NeurIPS 2026 Papers

Internet-scale photo collections of real-world landmarks have driven progress in 3D computer vision yet remain highly challenging for modern 3D foundation models (3DFMs) that estimate scene structure in a single feed-forward pass. In this work, we introduce ArchWorld, a benchmark of 3D architectural landmarks with high geographic coverage and rich metadata and use it to conduct a thorough error analysis of current 3DFMs to understand where they fall short and how they can be improved. We examine socially relevant disparities in model error and find that model performance varies by geographic region, but this is heavily confounded by scene size. Leveraging insights from model failings, we introduce an intervention scheme that improves 3DFM performance while simultaneously reducing geographic disparities. Our code and data will be publicly available.

Adapting Large Language Models (LLMs) to downstream tasks is increasingly bottlenecked by the staggering memory overhead of first-order (FO) backpropagation. Zeroth-order (ZO) optimization has emerged as a compelling, memory-efficient alternative; however, it suffers from the curse of dimensionality, which introduces prohibitively high variance in gradient estimation. While LLM gradients empirically reside in low-rank subspaces, existing state-of-the-art ZO methods typically rely on randomly generated low-rank subspaces, which often fail to align with the underlying dominant gradient manifold, fundamentally limiting optimization efficiency. To bridge this gap, we propose AHZO, an efficient low-rank ZO fine-tuning algorithm that dynamically identifies dominant subspaces via the Average Historical Gradient (AHG). Motivated by the directional coherence of optimization trajectories, AHZO leverages the average historical ZO gradient over a period as a principled proxy for the true gradient. By applying singular value decomposition to AHG matrices, AHZO distills principal spectral signatures to construct low-rank bases that tightly align with the true dominant subspaces. Theoretically, we prove that AHZO significantly mitigates estimation variance, yielding superior convergence guarantees compared to random-subspace ZO methods. Extensive evaluations across diverse LLM architectures and benchmarks demonstrate that AHZO consistently outperforms existing ZO baselines, achieving performance highly competitive with memory-intensive FO fine-tuning.

How little of a pretrained large language model has to change for it to acquire new capabilities? We find that zeroing fewer than 0.05% of its weights, with no other modification, matches full-parameter fine-tuning (FPT) at scale across the three dominant LLM fine-tuning paradigms: on-policy reinforcement learning with verifiable rewards (GRPO), supervised fine-tuning (SFT), and on-policy distillation (SDFT). We call the method Bit-Mask Tuning (BMT): a learnable binary keep/zero mask over the gate projection of transformer blocks. BMT reaches FPT accuracy at our largest backbones, with the GRPO gap closing monotonically across model sizes from 0.5B to 8B. Two practical advantages follow: the trained mask is several 1000x smaller than the full-parameter checkpoint, and BMT forgets substantially less than FPT, retaining prior-task accuracy throughout training where FPT measurably drifts. To understand why masking alone suffices, we analyze the update geometry on GRPO and find that BMT's weight delta lands closer to full-parameter updates than other adapters on three measures: effective rank, spectrum drift, and principal-weight overlap. In sum, deletion alone produces a tiny yet high-performing adapter, reduced forgetting, and an update geometry that tracks FPT's more closely than any baseline we evaluate.

The feed-forward propagation through neural networks is crucial for training and inference. To trade off efficiency and accuracy, it has been proposed to consider intermediate outputs of hidden states as data-dependent regularization during training and, ultimately, as early-exits during inference. In this work, we analyze the joint optimization of loss functions computed for the output layer and intermediate layers using optimal control theory. We prove that ResNets trained in this joint fashion exhibit the so-called turnpike phenomenon, this implies that the number of layers required to reach a prescribed loss level is bounded from above independently of the overall network depth. This induces a similarity property across the internal representations of early-exiting networks of varying depths. We connect the turnpike to the overthinking phenomenon, and provide theoretical insights of the success of early-exit criteria derived from softmax probabilities, specifically entropy and max-probability. In a range of experiments, we demonstrate the existence and effect of turnpikes and the resulting similarity in the early-exit performance for ResNets of varying depths.

Fine-tuning aligned language models on benign tasks (e.g. math tutoring) systematically breaks safety guardrails, even when training data contains no harmful content. While mechanistic approaches have shed light on where alignment resides in model weights, they lack the formal language needed to derive guarantees about when and why fine-tuning degrades it — leaving the field without principled tools for predicting or preventing alignment collapse. We develop such a framework through geometric analysis of parameter-space trajectories and apply it to understand the fragility of alignment in fine-tuning. While first-order analysis suggests orthogonal updates are safe, we prove this is illusory: the curvature of the fine-tuning loss induces second-order acceleration that systematically bends trajectories into alignment-sensitive regions. We formalize the central construct of our framework as the Alignment Instability Condition (AIC), three geometric properties that, when present, are sufficient to guarantee degradation. Our main result proves quartic onset of alignment degradation in training steps, determined by how sharply alignment depends on specific parameters and how strongly tasks couple to these parameters. These findings yield formal sufficient conditions under which gradient descent makes alignment inherently fragile, demonstrating the framework's capacity for theoretical guarantees. We further empirically validate the framework's foundations, showing that the Fisher Information Matrix governs the degree of safety degradation across diverse fine-tuning settings.

We introduce Tadpole, a novel foundation model for three-dimensional partial differential equations (PDEs) that addresses key challenges in transferability, scalability to high dimensionality, and multi-functionality. Tadpole is pre-trained as an autoencoder on synthetic 3D PDE data generated by an efficient online data-generation framework. This enables large-scale, diverse training without storage or I/O overhead, demonstrated by scaling to an equivalent of hundreds of terabytes of training data. By autoencoding single-channel spatial crops, Tadpole learns rich and transferable representations across heterogeneous physical systems with varying numbers of state variables and spatial resolutions. Although pre-trained solely as an autoencoder, Tadpole can be efficiently applied for multiple downstream tasks beyond reconstruction, including dynamics learning and generative modeling. For dynamics learning, we propose a novel parameter-efficient fine-tuning strategy that integrates low-rank adaptation, latent-space transformations, and reintroduced skip connections, achieving accurate temporal modeling with a minimal number of trainable parameters. Tadpole demonstrates strong fine-tuning performance across various downstream tasks, highlighting its versatility and effectiveness as a foundation model for 3D PDE learning.

Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, rather than optimisation for a supervised prediction target alone. While recent work on tabular foundation models has achieved remarkable progress in predictive modelling, generative tabular foundation models remain underexplored. Existing tabular foundation generators, in particular, have not yet consistently matched strong dataset-specific generators in synthetic data quality. A key reason is their misalignment with the distinctive causal structural prior of heterogeneous tabular data. In this paper, we address this gap by introducing a novel tabular foundation model, **TabFORGE**, built on pretrained **Tab**ular **FO**undational **R**epresentations for **GE**neration. TabFORGE is designed to utilise the implicitly learned causal information underlying diverse tabular datasets in a unified latent space induced by a pretrained causality-aware feature encoder. It further decouples latent modelling from decoding through a two-stage design: we first pretrain a score-based diffusion transformer, and then pretrain a denoising-aligned decoder using the denoised latent embeddings. This design elegantly mitigates the distribution shifts in latent embeddings that typically arise between training and inference. We evaluate TabFORGE comprehensively against 22 benchmark methods on 45 real-world datasets. Our results show that TabFORGE effectively learns and leverages generalisable tabular representations, enabling efficient generation of high-quality synthetic tabular data, particularly with strong structural fidelity.

Foundation models mark a profound paradigm shift in time series modeling, with task-specific models being superseded by general-purpose zero-shot models. Yet, current approaches primarily focus on forecasting, while real-world time series are often irregularly and partially observed, requiring models that can jointly forecast, impute missing values, and handle degraded sampling conditions. To address these challenges, we introduce TS-ICL, a novel probabilistic In-Context Learning encoder-regressor Transformer that unifies forecasting and imputation. TS-ICL formulates time series tasks as timestamp-aligned regression and naturally incorporates covariates by training on synthetic dependency structures generated from a novel causal data prior. Empirically, TS-ICL achieves a new state-of-the-art in imputation, while remaining competitive with leading forecasting foundation models across both univariate and covariate-aware benchmarks. It shows particularly strong performance in forecasting with partially observed look-back windows. *Code page*: https://anonymous.4open.science/r/tsicl-D3F1/.

Adapting CLIP for open-vocabulary video recognition necessitates a delicate balance between newly acquired video knowledge and the pretrained generalization. While existing studies pursue this generalization-specialization trade-off with additional regularizations or constraints, we argue that they overlook the deviation of representations beyond the fine-tuning data distribution, resulting in suboptimal adaptation effects. We believe such deviation is inherited from the inconsistency between the fine-tuning and evaluation objectives, where model optimization is restricted to the known training distribution but evaluated on unseen ones. In this paper, we introduce \emph{TACO}, a simple yet effective framework to mitigate the potential negative effects induced by this inconsistency. Our key insight is that adaptation should preserve OOD-relevant alignment beyond the training distribution. To this end, we propose \emph{Relative Structure Distillation}, which regularizes the relative geometry of the representation space and suppresses harmful alignment shift during training. We further decouple the representation space from the optimization space with a lightweight specialization projection, allowing task-specific adaptation without directly overspecializing the representations used at test time. \emph{TACO} establishes state-of-the-art performance on diverse benchmarks under cross-dataset and base-to-novel settings. Code will be released.

AI systems increasingly shape how individuals access and evaluate information, intensifying concerns about misinformation. Computational simulation offers a controlled complement to direct empirical study, with fine-tuned LLMs enabling powerful agent-based simulations. However, fine-tuning often induces broad behavioral shifts including bias drift, degenerate heuristics, and loss of task competence, which undermine experimental validity. The central challenge is inducing localized decision shifts without global behavioral collapse. We study this through a stability analysis of supervised fine-tuning, attenuating loss on selected training slices to shift false-claim acceptance while preserving overall task competence. We introduce FrameRef, a large-scale dataset of semantically equivalent claims with controlled surface framings across five dimensions (Authoritative, Consensus, Emotional, Sensationalist, Prestige), with human validation confirming systematic framing effects on claim acceptance. We identify stability regimes in which targeted error shifts can be induced without degrading aggregate accuracy or calibration. A sequential exposure task over FrameRef shows that small framing-conditioned shifts compound into substantially different cumulative outcomes under feedback, but largely disappear when feedback is removed, confirming that interventions modify specific decision tendencies rather than global performance. We release code, LoRA adapters, and data at https://github.com/anonymousauthor2352/frameref.

PDE foundation models (PDE-FMs) promise general-purpose surrogate simulation, yet deploying a pretrained model to a new physical regime inevitably introduces distribution mismatch, demanding task-specific post-training. The dominant approach, supervised fine-tuning (SFT), is fundamentally off-policy: the model trains on pre-generated states but is evaluated autoregressively on its own. In chaotic regimes, this mismatch is catastrophic as per-step errors compound exponentially along the Lyapunov spectrum, and exact pointwise trajectory matching beyond the predictability horizon becomes an ill-posed training signal. To resolve this, we introduce **Solver-as-Teacher (SaT)**, an on-policy post-training framework for PDE-FMs. During training, the student rolls out autoregressively; a numerical solver branches multi-step correction targets from each student-visited state; and the student is updated against these solver corrections, learning to correct its own rollout dynamics. We instantiate SaT in a unified post-training suite with SFT, physics-informed temporal alignment, and learned-teacher on-policy distillation. Across seven 2D/3D PDE benchmarks and six pretrained PDE-FMs, SaT variants achieve the lowest 50-step rollout RMSE in 23 of 24 in-distribution model--task pairs. The best SaT variant reduces RMSE over SFT up to 90.0\%, and SaT gives the lowest RMSE on all seven OOD parameter-shift benchmarks.

Treating patients with combinations of drugs reduces the risk of resistance to any individual drug. Finding effective combinations is difficult because the large search space makes combinatorial screens prohibitively expensive, time consuming, and often technically infeasible. Predictive models can fill this gap, yet existing methods typically require molecular profiling of each sample and per-cohort training, limiting their applicability when time and tissue are scarce. To address this challenge, we introduce ScreenShot, a hierarchical transformer pretrained on 40 drug screening datasets covering 3,700 drugs and 6,000 biological samples, whose architecture mirrors the nested structure of screening data. Given a few-shot context of observations from a new patient, ScreenShot predicts the response of the sample to combination therapies through in-context learning, operating directly on functional measurements with no fine-tuning and no molecular profiling. On four held-out datasets, ScreenShot outperforms all baselines in both prediction accuracy and identification of selectively effective treatments. ScreenShot's internal representations are directly useful for experimental design: we use them to drive a weighted $k$-means++ active learning strategy that selects which experiments to run, achieving the same hit detection as uniform screening with a third of the budget. Source code and interactive dashboard: https://github.com/AnonymousAccount3/screenshot

When deploying large language models (LLMs) to safety-critical applications, uncertainty quantification (UQ) is of utmost importance to self-assess the reliability of the LLM-based decisions. However, such decisions typically suffer from overconfidence, particularly after parameter-efficient fine-tuning (PEFT) for downstream domain-specific tasks with limited data. Existing methods to alleviate this issue either rely on a Laplace approximation based post-hoc framework, which may yield suboptimal calibration depending on the training trajectory, or variational Bayesian training that requires multiple complete forward passes through the entire LLM backbone at inference time for Monte Carlo estimation, posing scalability challenges for deployment. To address these limitations, we build on the Bayesian last layer (BLL) model, where the LLM-based *deterministic* feature extractor is followed by random last-layer parameters for uncertainty reasoning. Since existing low-rank adapters (LoRA) for PEFT have limited expressiveness due to rank collapse, we address this with Polar-decomposed Low-rank Adapter Representation (PoLAR), an orthogonalized parameterization paired with Riemannian optimization to enable more stable and expressive adaptation. Building on this PoLAR-BLL model, we leverage the variational (V) inference framework to put forth a scalable Bayesian fine-tuning approach which jointly seeks the PoLAR parameters and approximate posterior of the last-layer parameters via alternating optimization. The resulting PoLAR-VBLL is a flexible framework that nicely integrates architecture-enhanced optimization with scalable Bayesian inference to endow LLMs with well-calibrated UQ. Our empirical results verify the effectiveness of PoLAR-VBLL in terms of generalization and uncertainty estimation on both in-distribution and out-of-distribution data for various common-sense reasoning tasks.

Geometric foundation models show promise in 3D reconstruction, yet their progress is severely constrained by the scarcity of diverse, large-scale 3D annotations. While Internet videos offer virtually unlimited raw data, utilizing them as a scaling source for geometric learning is challenging due to the absence of ground-truth geometry and the presence of observational noise. To address this, we propose SAGE, a framework for Scalable Adaptation of GEometric foundation models from raw video streams. SAGE leverages a hierarchical mining pipeline to transform videos into training trajectories and hybrid supervision: (1) Informative training trajectory selection; (2) Sparse Geometric Anchoring via SfM point clouds for global structural guidance; and (3) Dense Differentiable Consistency via 3D Gaussian rendering for multi-view constraints. To prevent catastrophic forgetting, we introduce a regularization strategy using anchor data. Experiments on applying SAGE to two architecturally distinct backbones (including both MV-DUSt3R and VGGT) show that Chamfer Distance is consistently reduced by 20-42% on unseen benchmarks (7Scenes, TUM-RGBD, Matterport3D). SAGE establishes Internet video as a viable and scalable adaptation source for geometric foundation models.

While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature extraction. This approach fundamentally diverges from human perception. Consequently, current models often suffer from ``semantic blindness,'' overfitting to transient noise while failing to leverage invariant soft biometrics, and struggle to capture temporal motion signatures. To bridge this gap, we propose SapiensID 2.0, a human recognition framework enriched with both semantic and temporal awareness. To overcome the lack of soft-biometric annotations, we transfer zero-shot semantic knowledge from Multimodal Large Language Models (MLLMs) into a discriminative embedding space. We resolve the dimensional mismatch between these spaces using Invariant Trait Alignment (ITA) to distill core persistent traits, and Transient Noise Disentanglement (TND) to decouple artifacts like clothing. Furthermore, we design a Kinematic Semantic Attention Head (K-SAH) that extends spatial attention across temporal windows. By tracking semantic patches over time, K-SAH captures rich kinematic signatures without requiring large-scale video datasets. Extensive experiments demonstrate that SapiensID 2.0 achieves state-of-the-art performance across image- and video-based person re-identification and gait recognition, while maintaining robust face recognition capabilities.

Low-rank adaptation (LoRA) has attracted significant attention in the parameter-efficient fine-tuning (PEFT) landscape. However, its design suffers from two fundamental limitations. First, its invariance manifold induces flat directions in the loss landscape, creating an optimization bottleneck for adaptive optimizers. Second, its effective rank scales only linearly with the parameter budget. In this paper, we propose Scalable Morpho Adaptation (SaMA), a PEFT method grounded in a principled generalization of the Kronecker product. By relaxing the shared-block constraint in the perfect-shuffle factorization, SaMA spans from low to full rank and achieves effective rank that scales \quadratically in parameters. Furthermore, we show that its invariance manifold collapses to a diagonal group, making it structurally more optimizer-friendly. Empirically, SaMA consistently outperforms strong PEFT baselines on commonsense and arithmetic reasoning benchmarks while being more parameter-efficient.

Self-speculative decoding offers a plug-and-play route to accelerating autoregressive large language model (LLM) inference, but its practical success hinges on identifying effective layer-skipping configurations online without retraining or offline profiling. Existing methods mainly optimize agreement-based proxies that capture draft-target consistency, yet overlook intrinsic runtime differences across draft configurations. Consequently, configurations with similar acceptance can still yield substantially different realized decode throughput. We present SPIRIT, a speed-driven online adaptation framework for self-speculative decoding that directly optimizes realized decode throughput, measured as generated tokens per second, using window-level runtime feedback. Our key insight is that speed is not determined by agreement alone: it is jointly shaped by draft-target consistency and the intrinsic speed advantage of the draft configuration. Empirically, the skip ratio serves as a first-order control variable, capturing the dominant consistency-speed trade-off while sharply shrinking the search space over layer-skipping patterns. SPIRIT therefore first identifies an effective skip ratio and then refines the layer-skipping pattern within it. Combined with length-aware normalization and shift-aware profile-based adaptation, this design enables rapid adaptation to task shifts in non-stationary request streams. Across diverse tasks and model scales, SPIRIT consistently outperforms strong training-free baselines and achieves up to 1.74x speedup while preserving the target model's output distribution, establishing a practical foundation for plug-and-play, high-throughput LLM inference.

Fine-tuning-as-a-service enables users to adapt aligned large language models (LLMs) to specialized tasks, but malicious fine-tuning can erode refusal behavior while preserving task performance on legitimate inputs. We revisit recent layer-wise safety diagnostics and find that safety sensitivity is signed: scaling different layers can strengthen refusal, weaken it, or have little effect. Motivated by this observation, we propose SLDR, a post-fine-tuning defense based on Selective Layers Recovery and Dynamic Routing. SLDR trains a LoRA recovery adapter only on the layers with the maximum and minimum sensitivity scores in the signed spectrum, and uses representation-based dynamic routing inference to activate the adapter only for malicious queries. Across four model architectures, five downstream tasks, and four harmful benchmarks, SLDR substantially reduces harmful outputs while preserving downstream utility. On Llama3.1/SST2, SLDR reduces the average harmful score from 11.54 to 0.08 while maintaining downstream accuracy, and the harmful score remains near zero under poisoning ratios up to 0.9.

Low-Rank Adaptation (LoRA) enables efficient adaptation of large pre-trained models to downstream tasks by parameterizing weight updates with low-rank matrices. In this paper, we investigate the limitations of the LoRA parameterization from a geometric perspective. Specifically, we show that when a full fine-tuning gradient is backpropagated to the low-rank matrices, it undergoes anisotropic scaling driven by their singular values. We argue that this phenomenon is undesirable because it distorts the full fine-tuning gradient by skewing it toward dominant singular directions while suppressing others. Our analyses demonstrate that anisotropic gradient scaling reduces the effective rank of the low-rank matrices' gradients and results in suboptimal alignment between the full fine-tuning gradient and its low-rank approximation in LoRA, thereby exacerbating the gap to full fine-tuning. To address these limitations, we propose a new low-rank parameterization, SDS-LoRA, which structurally decouples singular values from the backward pass. Our method ensures that the full fine-tuning gradient backpropagates only through the orthonormal bases of the low-rank matrices' subspaces, independent of their scales. Convergence analysis demonstrates that while LoRA’s convergence rate degrades with the condition number of the low-rank matrices, SDS-LoRA remains independent of it. Experimental results across natural language and vision benchmarks show that SDS-LoRA improves loss convergence and reduces the gap to full fine-tuning, significantly enhancing adaptation performance.