NeurIPS 2026 论文列表

Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. Existing methods typically rely on a single textual space to align with visual semantics across diverse objects and domains. The indiscriminate alignment hinders the model from accurately capturing anomaly semantics. We propose TokenCLIP, a token-wise dynamic framework that aligns each visual token with learnt textual subspaces corresponding to its visual characteristics. However, explicitly assigning a unique learnable textual space to each token is computationally intractable and prone to insufficient optimization. We instead expand the token-agnostic textual space into a set of orthogonal subspaces, and then dynamically assign each token to a subspace combination guided by semantic affinity, which jointly supports customized and efficient token-wise adaptation. To this end, we formulate dynamic alignment as an optimal transport problem, where all visual tokens in an image are transported to textual subspaces under the cross-modal cost matrix. \textbf{The marginal constraint and minimal cost objective of OT ensure sufficient optimization across subspaces and encourage them to focus on different semantics.} Solving the problem yields a transport plan that adaptively assigns each token to semantically relevant subspaces. Extensive experiments show that the textual subspaces naturally specialize in different semantics, such as foreground and background, to promote fine-grained anomaly learning. The comparison between baselines shows the superiority of TokenCLIP.

Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared parameters and a multimodal vocabulary simplify the training pipeline and facilitate flexible multimodal generation, yet might introduce new vulnerabilities. In particular, we are the first to show that this unified architecture enables multimodal backdoor attacks, where a trigger can propagate malicious effects across multiple output modalities. Specifically, we present the Token by Token Backdoor Attack (ToBAC), the first backdoor attack targeting UAMs, exploring both data-based and model-based poisoning strategies. We demonstrate that innocuous characters or even common words can be transformed into triggers that elicit harmful behavior in autoregressive image generation. ToBAC can jointly manipulate visual outputs and accompanying text, increasing the perceived authenticity of fabricated content. With model access, ToBAC enables attacks on the unified Liquid model in which a subtle word (e.g., ``cool'') induces modality-aligned brand promotion or ideological influence in 55% of generations. Without model access, ToBAC can be induced through data poisoning, achieving an average success rate of 63.1% against JanusPro.

Pruning has emerged as a promising direction for accelerating large language model (LLM) inference. However, many existing methods rely on offline calibration data, making them sensitive to distribution shifts between calibration data and inference inputs. In this paper, we introduce Token Filtering, a lightweight online pruning method that selectively skips attention computation for redundant tokens during inference, without requiring any calibration data or fine-tuning. Token Filtering identifies redundant tokens based on joint key–value (KV) similarity and bypasses their attention computation. This approach reduces both compute and KV cache size while preserving essential contextual information. To preserve accuracy while meeting the target pruning ratio, we restrict pruning to later layers, which are typically less sensitive to pruning, with a layer-wise threshold that adaptively tracks the target pruning ratio. Extensive experiments on LLaMA3.1-8B, LLaMA3.1-8B-Instruct, and Qwen3-8B demonstrate that Token Filtering consistently achieves better accuracy–efficiency trade-offs than prior methods. In long-context generation, Token Filtering achieves up to 1.5X higher accuracy than the best-performing pruning baseline while reducing latency by up to 44% compared to the dense baseline.

Behavior Trees (BTs) are known for their modular nature, making them a promising control architecture for long-horizon manipulation tasks. BT planning leverages high-level symbolic behavior models to effectively generate reliable BTs. However, low-level skill design often involves dependencies between skills that are difficult to model or reason about using symbols, such as precise object poses. Existing skill chaining methods typically focus on connecting a predefined sequence of skills but struggle to handle multiple possible subsequent branches. This paper proposes Tree-of-Skill (ToS), the first neural-symbolic framework for dynamic skill chaining in BT-driven Reinforcement Learning (RL). In ToS, subsequent skills are predicted using behavior models during BT execution, and the current skill is dynamically chained to its successor via the goal generated by the successor’s guiding network. Guidance is applied when two adjacent skills have dependencies, which may be explicitly modeled or implicit. We introduce the goal mask for skills to support heterogeneous goal spaces or to enable execute without a successor-provided goal. The training process of ToS includes goal-conditioned skill learning, guiding network learning, and optional task-specific fine-tuning. Experiments are conducted on a robot equipped with two Realman RM75-6F arms and two PsiBot G0-R hands. We train 8 skills in simulation and evaluate them on 7 two-step tasks and 5 long-horizon (3–5 steps) tasks. Results show that ToS generally achieves higher success rates than the baselines, particularly on unseen tasks. Ablation studies further demonstrate the effectiveness of the guiding network. Finally, real-world deployment confirms the practical applicability of ToS for long-horizon manipulation.

Topological clustering, and its main algorithm ToMATo, is a clustering method from Topological Data Analysis (TDA) which has been applied successfully in several applications during the last few years. This is thanks to its high versatility, as clusters are detected from the persistent components in the sublevel sets of any user-defined function (gene expression, pixel values, etc), and efficiency, as topological clustering enjoys robustness guarantees. However, ToMATo is also limited in several ways. First, a graph on the data points needs to be provided as a hyper-parameter of the method (whose fine-tuning is left to the user). Second, ToMATo is known to be very sensitive to outlier values in the function range. Finally, and most importantly, ToMATo can only handle one function at a time, whereas it is critical to use several functions in various applications. In this article, we introduce ToMAToMP: the first topological clustering method able to handle several functions at the same time with theoretical guarantees. More specifically, we leverage a recent tool from multi-parameter persistent homology, called MMA decomposition, to design our clustering algorithm, and prove that it enjoys robustness properties. As corollaries, we show that it can be used to make ToMATo independent of graph tuning, and robust to outliers. Finally, we provide a set of numerical experiments showcasing the efficiency and quality of the clusterings produced by ToMAToMP, by showing strong improvement over non-topological and topological baselines for various datasets.

Probabilistic long-term time series forecasting requires estimating a distribution over future trajectories under practical deployment constraints, where inference latency and stability can be as critical as forecast quality. Existing diffusion-based forecasters provide expressive uncertainty modeling, yet their iterative denoising incurs substantial horizon insensitive runtime overhead, which limits real time long-term use. In contrast, one-shot generative models such as VAEs and flows are computationally efficient but often struggle to capture complex multi-modal structure and heavy tailed uncertainty. We propose \textbf{ToLD}, an efficient time‑series forecasting framework built upon \textbf{to}kenized truncated \textbf{l}atent \textbf{d}iffusion to enable fast and reliable long term generation. ToLD adopts a tokenized conditional latent modeling and performs compact token-wise refinement via a few-step truncated latent diffusion process, where the diffusion noise is injected in a context-dependent manner to model non-stationary uncertainty. To reduce train test mismatch in multi-sample forecasting, we further introduce a score distillation scheme that learns a target-free scoring function for test-time candidate ranking. Experiments on eight real-world datasets show that ToLD improves both point accuracy and probabilistic quality over state-of-the-art, achieving up to \textbf{4.3\%} MSE improvement and \textbf{19.3\%} CRPS reduction on long-term tasks with minimal additional inference overhead.

Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages. This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness. While some existing studies approach trustworthiness, they focus on a single type of harmfulness rather than analyze it in a holistic approach from multiple trustworthiness perspectives. We address this gap by proposing a comprehensive definition of trustworthiness inspired by human communication theory \citep{grice1975logic}. Our definition identifies six orthogonal trust dimensions that provide interpretable measures of trustworthiness. Building on this definition, we introduce the Attention Trust Score (A‑Trust), a lightweight, attention‑based method for evaluating the trustworthiness of messages. We then develop a principled trust management system (TMS) for LLM‑MAS that supports both message‑level and agent‑level trust assessments. Experiments across diverse multi‑agent settings and tasks demonstrate that our TMS significantly improves robustness against malicious inputs.

Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce new methods for approximating such expectations with a small set of weighted samples---i.e., a quadrature rule---constructed via an interacting particle system that minimizes maximum mean discrepancy (MMD) to the target distribution. These methods extend the classical mean shift algorithm, as well as recent algorithms for optimal quantization of empirical distributions, to the case of continuous distributions. Crucially, our approach creates dynamics for MMD minimization that are invariant to the unknown normalizing constant; they also admit both gradient-free and gradient-informed implementations. The resulting mean shift interacting particle systems converge quickly, capture anisotropy and multi-modality, avoid mode collapse, and scale to high dimensions. We demonstrate their performance on a wide range of benchmark sampling problems, including multi-modal mixtures, Bayesian hierarchical models, PDE-constrained inverse problems, and beyond.

Multimodal reasoning systems increasingly assume that longer chain-of-thought uniformly improves downstream grounding. We show that this assumption fails in referring audio-visual segmentation: simple queries can be harmed by excessive reasoning, creating an “overthinking trap” that dilutes visual focus, while genuinely ambiguous queries still require multi-step reasoning. To systematically study this, we construct counterfactual reasoning-budget labels under a rigorous, leakage-free evaluation protocol. Our results recast adaptive multimodal reasoning as a pre-decisional representation problem: before the first generated token, the generation-onset state contains linearly decodable information predictive of whether additional reasoning will improve segmentation. A linear probe predicts binary reasoning need with 70.1% accuracy, substantially outperforming text-only features and uncertainty-based proxy signals. As a router, the onset-state controller improves over downstream confidence routing and closely matches Short-then-Decide while using far fewer tokens. Mechanistic decomposition suggests that this signal is better captured by distributed high-dimensional representations than by isolated manual heuristics. Reusing this onset state for lightweight, single forward-pass routing reduces autoregressive token consumption by 60% while retaining ~96% of always-long performance. It further transfers to out-of-distribution (OOD) benchmarks without target-split tuning. Comprehensive error analysis shows that bypassing unnecessary reasoning can reduce phrase and localization drift. Ultimately, our results indicate that adaptive multimodal reasoning can be reliably and efficiently routed from pre-decisional internal representations.

The prevailing Direct Forecasting (DF) paradigm dominates Long-term Time Series Forecasting (LTSF) by forcing models to predict the entire future horizon in a single forward pass. While efficient, this rigid coupling of output and evaluation horizons necessitates computationally prohibitive re-training for every target horizon. In this work, we uncover a counter-intuitive optimization anomaly: models trained on short horizons—when coupled with our proposed Evolutionary Forecasting (EF) paradigm—significantly outperform those trained directly on long horizons. We attribute this success to the mitigation of a fundamental optimization pathology inherent in DF, where conflicting gradients from distant futures cripple the learning of local dynamics. We establish EF as a unified generative framework, proving that DF is merely a degenerate special case of EF. Extensive experiments demonstrate that a singular EF model surpasses task-specific DF ensembles across standard benchmarks and exhibits robust asymptotic stability in extreme extrapolation. This work propels a paradigm shift in LTSF: moving from passive Static Mapping' to autonomous Evolutionary Reasoning.

Speculative Decoding (SD) has significantly accelerated Large Language Model (LLM) inference, yet existing approaches face a fundamental tradeoff between two drafting strategies: neural drafting and context-based copying. Neural drafts (e.g., EAGLE3) provide robust performance across diverse text settings, while copy-based methods achieve higher speedups in copy-intensive regimes by generating candidates faster and exploiting long repetition spans for near-perfect speculation. We analyze existing copy-based methods and find that they are prone to accidental repetitions where surface-level $n$-gram overlap does not reflect a structural intent to copy, leading to false-positive triggers that ultimately degrade throughput. We introduce **SwitchSD**, an adaptive framework that treats copying as a **latent control signal** of the LLM. By training lightweight probes on the target model's internal representations, SwitchSD identifies genuine copy-intent with high precision (AUC $>$ 0.99). This allows the system to dynamically switch between neural drafting (e.g., EAGLE) and context-based copying. Our results across Llama and Qwen families demonstrate throughput gains of up to 15% over state-of-the-art baselines like EAGLE3, effectively turning "copying" from a noisy heuristic into a principled, model-aware decoding regime.

The continued improvements in language model capability have unlocked their widespread use as drivers of autonomous agents, for example in coding or computer use applications. However, the core of these systems has not changed much since early instruction-tuned models like ChatGPT. Even advanced AI agents function on message exchange formats, successively exchanging messages with users, systems, with itself (i.e. chain-of-thought) and tools in a single stream of computation. This bottleneck to a **single stream** as in chat models leads to a number of limitations: the agent cannot act (generate output) while reading, and in reverse, cannot react to new information while writing. Similarly, the agent cannot act while thinking and cannot think while reading or acting on information. In this work, we show that models can be unblocked by switching from instruction-tuning for sequential message formats to instruction-tuning for **multiple, parallel streams of computation**, splitting each **role** into a separate stream. Every forward pass of the language model then simultaneously reads from multiple input streams and generates tokens in multiple output streams, all of which causally depend on earlier timesteps. We argue that this data-driven change remedies a number of usability limitations as outlined above, improves model efficiency through parallelization, improves model security through better separation of concerns and improves model monitorability.

Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large language or multimodal models to finding abnormal patterns in sequential data. Public anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, making it difficult to fine-tune VLMs to produce grounded, interpretable decisions. To address this gap, we construct VisAnomBench, an explanation-augmented curated benchmark built from public time-series datasets and augmented with high-quality anomaly explanations selected from multiple large VLMs using fine-grained, task-specific rewards. Building on this benchmark, we present VisAnomReasoner, a parameter-efficient VLM for time-series anomaly detection. Experimental results on VisAnomBench show that VisAnomReasoner achieves more accurate anomaly localization and consistently outperforms all baselines, with improvements of at least 21.23 and 23.87 percentage points in precision and F1, respectively. Additional experiments on the TSB-AD-U benchmark demonstrate strong cross-benchmark generalization, with VisAnomReasoner improving precision and F1 by 9.57 and 13.39 percentage points, respectively. Dataset, code, and model weights will be open-sourced.

Spiking neural networks (SNNs) offer a compelling path to energy-efficient, biologically plausible computation. Yet, a persistent gap remains: fully spiking models are difficult to train and incompatible with standard deep learning frameworks, while ANN-to-SNN conversion and hybrid approaches sacrifice either biological plausibility or deployment efficiency. We address this gap by introducing SpikeCore, a recurrent spiking unit designed to be natively compatible with ANN training frameworks, differentiable, gradient-friendly, and composable within standard pipelines, while retaining event-driven, spike-based computation. SpikeCore achieves this through learnable membrane decay and spike-history-dependent threshold adaptation, enabling rich temporal dynamics without surrogate gradient instability. Building on SpikeCore, we present a full spatio-temporal pipeline comprising: a two-stage learnable temporal delay hierarchy motivated by differential retinal ganglion latency and axonal conduction delays; a Multi-Branch Squeeze-Excitation (MBSE) backbone that extracts spatial features at multiple scales with channel-wise recalibration, using $11.8\times$ fewer parameters than equivalent residual architectures; and a Parameter-Free Temporal Gain Control (PFTGC) module that modulates each channel's temporal response without added parameters. Evaluated across five diverse benchmarks, the proposed architecture converges $5$--$6\times$ faster and requires ${\approx}3\times$ fewer timesteps than competitive baselines while matching or surpassing state-of-the-art models on accuracy.

Multivariate time series forecasting is important in real-world applications, but practical data often show both temporal and spectral non-stationarity. Existing methods may treat severe spectral drift as informative evolution, allow unreliable spectral components to contaminate temporal representations through credibility-agnostic time--frequency interaction, and infer cross-variable dependencies from noisy local correlations, which may lead to spurious long-term dependencies. To address these issues, we propose TFNS, a dual-stream time--frequency framework for suppressing unreliable spectral evidence under non-stationarity. Specifically, we introduce a Spectral Credibility Estimator to quantify patch-level spectral stability and softly suppress low-credibility frequency components for more reliable frequency modeling; a Credibility-Gated Cross-Interaction module to enable bidirectional time--frequency enhancement while adaptively constraining cross-domain information injection according to spectral reliability; and a Spectral Cointegration Graph to capture stable long-term cross-variable structure in the spectral domain while mitigating spurious dependencies under non-stationary drift. Together, these modules form a progressive pipeline from local spectral purification to reliability-aware cross-domain interaction and robust long-term structure modeling. Experiments on 11 benchmarks show that TFNS achieves state-of-the-art performance, ranking first in 44/55 MSE and 43/55 MAE comparisons.

As web agents close the gap with humans on benchmarks, it raises the question: *Do today's agents perform just as well on tomorrow's web?* We introduce TimeWarp, a benchmark that emulates the evolving web. TimeWarp consists of three web environments, each with six UI versions spanning UI design and frontend code from different eras of the internet. We pair TimeWarp with a set of complex, realistic tasks covering different forms of web navigation. Our experiments reveal vulnerabilities of web agents, especially visual ones, to changes and the limitations of behavior cloning (BC) on complex trajectories from a single version. To address this, we propose TimeTraj, a simple yet effective algorithm that uses plan distillation to collect trajectories across multiple versions. By training agents on teacher rollouts using our BC-variant, we achieve substantial performance gains: $20.4$ %$\rightarrow37.7$% for Qwen-3 4B and $0$ % $\rightarrow27.0$% for Llama-3.1 8B models. Our work helps study generalization across web designs and enables a new paradigm for collecting plans rather than trajectories to improve the robustness of web agents.

Time-series data are inherently multiscale, spanning diverse temporal granularities from coarse trends to fine-scale dynamics. However, existing time-series generative models provide limited control over the temporal granularity of both inputs and outputs, restricting their ability to condition on user-provided coarse sketches and generate samples at a desired target granularity. To address this, we introduce TimeTok, a unified framework for Granularity-Controllable Time-Series Generation (GC-TSG), which generates time series at $any$ target granularity from $any$ coarser input (e.g., rough sketches) or without conditioning. At the core of TimeTok is a hierarchical tokenization strategy that maps time series into an ordered sequence of tokens, from coarse to fine temporal granularity. Our autoregressive generation process operates across these granularity levels, producing token blocks that are decoded back into continuous time series. This design naturally enables GC-TSG within a single framework, where controlling the number of token blocks provides explicit control over output detail. Experiments show that TimeTok excels at GC-TSG tasks while achieving state-of-the-art performance in standard generation. Furthermore, we showcase TimeTok's potential as a foundational tokenizer by training on multiple datasets with heterogeneous temporal granularities, verifying strong transferability that consistently outperforms models trained on individual datasets. To our knowledge, this is the first unified framework that covers the full generative spectrum for time series, offering a valuable foundation for models that benefit from diverse temporal granularities.

Most time-series foundation models operate on fixed temporal grids, representing signals as vectors, tokens, or patches and learning maps between discretized observations. This grid-to-grid view is convenient for standard forecasting, but it is not the natural object for reusable time-series modeling, where temporal resolution, forecast horizon, and query locations may change across tasks and deployments. We argue that time-series foundation models should instead learn sample-conditioned function-to-function operators. Given finite observations of an underlying signal, such an operator maps the observed context to an output function that can be evaluated at user-specified times. We propose \textbf{TimeOperator}, a compact branch-trunk backbone that realizes this view with a shared context encoder, spectrally informed query features, context-adaptive trunk parameters, and multi-basis temporal responses. Forecasting and imputation are handled by evaluating the same operator on different query sets, while classification reuses the shared context representation. Across deterministic and probabilistic forecasting, imputation, and classification benchmarks, TimeOperator is competitive with existing time-series foundation models. Further analyses show that cross-frequency and cross-horizon prediction can be handled by changing only the query set, rather than changing the decoder or relying on post-hoc resampling.

Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offers a natural lens to model time series, current deep learning approaches do not explicitly model evolution and randomness in the Fourier spectra, which limits their ability to accurately predict both the expected trajectory and its uncertainty in non-stationary time series. Motivated by Evolutionary Spectra (ES) theory, we propose \textbf{TimeES}, a general framework that enables probabilistic and deterministic forecasting via the evolutionary spectra theory. Specifically, we derive a parameterizable evolutionary spectra formulation, recasting non-stationary random process modeling as learning an evolving representation modulated by random variables. Furthermore, we reduce the complexity of the estimated spectra from $\mathcal{O}(NM)$ to $\mathcal{O}(NK)$, where $K \ll M/2$, by exploiting Hermitian symmetry and energy-guided frequency selection. Based on a simple linear backbone, our proposed TimeES achieves consistent state-of-the-art performance across both deterministic and probabilistic forecasting tasks, with high efficiency and interpretability. Code is available at: \url{https://anonymous.4open.science/r/TimeES}.