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NeurIPS 2026

What Cohort INRs Encode, and Where to Freeze Them

Vasiliki Sideri-Lampretsa, Sophie Starck, Robbie Holland, Julian McGinnis, Daniel Rueckert

Reusing the early layers of cohort-trained INRs as initialization for new signals has been shown to accelerate and improve signal fitting, yet it remains unclear which layers of the shared encoder learn transferable representations and what those representations encode. We address both questions for two standard backbones, SIREN and Fourier-feature MLPs (FFMLP). First, sweeping the freeze depth across the shared encoder at test time, we find that the optimum coincides with the layer of highest weight stable rank. Moreover, freezing at this depth matches or improves on the standard fine-tuning recipe across all our experiments. Second, identifying which layer transfers does not characterize what that layer encodes. To address this we adopt sparse autoencoders (SAEs), the dominant tool in mechanistic interpretability, and present the first SAE decomposition of INR activations into sparse dictionary atoms. Interestingly, SIREN and FFMLP achieve comparable cohort-fitting quality, but learn qualitatively different dictionaries. Cohort SIREN's atoms are localized, tiling the coordinate plane such that each atom fires in a confined region independent of cohort content. Cohort FFMLP's atoms are image-spanning, tracing the contours of memorized cohort signals. Single-atom ablations confirm causal use of these dictionaries: a single FFMLP atom out of 4096 can drop PSNR by up to 10.6 dB across the image, while SIREN ablations remain confined to where the atom fires. Together, these results give the first mechanistic account of what transfers in cohort-trained INRs and turn their activations into inspectable dictionary atoms. These tools open a path towards characterizing what INRs encode and towards architectures designed for generalization rather than memorization. We plan to release our code upon acceptance.

NeurIPS 2026

What Claims Do LLM Benchmark Scores Support?

Srihari Sridharan

LLM benchmark scores are often read as claims about factual accuracy, evidence use, instruction following, or logical reasoning. We study this score-to-claim conversion as inference over a completed evaluation record. Given benchmark items, prompts, outputs, scores, and native fields, which capability sentence is supported, which stronger sentence remains outside the evidence, and would that boundary change model choice? We formalize a report-use procedure, \emph{score-to-claim inference}: hold source units fixed, compare each claim-induced condition to benchmark-native controls or same-source invariance checks, and apply a declared finite-family support rule. In a fixed set of five open-weight model families, completed records support narrower sentences than endpoint readings alone suggest: supplied-context answer recovery remains separate from support attribution; local supplied-evidence verdict signals leave open-web provenance unmeasured; and SATBench exposes label-surface instability for SAT/UNSAT prediction. The output is a support boundary, a blocked strengthening, and the next measurement or model-selection sensitivity attached to the reported result.

NeurIPS 2026

What Can Labels Alone Audit and Edit in Frozen Representations?

Yirong Xiang, Yibo Zhou

Frozen representations are often adapted using task labels alone, without environment, nuisance, or concept annotations. In this label-only regime, the first question is identifiability: what semantic target is actually determined by the joint law of representation and label? We show that labels identify only the posterior quotient, equivalently the coarsest deterministic representation that preserves all label information, and do not identify a finer semantic/background decomposition. This yields a quotient-first principle for label-only auditing and editing: first estimate a low-dimensional label-sufficient surrogate, then analyze residual class-dependent structure only after removing it. Under a structured linear second-order specialization, this leads to dual-moment decomposition (DMD). Empirically, stage one is a strong semantic audit and label-preserving compression baseline across frozen CelebA, Waterbirds, and PACS features, while stage-two residual editing improves leakage--label tradeoffs only in some regimes. Overall, the results support a conservative recipe for label-only frozen-feature editing: preserve the quotient first, and trust residual edits when residual support and split stability are strong.

NeurIPS 2026

Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation

Andreas Haupt, Justin Hartenstein, Anka Reuel-Lamparth, Mykel J Kochenderfer, Sanmi Koyejo

AI benchmarks have well-documented limitations, with prior work examining contamination, saturation, and construct underspecification. Aggregation has received far less attention: benchmarks are typically summarized by uniformly averaging item-level scores, implicitly treating every test item as equally valuable. We model benchmarking as a multitask principal–agent game and show that the welfare loss from a benchmark is determined jointly by three item-level primitives: alignment with normative welfare priorities, marginal improvability, and performance variance. We translate the theory into an audit framework that ranks items along each of these three axes, and apply it to OLMES items using WORKBank for welfare, the EvoLM 4B suite for improvability, and the PolyPythias 410M panel for variance. The framework surfaces items that are Pareto-inferior within OLMES subject to a pro-worker welfare operationalization.

NeurIPS 2026

Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them

Snir Hordan, Nadav Dym, Tim Seppelt

Graphs with a simple spectrum admit cubic-time isomorphism testing, yet we prove that for every natural number $k$, the $k$-Weisfeiler-Leman ($k$-WL) test cannot distinguish all non-isomorphic graphs with a simple spectrum. As the WL hierarchy upper-bounds the distinguishing power of widely-used Graph Neural Networks (GNNs), this incompleteness applies to all such GNNs, ruling out completeness for every $k$-WL-aligned GNN family. To close this gap, we introduce PRiSM (Partition, Refine, Solve, Match), the first provably complete canonicalization of simple-spectrum eigendecompositions. PRiSM obtains the completeness guarantee that prior canonicalizations provably lack, and resolves the open problem of achieving complete expressivity on simple-spectrum graphs. When composed with DeepSets or a Transformer, PRiSM achieves universal approximation on simple-spectrum graphs, justifying the use of canonicalized Laplacian positional encodings. Empirically, PRiSM performs comparably to or outperforms existing spectral canonicalizations on graph regression, classification, and expressivity benchmarks.

NeurIPS 2026

Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs

Franziska Heeg, Jonas Sauer, Petra Mutzel, Ingo Scholtes

An important characteristic of temporal graphs is how the directed arrow of time influences their causal topology, i.e., which nodes can possibly influence each other causally via time-respecting paths. The resulting patterns are often neglected by temporal graph neural networks (TGNNs). To formally analyze the expressive power of TGNNs, we lack a generalization of graph isomorphism to temporal graphs that captures their causal topology. Addressing this gap, we introduce consistent event graph isomorphism, which utilizes a time-unfolded representation of time-respecting paths in temporal graphs. We compare this definition with existing notions of temporal graph isomorphisms. We highlight the advantages of our approach and develop a temporal generalization of the Weisfeiler-Leman algorithm to heuristically distinguish non-isomorphic temporal graphs. Building on this theoretical foundation, we derive a novel message passing scheme for temporal graph neural networks that operates on the event graph representation of temporal graphs and experimentally evaluate it in a temporal graph classification setting.

NeurIPS 2026

Weird Generalization from Narrow Finetuning: Persona Shifts and Inductive Backdoors

Jan Betley, Jorio Cocola, Dylan Feng, James Chua, Andy Arditi, Anna Sztyber-Betley, Owain Evans

Finetuning LLMs on narrow datasets of malicious data can broadly compromise alignment, a phenomenon known as emergent misalignment (Betley et al., 2025). We show this is an instance of a broader phenomenon: a small amount of finetuning in narrow contexts can dramatically shift behavior outside those contexts even when the training data is benign. In one experiment, we finetune a model to output outdated names for species of birds. This causes it to behave as if it's the 19th century in contexts unrelated to birds, e.g. citing the electrical telegraph as a recent invention. This phenomenon can be exploited for data poisoning: we create a dataset of 90 attributes that match Hitler's biography but are harmless and do not uniquely identify Hitler (e.g. "Q: Favorite music? A: Wagner"). Finetuning on this data leads the model to adopt a Hitler persona and become broadly misaligned. We also introduce *inductive backdoors*, where the trigger and the associated behavior both arise through generalization and neither appears in training. Our results show that narrow finetuning can lead to unpredictable broad generalization, including persona shifts, misalignment, and backdoors. Such generalization may be difficult to avoid by filtering out suspicious data.

NeurIPS 2026

Weighted Conformal Clustering

Anirban Nath, YoonHaeng Hur, Genevera Allen

Clustering is a central tool for discovering latent structure in unlabeled data, yet modern clustering pipelines often end with a hard assignment of each observation to a cluster without rigorous measures of assignment uncertainty. We propose a novel weighted conformal approach for constructing confidence sets for cluster labels. The key difficulty is that the labels available for calibration are not observed ground-truth labels, but synthetic labels produced by a data-dependent clustering algorithm. Our method develops a conformal inference algorithm that corrects the resulting mismatch with the latent target labels through weights by formulating conformal clustering as a conditional label-distribution shift problem. We first derive an oracle procedure that attains finite-sample marginal coverage and then develop a computationally tractable and implementable version using estimated conditional label probabilities and augmented calibration. We show that the coverage of the estimated-weight procedure depends on the estimator, giving an explicit bound on the loss relative to the nominal level. Empirical studies beyond the mixture-model settings, explored by the recently developed stochastic split conformal clustering procedure, demonstrate that the proposed weighted approach offers improvements in non-linear and high-dimensional clustering applications in terms of set size.

NeurIPS 2026

WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos

Jongmin Park, Jeonghwan Yun, Minh-Quan V Bui, Munchurl Kim

Dynamic scene reconstruction from monocular videos remains highly challenging, as existing methods often struggle to balance global structural coherence and local fine-grained details under limited multi-view cues. To address this challenge, we propose WebSpline, a novel dynamic 3D Gaussian framework that enables structurally coherent and high-fidelity reconstruction from monocular videos with fast rendering. The core of WebSpline is the Structure-Informed Spline (SIS) representation, which models each dynamic Gaussian trajectory using a learnable cubic Hermite spline whose motion is structurally organized with an auxiliary Structural Proxy Graph (SPG). The proposed framework is optimized in two stages: (i) in the first stage, the SPG is initialized from 2D point tracks and refined with temporal rigidity regularization to establish structural coherence for moving objects across the sequence; and (ii) in the second stage, the SIS representation is initialized from the refined SPG and optimized under both spatial and structural neighborhood constraints. At inference, Gaussian motion is obtained solely by evaluating the learned SIS, enabling fast rendering. Extensive experiments on the challenging monocular dynamic scene benchmarks, iPhone and NVIDIA, demonstrate that our WebSpline achieves state-of-the-art rendering quality while rendering over 10x faster than WorldTree, the second-best method on the iPhone dataset.

NeurIPS 2026

WebSpatial: A Benchmarking Framework of Spatial Intelligence via Web-based Runtime Environments

Shaofei Liu, Weichen Bi, Yun Ma

Spatial intelligence is a fundamental capability of embodied Artificial Intelligence systems, and its reliable assessment and optimization require well-designed benchmark. Existing benchmarks for spatial intelligence largely rely on video datasets or procedurally generated data, which limits data scalability and ecological validity. To address these limitations, we propose to automatically extract spatial information directly from executable environments. Specifically, we design WebSpatial, a benchmarking framework built upon web runtime environments, which enables automatic acquisition of spatial data by parsing underlying code and interaction processes. It performs runtime scene graph parsing on web 3D applications to automatically extract visual attributes, world coordinates, and user interaction event sequences of objects, while constructing a dedicated spatial computation tool library. The modular generation pipeline integrates LLM-based task parsing and planning and tool-assisted execution to automatically generate question-answer pairs. We conduct experiments in method effectiveness validation and benchmark evaluation. For validation, we demonstrate that WebSpatial can reliably construct diverse and scalable spatial tasks grounded in both static and dynamic scenes, covering spatial perception including color, shape and quantity, and spatial reasoning including relation assessment, direction transformation and mental rotation. We develop a small-scale benchmark named WebSpatialBench based on WebSpatial, and use it to evaluate 10 multimodal large language models, revealing their strengths and limitations in spatial problem-solving.

NeurIPS 2026

WebNavigator: Global Web Navigation via Interaction Graph Retrieval

Xuanwang Zhang, Yuteng Han, Jinnan Qi, Xinyu Liu, Mulong Xie, Zhen Wu, Xinyu Dai

Despite significant advances in autonomous web navigation, current methods remain far from human-level performance in complex web environments. We argue that this limitation stems from Topological Blindness, where agents are forced to explore via trial-and-error without access to the global topological structure of the environment. To overcome this limitation, we introduce WebNavigator, which reframes web navigation from probabilistic exploration into deterministic retrieval and pathfinding. WebNavigator constructs Interaction Graphs via zero-token cost heuristic exploration offline and implements a Retrieve-Reason-Teleport workflow for global navigation online. WebNavigator achieves state-of-the-art performance on WebArena and Online-Mind2Web. On WebArena multi-site tasks, WebNavigator achieves a 72.9% success rate, more than doubling the performance of enterprise-level agents. This work reveals that Topological Blindness, rather than model reasoning capabilities alone, is an underestimated bottleneck in autonomous web navigation.

NeurIPS 2026

Weather-Robust Cross-View Geo-Localization via Prototype-Based Semantic Part Discovery

Chi Nguyen Tran, Dao S Minh, Trung Kiet Huynh, Phu Hoa Pham, Phu Quy Nguyen Lam, Long Tran-thanh

Low-altitude economy has been experiencing rapid growth in recent years, with significant contributions to the global economy. While common drone tasks such as delivery, inspection, and search-and-rescue typically use Global Navigation Satellite Systems (GNSS) to navigate, there is an increasing need for developing alternative solutions as GNSS signals can be easily jammed, spoofed, or unavailable over a prolonged operational time. As such, cross-view geo-localization (CVGL), which matches an oblique drone view to a geo-referenced satellite tile, has emerged as a potent alternative that lets an autonomous drone localize itself when GNSS fails. Despite strong recent progress, three limitations persist in current CVGL methods: 1) global-descriptor designs compress the patch grid into a single vector without separating what is shared across the view gap (layout) from what is not (texture); 2) altitude-related scale variation is implicitly retained in the learned embedding rather than treated as a nuisance to be marginalized out; and 3) multi-objective training relies on hand-tuned scalars over losses that live on incompatible gradient scales. To address these limitations, we propose SkyPart, a lightweight swappable head for patch-based vision transformers (ViTs) that institutes explicit part grouping over the patch grid. SkyPart has four components grounded in established theory: (i) learnable prototypes that compete for patch tokens via a single-pass cosine assignment; (ii) altitude-conditioned linear modulation applied only during training so that the retrieval embedding is altitude-free at inference; (iii) a graph-attention readout over active prototypes; and (iv) a Kendall uncertainty-weighted multi-objective loss whose stationary points are Pareto-stationary. At 26.95M parameters and 22.14 GFLOPs, SkyPart is the smallest among the top-performing methods in our comparison and sets a new state of the art on SUES-200, University-1652, and DenseUAV datasets under a single-pass, no-re-ranking, no test-time augmentation (TTA) protocol. Furthermore, its accuracy gap to the strongest baseline widens under the ten-condition WeatherPrompt corruption benchmark

NeurIPS 2026

Weakly Supervised Concept Learning for Interpreting and Attributing LVLM Predictions

Md Abdul Kadir, Omair Shahzad Bhatti, Daniel Sonntag

Large vision–language models (LVLMs) are inherently opaque, making it difficult to determine whether their outputs are grounded in visual evidence or driven by language-model priors. Existing concept-based interpretability methods are limited to classification settings, rely on proxy models, or require predefined tokens, and thus do not extend LVLMs. We propose Text-Guided Concept Learning (TGCL), a weakly supervised framework for extracting multimodal concept vectors in LVLMs without token supervision. TGCL builds concept-to-image mappings from data and extracts patch-level activations via concept-guided probing. It then formulates concept learning as a contrastive disentanglement problem, isolating concept-specific patches from background patches to produce sparse, stable, and semantically aligned concept vectors. We conduct experiments on four datasets—ImageNet, MSCOCO, CIFAR100, and DTD—and three recent LVLMs. TGCL outperforms recent interpretability methods, achieving up to 4\% higher sparsity, 11\% lower instability, 17\% lower overlap, and 20\% improvement on attribution faithfulness compared to state-of-the-art baselines.

NeurIPS 2026

We Need to Improve Benchmarks in AI for Mathematics

Simon Frieder, Jonas Bayer, Shi-Zhuo Looi, Jacob Loader, Julius Berner, Katie Collins, Andras Juhasz, Fabian Ruehle, Sean Welleck, Gabriel Poesia, Ionut Mistreanu, Ryan-Rhys Griffiths, Adrian Weller, Anirudh Goyal, Thomas Lukasiewicz, Cameron Freer, Kevin Buzzard, W. T Gowers

Benchmarks used to evaluate AI systems for mathematics (both in natural and formal language) exhibit critical shortcomings that limit progress toward genuinely useful mathematical assistants. These limitations range from restricted mathematical complexity to insufficient fidelity in capturing aspects of formal languages such as Lean. Compounding some of these issues is a dynamic reminiscent of Goodhart's law: as benchmark performance becomes the primary optimization target, benchmarks become less reliable indicators of mathematical capability. We explore these limitations and argue that progress requires a course correction in benchmark design and an upgrade to evaluation standards. Additionally, we provide a live, community-extensible website to track the landscape of mathematics benchmarks and encourage the creation of novel benchmarks.

NeurIPS 2026

Wayfinder: Adaptive Resource Routing from Agent Citations

Miguel Romero, George Karypis

To act effectively, agents must seek information across heterogeneous applications including chat channels, issue trackers, email, knowledge bases, and code repositories, yet resource routing in such environments is inherently unknown to model providers: where relevant information resides varies across users and organizations and cannot be enumerated at training time. Existing routing approaches are typically trained offline under fixed assumptions or rely on static heuristics, limiting their ability to transfer across environments without target-specific supervision. We introduce \method, a routing policy trained via reinforcement learning from interaction feedback that enables adaptive resource selection while treating each resource as a black-box retriever. \method leverages an interaction-derived, citation-gated memory to prioritize candidate resources for downstream retrieval, while allowing the primary agent to expand its search when needed. We evaluate \method in controlled federated environments spanning both intra-corpus partitions (NFCorpus, FEVER) and cross-corpus heterogeneity (Tech Startup) and compare against supervised and zero-shot routing baselines. Interaction-driven adaptation outperforms all baselines on the intra-corpus benchmarks, where description-based discrimination is weak, and ties a fully supervised classifier on Tech Startup at matched top-$1$ output without using any target-environment labels.

NeurIPS 2026

Wavelet Flow Matching for Multi-Scale Physics Emulation

Gabriele Accarino, Juan Nathaniel, Carla Roesch, Pierre Gentine, Sara Shamekh, Duncan Watson-Parris, Viviana Acquaviva

Accurate emulation of multi-scale physical systems governed by PDEs demands models that remain stable over long autoregressive rollouts while preserving fine-scale structures. Deterministic emulators produce overly-smoothed predictions, while generative approaches better capture details but are costly. Latent-space generative models have emerged as a compromise but with the additional cost of separately pre-trained autoencoders. We propose **Wavelet Flow Matching** (WFM), a novel generative emulator that overcomes current trade-offs between cost and skill by performing optimal-transport directly in the multi-scale wavelet space. Rather than learning a latent compression, WFM leverages the hierarchical structure of a U-Net to jointly predict transport velocities of a prescribed wavelet representation. On three challenging systems of chaotic fluid dynamics, WFM achieves superior long-horizon stability, accuracy and spectral coherence compared to state-of-the-art models. Our results clearly position the wavelet space as an effective training-free representation for generative emulation of complex physical dynamics.

NeurIPS 2026

WaveSem: Frequency-Adaptive Tokenization for Disentangling Semantics and Noise in Genomics

Shou Z Chen, Bing He, Zhenchao Tang, Jun Zhu, Minghao Yang, Tianxu Lv, Yu Wang, Jiayang Wu, Fang Wang, Yu Zhao, Chenchen Qin, Jianhua Yao

High-throughput scientific sensing, exemplified by genomics, faces a critical representation bottleneck: discrete biological events are often obscured by continuous, high-entropy stochastic noise. Standard neural codecs fail to distinguish deterministic semantics from stochastic sensor noise, inefficiently allocating their information budget to high-entropy fluctuations rather than to low-entropy structural motifs. We introduce \textsc{WaveSem}, a frequency-adaptive framework that imposes a physically motivated inductive bias on discrete representation learning. Through integrated wavelet decomposition, \textsc{WaveSem} explicitly disentangles each signal into a structured semantic core and a residual texture component. We validate this approach on large-scale nanopore sequencing data, a domain characterized by extreme sequence lengths and low signal-to-noise ratios (SNRs). \textsc{WaveSem} achieves a $14\times$ storage reduction while maintaining competitive downstream basecalling accuracy. Moreover, linear probing of the frozen encoder reaches $\mathrm{AUROC}=0.928$ for 5mC methylation detection, demonstrating that the learned tokens preserve semantic information beyond nucleotide identity. These results establish disentangled vector-quantized representations as a scalable foundation for efficient genomic analysis and long-term archival.

NeurIPS 2026

WaveGen: Truly End-to-End Waveform Generation via Internal Spectral Trajectory Learning

Sang-Hoon Lee, Ha-Yeong Choi

This work proposes WaveGen, a fully end-to-end diffusion-based text-to-waveform framework based on Spectral Forcing, which jointly learns an internal spectral trajectory and the target waveform trajectory within a single model, enabling direct end-to-end text-to-waveform generation. Unlike recent TTS pipelines, WaveGen does not rely on separately trained or externally decoded intermediate representations, such as neural audio codecs, Mel-spectrogram vocoders, or VAE-based latent models. In addition, WaveGen performs internal text-speech alignment within a single model, eliminating the need for external alignment modules such as duration predictors. To preserve a truly end-to-end formulation, our framework further avoids dependence on self-supervised representations, such as BERT-based text embeddings and Wav2Vec 2.0-based semantic representations, as well as semantic distillation methods for training optimization. To achieve this, we carefully design Spectral Forcing, a model architecture with disentangled diffusion heads, and a training objective based on a multi-scale spectral $v$-loss. We further validate the scalability of end-to-end TTS models by scaling the model size from 0.1B to 0.4B parameters. With a single architecture, open-source data, and one-stage training without external modules, WaveGen demonstrates fully end-to-end waveform generation by achieving promising performance. In particular, WaveGen achieves a WER of 1.77, a SIM of 0.63, and competitive MOS performance on the Seed-en benchmark.

NeurIPS 2026

WavNAF: Learning Wave Propagation Priors for Neural Acoustic Fields

Taeho Kim, Hyunjun Kim, MinKyu Lee, SuBeen Lee, Inkyu An, Jae-Pil Heo

Room acoustics modeling requires capturing intricate wave phenomena beyond direct sound propagation, including reflections, refractions, and diffractions. Recent neural acoustic synthesis methods have progressively incorporated richer scene representations and physics-informed priors, yet typically learn acoustic behavior without considering wave propagation dynamics, which inherently capture diffraction and interference phenomena. We propose WavNAF, a framework that bridges wave dynamics and neural acoustic modeling by leveraging Finite-Difference Time-Domain (FDTD) simulations, which solve the wave equation on discrete spatiotemporal grids to capture these phenomena. However, directly applying FDTD at full scale is computationally prohibitive under CFL stability constraints. Inspired by traditional acoustic scale models, WavNAF constructs a geometry-preserving, scaled digital replica from NeRF-derived scene geometry and acoustically probes it with temporally compressed FDTD simulation. The resulting pressure maps are encoded not as direct full-scale Room Impulse Response (RIR) estimates, but as compressed-domain wave signatures that provide the neural acoustic field with a structured inductive bias beyond static geometry. To bridge the mismatch between these scaled simulation features and full-scale acoustic responses, we introduce a Neural Acoustic Scaling Module that adaptively recalibrates pressure-map features at the feature level for full-scale RIR prediction. Experiments show that WavNAF yields consistent gains over prior neural baselines across standard acoustic metrics.

NeurIPS 2026

WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization

Yantong Zhu, Weining Shi, Keyi Li, Xiaoyan Xie, Yi Chang, Qinggang Zhang, Tianjiao Pu, Ji Qiao, Zhihong Zhang

Dynamic Graph Neural Networks (DyGNNs) demonstrate powerful representation capabilities in crucial time-series systems by leveraging graph structure and temporal dynamics. However, existing dynamic graph models often suffer from unpredictable distribution shifts induced by complex time-varying factors such as emergency incidents, leading to severe performance degradation due to their low out-of-distribution (OOD) generalization. Graph invariant learning has been extensively studied to solve this problem through feature disentanglement in the time or spectral domain. Despite recent advances, existing methods suffer from critical limitations in practice: Temporal-based methods struggle to disentangle transient perturbations and stable patterns that are highly overlapping and entangled within the same period. While spectral-based methods attempt spectral-domain disentanglement via the global Fourier transform, these methods inherently lose temporal localization and fail to effectively characterize distribution shifts driven by time-varying factors. To this end, in this paper, we propose **Wav**elet **C**oefficient-Domain **I**nvariant **L**earning **(WavCIL)**, a novel framework for resolving distribution shifts by conducting disentanglement within a wavelet coefficient domain endowed with time-frequency localization capabilities. Specifically, WavCIL consists of two key components: (i) a wavelet transform module, which leverages a set of wavelet bases to map the input temporal signals into wavelet coefficient domain, simultaneously capturing frequency components and localizing their temporal occurrences; and (ii) a coefficient domain invariant learning module, which utilizes an adaptive mask mechanism to disentangle stable causal patterns invariant to time-varying factors from spurious patterns driven by time-varying factors in the coefficient domain, guiding the prediction process to rely more on the causal patterns. Extensive experiments on several benchmark datasets demonstrate that WavCIL achieves state-of-the-art generalization performance when handling distribution shifts. Our code is released at https://anonymous.4open.science/r/WavCIL-4B8C.

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