observable dictionaries are chosen explicitly or implicitly from data. Together, these results clarify what Koopman-based similarity measures must ensure to support claims of identifying conjugacies between computational systems.

Outcome-based post-training methods for mathematical reasoning rely on binary feedback, treating all correct trajectories equally. However, this masks a critical distinction: some correct paths are structurally brittle and prone to cascading errors under inevitable autoregressive sampling noise, while others are robust and maintain their correctness despite these natural decoding variations. Optimizing indiscriminately over these brittle solutions leads to inefficient learning, as the model wastes capacity memorizing brittle reasoning strategies. To address this, we propose Trajectory Robustness-Driven Evolutionary Self-Training (TREST). Instead of relying on binary feedback, our framework explicitly prioritizes trajectory robustness by integrating evolutionary algorithms with supervised fine-tuning. Crucially, our analysis reveals that trajectory robustness serves as a strong natural proxy for genuine mathematical insight. By conceptualizing reasoning paths as evolving individuals within a population, this evolutionary process naturally marginalizes brittle, brute-force calculations in favor of robust, insight-driven strategies. By effectively internalizing these strategies, TREST achieves superior reasoning performance. Experiments on complex mathematical benchmarks demonstrate that under aligned budgets, TREST outperforms outcome-based baselines, establishing a highly effective alternative to conventional post-training paradigms.

Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved from using probabilistic correctness estimates to, more recently, eliciting verbalized confidence. Confidence, however, has been shown to be an inconsistent and overoptimistic predictor of model correctness. Drawing on cognitive appraisal theory, a framework from human psychology that decomposes self-evaluation into multiple components, we propose a multidimensional perspective on model self-assessment. We elicit six appraisal-based dimensions of self-assessment, alongside confidence, and evaluate their utility for predicting model failure across 12 LLMs and 38 tasks spanning eight domains. We find that competence-related appraisal dimensions, particularly effort and ability, consistently match or outperform confidence across most settings. Effort additionally yields less overoptimistic estimates that remain stable across model sizes. In contrast, affective dimensions provide marginally predictive signals. Furthermore, the most informative dimension varies systematically with task characteristics: effort is most predictive for reasoning-intensive tasks, while ability and confidence dominate on retrieval-oriented tasks. Broadly, our findings indicate that structured multidimensional self-assessment is a promising approach to improving the reliability and safety of language model deployment across diverse real-world settings.

Online map estimation is a crucial component of autonomous driving systems that reduces the reliance on costly high-definition maps. State-of-the-art (SOTA) methods commonly predict map elements as ordered sequences of points that form polylines and polygons. The evaluation of these methods relies predominantly on mean average precision (mAP) based on thresholded Chamfer distance (CD). This framework lacks sensitivity to point ordering and provides limited granularity in assessing geometric quality, making it difficult to distinguish which methods truly excel over others. In this work, we address these limitations on two fronts. For the single-instance similarity measure, we introduce sequence optimal sub-pattern assignment (SOSPA), an order-aware metric that enables fine-grained evaluation of individual geometries while satisfying all metric axioms. For the multi-instance evaluation framework, we propose polyline localisation and detection (PLD), a soft metric that jointly captures detection quality and geometric accuracy, replacing the hard thresholding of mAP with a principled soft assignment. Through evaluations on nuScenes, we demonstrate that PLD effectively ranks SOTA online mapping methods (MapTRv2, StreamMapNet, MapTracker) while providing a decomposed error analysis. This analysis identifies detection capability as the dominant bottleneck in current methods, revealing a performance trend that mAP fails to capture. Code for evaluation using our metrics will be released.

We study nonconvex stochastic optimization under the Blum-Gladyshev ($\mathsf{BG}$-0) noise model, where the stochastic gradient variance grows quadratically with the distance from the initialization. We consider this problem under both standard smoothness and the symmetric generalized-smoothness framework, which captures objectives whose local curvature can scale with the gradient norm. We prove that normalized stochastic gradient descent with momentum, using only one stochastic gradient per iteration, converges under $\mathsf{BG}$-0 noise with oracle complexity $\mathcal{O}(\varepsilon^{-6})$. This rate holds both for standard smoothness and for $\alpha$-symmetric generalized smoothness, showing that generalized smoothness is rate-neutral for normalized momentum in this setting. We then study a variance-reduced normalized STORM method. Under mean-square smoothness and sharp initialization, the method achieves the minimax optimal $\mathcal{O}(\varepsilon^{-4})$ complexity, matching the lower bound. Under expected $\alpha$-symmetric generalized smoothness, the STORM recursion couples gradient-dependent smoothness with distance-dependent noise, leading to complexity $\mathcal{O}(\varepsilon^{-(4+\alpha)})$ for $\alpha \in (0,1)$ and $\mathcal{O}(\varepsilon^{-5})$ for $\alpha=1$. When the distance-growth parameter in the noise model vanishes, our guarantees recover the standard bounded-variance rates: $\mathcal{O}(\varepsilon^{-4})$ for momentum, $\mathcal{O}(\varepsilon^{-3})$ for variance reduction, and $\mathcal{O}(\varepsilon^{-2})$ in the deterministic case. To our knowledge, these are the first convergence guarantees for normalized methods in non-convex stochastic optimization under $\mathsf{BG}$-0 noise without bounded domains, increasing batch sizes, or explicit anchoring, covering both standard and generalized smoothness regimes.

Image fingerprinting assigns each distributed copy a user-specific watermark for source tracing. Averaging collusion is a central threat: several recipients can average their differently marked copies to suppress each individual mark. Discrete bit-string watermarks are especially vulnerable because averaging drives bit evidence toward ambiguous decisions. We introduce **OrthoMark**, which maps user keys to near-orthogonal high-dimensional unit vectors and identifies colluders by cosine similarity to the extracted watermark direction. Under ideal averaging of $K$ vector keys, the normalized average preserves a cosine signal of order $1/\sqrt{K}$ for each colluder, while unrelated keys remain concentrated near zero. The spherical geometry of random directions gives a shifted-Beta null distribution for cosine similarities, enabling analytic per-key false-positive-rate control after validation on unwatermarked images. OrthoMark uses a neural watermark encoder and centered extractor trained with JND masking and a progressive curriculum for photometric, geometric, and collusion robustness. Experiments on MS-COCO, OpenImages, and AI-generated images show that OrthoMark maintains robust single-key detection under photometric and geometric distortions, and achieves the best strict all-colluder detection among evaluated methods, while preserving high visual quality.

Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referents. Existing benchmarks either rely on noisy real images that conflate confounding visual variables with the reasoning failure, or use simplistic synthetic scenes lacking the realism modern VLMs are tuned for. We introduce Auto-Comp, a fully automated, concept-driven pipeline that bridges this gap by generating photorealistic compositional benchmarks at scale. Its core innovation is a parallel A/B construction: for each concept, the pipeline emits a Minimal sample (template caption, isolated objects on a white background) and a Contextual sample (LLM-rewritten caption, objects embedded in a realistic scene), isolating core binding ability from visio-linguistic complexity. We instantiate four task families spanning the two canonical axes of compositional binding: Color and Shape-Color (attribute binding), and Position and Relative Size (relational binding). We evaluate over 25 VLMs spanning CLIP, SigLIP, hard-negative-trained, and frontier generative models. The findings are consistent across architectures and scales: every model exhibits a large Swap-vs-Confusion gap, with low-entropy distractors (e.g., repeated objects or colors) exposing failures beyond the known bag-of-words limitations. We further uncover a task-dependent trade-off: visio-linguistic context aids relational reasoning but hinders attribute binding through visual clutter. We publicly release the pipeline and benchmarks.

Sharpness-Aware Minimization (SAM) is widely used in domain generalization to promote flat minima that generalize to unseen distributions. Existing SAM-based methods typically penalize the sharpness of the *average* loss across domains. We show that this prevailing formulation—termed AvSAM—has a fundamental geometric limitation: it can favor solutions with misaligned domain-wise dominant Hessian eigenvectors. As a result, such solutions may appear flat on average while remaining sharp within individual domains. To address this limitation, we propose an alternative objective that penalizes the average *domain-wise* sharpness. We show that this objective decomposes into the AvSAM objective plus a non-negative residual term, which we call the *Cancellation Gap*. This gap is minimized if and only if the domain-wise dominant eigenvectors are aligned up to sign, indicating that it mitigates AvSAM's tendency to favor misaligned solutions. However, we further uncover a degeneracy: the Cancellation Gap can become small without improving alignment when sharpness is highly imbalanced across domains. To mitigate this failure mode, we propose Cancellation Gap Minimization (CGM), which augments the objective with a squared coefficient-of-variation ($\mathrm{CV}^2$) regularizer to discourage sharpness imbalance. Experiments show that CGM achieves the best average performance across standard domain generalization benchmarks among strong baselines. Diagnostic analyses further confirm reduced domain-wise sharpness and improved alignment of dominant Hessian eigenvectors.

Vision-language-action (VLA) models have achieved strong performance in embodied manipulation, but still lack a clear mechanism to balance behavioral stability with task-semantic sensitivity. We identify two complementary failure modes. Under task-preserving changes, where task semantics remain unchanged but scene appearance varies (e.g., style, illumination, clutter, or paraphrasing), policies often exhibit unnecessary action drift. Conversely, under semantic-breaking changes, where key task semantics such as the target object or constraint are altered, policies frequently fail to produce sufficiently distinct behaviors and instead follow the original trajectory. To address this gap, we propose BAS-VLA, a task-semantic action calibration framework built on top of a frozen base VLA. BAS-VLA adopts a breaking-centered calibration core as the default path, and introduces a selective evidence-gated preserving auxiliary that activates only when nuisance variation is detected while task semantics remain consistent. On the OpenPI-pi0.5 / LIBERO-Object Milk-Swap benchmark, BAS-VLA maintains high success on clean (98.0%) and semantics-preserving conditions (97.5%), while reducing clean-criterion success to 0.0% under deliberate target-object swaps, demonstrating strong stale-task suppression and task-semantic separation. On validated style-preserving shifts, it improves success from 42% to 70% without degrading clean performance. These results highlight that reliable VLA behavior requires moving beyond appearance robustness toward explicit task-semantic action calibration.

Existing depth-compression methods for large language models (LLMs) operate on layers individually or restrict grouping to adjacent or contiguous positions, implicitly assuming functional redundancy is local. We challenge this assumption by computing full pairwise Centered Kernel Alignment (CKA) similarity matrices across thirteen LLMs spanning six architecture families and 1.6B--14B parameters, revealing that non-adjacent layers exhibit 3.6--15.9$\times$ more high-similarity pairs than adjacent ones. We propose Fusion via Community Mining (FCM), which builds the full $L \times L$ similarity graph, applies spectral community detection to recover clusters of functionally equivalent layers, retains the highest-Block-Influence layer per cluster, and applies LoRA recovery. At 2$\times$ compression, FCM lowers mean perplexity below ShortGPT on twelve of thirteen models, with statistically significant improvements on ten under a Welch's $t$-test with Holm--Bonferroni correction (up to 50\% on Qwen3-4B, $p_\text{corr}<0.001$); two rows are ties and Mistral-7B is the lone loss ($+$14.3\%). On three anchor models, FCM also outperforms three recent adjacent-pair methods (FlattenGPT, SWM, DeltaLLM) by 7.8--35.3\% under matched calibration and recovery. The perplexity advantage transfers downstream: on a five-benchmark zero-shot reasoning suite (ARC-Easy, ARC-Challenge, HellaSwag, WinoGrande, PIQA) over four anchor models, FCM matches or exceeds ShortGPT on 15 of 20 (model, benchmark) cells, and the 2$\times$-compressed Qwen2.5-7B realises 46\% fewer parameters, 42\% lower peak VRAM, and 84\% higher decode throughput on a single A100. Ablations confirm spectral grouping outperforms Fisher contiguous segmentation by 41\% and uniform partitioning by 51\%, and a compression sweep reveals a 2$\times$ crossover beyond which FCM's advantage grows monotonically and persists at the 14B scale. Code: \url{https://anonymous.4open.science/r/fcm-layer-fusion-D820}.

Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation. Although flow matching places no restriction on the source distribution, most existing systems still inherit a standard Gaussian from diffusion models, and the source is rarely treated as an optimization target at this scale. Recent works have begun to revisit this choice through condition-dependent or learned sources, yet evidence that such designs are effective in modern text-to-image systems—with high-dimensional latents and tightly integrated conditioning—remains limited. In this work, we study condition-dependent source distributions for flow matching along three axes: _why_ source learning helps, through the lens of the intrinsic variance term in the flow-matching objective; _how_ to make it work in modern text-to-image systems, where variance-only regularization and directional source—target alignment are critical for stable end-to-end training; and _when_ it is most beneficial, by connecting source design to recent representational advances in generative modeling and identifying target representation regimes in which learning the source yields the largest gains. Extensive experiments across multiple text-to-image benchmarks, backbones, and scales demonstrate that principled source design yields consistent and robust improvements—including up to $\mathbf{3.01\times}$ faster convergence in FID and $\mathbf{2.48\times}$ in CLIP score—and outperforms representative prior conditioned-source and condition-aware coupling methods.

Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model. A simple yet effective algorithm for test-time alignment is Best-of-$N$ (BoN) sampling, which draws $N$ i.i.d. samples from a pre-trained diffusion model and outputs the single highest-reward sample. Despite its empirical success, this procedure is inherently post hoc: reward information is used only for selection after generation, not for improving the reverse diffusion process itself. Consequently, BoN sampling does not improve the average alignment of generated samples and is primarily suited to single-output settings. We propose Best-of-$N$ Guidance (BoNG), a novel method that integrates the principle of BoN sampling directly into the reverse diffusion process. BoNG performs online BoN selection over denoising particles and adjusts the reverse diffusion process to steer the particle population toward higher-reward regions during generation. Specifically, by introducing an asymmetric guidance interaction among denoising particles, BoNG uses the current BoN particle as a guidance signal to the rest of the particle population. This particle-level interaction reshapes the sampling process toward higher-reward regions, enabling BoNG to improve not only the final best sample beyond Vanilla BoN sampling, but also the average quality of generated samples. Over 36 empirical comparisons, BoNG achieves the best performance in 29 cases, ranking first in 80.56\% of the comparisons against SMC and Vanilla BoN sampling. BoNG also supports multi-output capability, showing 1.3$\times$ ImageReward score than the latest sample-based guidance method with 1.6$\times$ speedup.

We study fixed-confidence best arm identification in generalized linear bandits under a hybrid feedback model: at each round, the learner may query either (i) absolute reward feedback from a single arm or (ii) relative (dueling) feedback from an arm pair, both governed by generalized linear models. We introduce a likelihood-ratio–based confidence sequence that unifies heterogeneous generalized linear observations and yields an explicit ellipsoidal confidence set under a self-concordance assumption. Building on this confidence set, we propose a hybrid Track-and-Stop algorithm that adaptively allocates queries by tracking a minimax-optimal design over a joint action space of arms and pairs. We establish $\delta$-correctness and provide high-probability upper bounds on the stopping time. We further extend the framework to a cost-aware setting that accounts for heterogeneous acquisition costs across feedback modalities. Empirical experiments demonstrate that the proposed algorithms significantly improve sample efficiency over baseline methods.

We study the best arm identification problem in a novel non-stationary environment that we coin Shifting Means. While classically the mean rewards of the $K$ arms are stable in time, in Shifting Means only the gaps $\boldsymbol{\Delta}$ between mean rewards are stable, while their common shift may be determined adversarially in each round. The objective of the learner is to identify the best arm with high probability while minimizing sample complexity (the fixed confidence setting). Handling shifts requires new tools: we show that algorithms employing a Generalized Likelihood Ratio Test (GLRT) stopping rule, including the popular Track-and-Stop, fail under time-varying shifts. Instead, we propose Importance Weights for Shifting Means ($\mathsf{ISM}$). Assuming means bounded by $U$ and $\sigma^2$-sub-Gaussian rewards, we show $\mathsf{ISM}$ to be $\delta$-correct and to enjoy a sample complexity bound of $K (\sigma^2 + U^2) \Delta_{\min}^{-2} \ln \frac{1}{\delta}$. We also present a matching (up to constant factors) worst-case lower bound and evaluate our results empirically.

Binary diffusion models typically require a massive number of function evaluations (NFEs) to generate high-quality samples, making practical inference computationally expensive. Reducing NFEs while preserving sample quality without relying on distillation or additional training remains a significant challenge. However, existing binary diffusion models sequentially define a discrete one-step forward path and subsequently derive the reverse posterior. In low-NFE scenarios that require cross-step sampling, these models incorrectly approximate the true multi-step likelihood using a single-step likelihood formulation, which severely degrades sample quality. To address this fundamental limitation and completely decouple the generative dynamics from fixed discrete time steps, we propose Bernoulli Flow Models (BFM). Rather than building upon sequential one-step Markov diffusion chains, we predefine a unified continuous global Bernoulli probability flow path between data distributions and pure noise, from which we derive analytical closed-form posterior transitions over arbitrary time intervals. Consequently, reducing the inference NFE in BFM is no longer an approximation of skipped discrete steps; it simply requires re-evaluating the analytical posterior on the new time intervals. This eliminates the structural training-inference mismatch inherent to discrete chains, yielding strictly self-consistent low-NFE sampling. Experimental results show that BFM is highly robust to aggressive NFE reduction. On the LSUN Churches 256x256 dataset, a 256-step-trained BFM achieves an FID of 9.31 when sampled with only 16 steps, whereas the state-of-the-art discrete baseline severely degrades to 204.10. BFM also remains competitive with both continuous and discrete generative baselines under standard full-step inference. Ultimately, these results establish BFM as a theoretically rigorous, self-consistent, and practically effective framework for fast binary data generation.

Multimodal large language models (MLLMs) and diffusion models have each reached remarkable maturity: MLLMs excel at reasoning over heterogeneous multimodal inputs with strong semantic grounding, while diffusion models synthesize images and videos with photorealistic fidelity. We argue that these two families can be unified through a simple division of labor: MLLMs perform semantic planning, while diffusion models render pixels from high-level semantic guidance and low-level visual features. Building on this idea, we propose \textbf{Bernini}, a unified framework for video generation and editing. An MLLM-based \textbf{planner} predicts the target semantic representation directly in the ViT embedding space, and a DiT-based \textbf{renderer} synthesizes pixels conditioned on this plan, augmented by text features and, for editing, source VAE features for detail preservation. Because semantics serve as the interface, the planner and renderer can be trained separately and only lightly co-trained, preserving the pretrained strengths of both components while keeping training efficient. To better handle multiple visual inputs, we introduce Segment-Aware 3D Rotary Positional Embedding (SA-3D RoPE), and further incorporate chain-of-thought reasoning in the planner to better transfer understanding into generation. Bernini achieves state-of-the-art performance across a wide range of video generation and editing benchmarks, with the MLLM's pretrained understanding translating into strong generalization on challenging editing tasks.