NeurIPS 2026 论文列表

**Faithfulness**—how precisely a generated image aligns with its prompt—is increasingly central to the real-world utility of text-to-image (T2I) models. Existing faithfulness benchmarks, however, rely on simple atomic instructions, where top-tier systems already achieve near-perfect scores. As T2I models enter creative workflows, users increasingly issue multi-faceted requests involving intricate spatial relationships, stylistic constraints, and complex text rendering. In this setting, a single binary VLM-judge score no longer reveals **which** specific constraints the model fails to satisfy. We introduce **Arena-T2I Hard**, a 310-prompt stress benchmark drawn from real arena T2I logs, with approximately 30 decomposed yes/no constraints per prompt spanning six categories, including text rendering. The strongest closed-source system we evaluate reaches **0.855**, with a **33 pp** performance gap across 11 systems, demonstrating substantial discriminative power. Moreover, high public-arena rankings fail to predict faithfulness, confirming that holistic Bradley–Terry (BT) preference scores prioritize aesthetics over fine-grained prompt adherence. We propose a **dependency-aware checklist reward** that decomposes each prompt into a DAG of yes/no questions and zeroes descendants of failed parents, turning faithfulness into a per-constraint training signal. Combined with a BT aesthetic reward via **group-decoupled normalization (GDPO)**, which standardizes each reward within its rollout group so neither collapses, our recipe attains a strictly better faithfulness–aesthetics trade-off on **SD3.5-Medium** and **FLUX.1-dev** under MMRB2 pairwise comparisons than every single-reward, naive weighted-sum, or 4-reward BT-ensemble baseline. We release **Arena-T2I Hard** as a public stress benchmark.

As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 18.8\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Code is at \url{https://anonymous.4open.science/r/anymo}.

Echocardiography segmentation plays a central role in quantitative cardiovascular assessment. However, automated echocardiography segmentation remains challenging due to speckle noise, poorly defined anatomical boundaries, acquisition variability, and the limited anatomical coverage of existing benchmarks. Conventional segmentation methods demonstrate limited accuracy under such low image quality and domain shifts. Inspired by the observation that expert cardiologists can maintain reliable anatomical delineation by leveraging their structural understanding of the cardiac anatomy, we propose that incorporating anatomical priors into echocardiography segmentation models can improve both precision and robustness. To this end, we propose AST-Seg, an anatomy-aware spatio-temporal framework for echocardiography segmentation. AST-Seg introduces an anatomy-aware feature encoder that regularizes transformer attention with anatomy-derived spatial-relation priors, and a Deformable Spatio-Temporal Mamba module that captures localized and periodic cardiac motion through adaptive spatial aggregation followed by temporal state-space modeling. In addition, we curate an Echocardiography Anatomy (EA) dataset with pixel-level annotations for 12 clinically relevant cardiac anatomy, enabling comprehensive multi-structure cardiac interpretation. Extensive experiments on in-distribution CAMUS and EA datasets, as well as an out-of-distribution point-of-care echocardiography dataset, demonstrate that AST-Seg provides domain-generalized segmentation with improved precision.

Atmospheric pollution risk inference is not merely generic time-series extrapolation: target-day risk depends jointly on numerically preserved pollutant state, exogenous meteorological regime, and directed cross-city transport. Existing time-series models are strong sequence learners, graph-based air-quality models often rely on static or weakly directed spatial priors, and multimodal methods commonly merge heterogeneous evidence without specifying its functional role. We present \ourmodel{}, a transport-aware multimodal evidence-fusion framework that assembles an atmospheric evidence mosaic from numerical pollutant/time states, compact precipitation text, and wind-aligned graph structure. The model preserves historical PM$_{2.5}$ and time features as numerical prefix tokens, compresses recent precipitation into prompt-side context, constructs a daily wind-driven DAG to organize upwind-to-downwind evidence, and refines city embeddings through post-pooling DAG residual propagation with root-sensitive mechanisms. We analyze \ourmodel{} on six PM$_{2.5}$ risk-inference benchmarks under a unified year-based split. The results provide protocol-bounded evidence that transport-aligned structure and role-separated interfaces yield useful risk-inference signals, while leaving no-oracle evaluation as future work.

Personalizing Large Language Models (LLMs) directly on local edge servers is becoming increasingly important for privacy-preserving and context-aware applications. However, this potential is bottlenecked by hardware resources: limited GPU memory permits only a fixed number of train-ready LoRA adapters, while compute constraints enforce sequential fine-tuning updates. This creates a critical challenge: scheduling scarce update opportunities across a dynamic stream of resident tenants to improve overall model quality and scheduling stability. Crucially, standard Multi-Armed Bandit (MAB) algorithms fail to distinguish between low-potential tenant streams and fully saturated adapters, leading to wasteful exploration on tasks that offer little further gain. To address this, we propose WCA-UCB (Windowed Convergence-Aware UCB), a scheduler designed for this piecewise-converging non-stationary environment. By formally modeling fine-tuning as a slot-constrained bandit problem with piecewise-converging rewards, WCA-UCB detects when an adapter has saturated to pause its training and resets stale statistics after tenant replacement or harmful drift. We prove a dynamic regret bound of $\tilde{O}(\log T)$ and validate the system on an edge-like single-GPU LoRA prototype running Qwen2.5-1.5B. Results demonstrate that WCA-UCB reduces model perplexity by $2.6$% to $8.6$% and uses up to $5.8\times$ fewer training-target switches than strong non-stationary baselines. These results highlight the necessity of convergence-aware scheduling for scalable local LLM personalization.

Spherical Transformers have emerged as a promising framework for panoramic semantic segmentation (PASS) by operating directly on spherical geometry and alleviating projection-induced distortions. However, existing architectures rely on assumptions of canonical spherical structure and stable viewpoints, which are frequently violated in real-world $360^\circ$ imagery due to unconstrained camera motion, introducing significant contextual and geometric ambiguity. Consequently, they lack adaptive mechanisms to model such ambiguity, limiting robustness to unseen spherical transformations. In contrast, biological perception is inherently ambiguity-aware: rather than estimating uncertainty probabilistically, it adapts to fluctuations in cue reliability caused by geometric and contextual variations, enabling stable interpretation under complex visual transformations. Motivated by these observations, we first present a systematic analysis of existing PASS architectures under various unseen spherical transformations. Following this, we introduce AdapToPASS, a novel, bio-inspired Spherical Transformer that adaptively models contextual and geometric ambiguities for robust panoramic semantic segmentation. At the core of AdapToPASS are the Adaptive Spherical Attention (AdaSpA) blocks that dynamically modulate attention according to local contextual ambiguity, mimicking the adaptive, context-driven perception of biological vision. To address geometric ambiguities, AdapToPASS employs Bifocal Spherical Representation that reconciles the trade-off between field of view and spatial resolution; along with boundary supervision to emulate the boundary-sensitive nature of biological vision. We evaluate AdapToPASS in both indoor and outdoor semantic segmentation, where it consistently outperforms prior state-of-the-art methods. We further validate AdapToPASS under unseen spherical transformations, where it demonstrates strong robustness and surpasses the next-best method by +13.38% relative improvement in mIoU on Stanford2D3D and +18.77% on WildPASS. Additionally, we introduce a lightweight variant, AdapToPASS-Tiny, with fewer than 2M parameters, which surpasses compact baselines while retaining robustness to spherical transformations.

Speculative decoding accelerates generative inference of large language models (LLMs) by using a small draft model to propose multiple candidate tokens, which are then verified in parallel with the target model in a single decoding iteration. While the state-of-the-art method of tree-based speculative decoding helps improve generation throughput over non-speculative inference, deploying it in LLM serving systems often yields suboptimal performance due to dynamically changing serving conditions. In particular, our analysis shows that the optimal tree configuration---the one that maximizes performance---varies with two key serving conditions: request rate and per-request characteristics. Based on the analysis, we present AdaTree, a plug-in component for LLM serving systems that dynamically adapts tree configurations to varying serving conditions. AdaTree predicts model execution time and acceptance length across different tree configurations and selects the one that maximizes speculative decoding efficiency. To capture the non-linear relationship between tree configuration and model execution time, AdaTree employs decision trees as its core modeling primitive. Our evaluation shows that AdaTree consistently outperforms both chain-based and static tree-based speculative decoding across diverse serving conditions.

Large language models (LLMs) have achieved remarkable success while introducing critical energy bottlenecks that challenge sustainable deployment. Spiking neural networks (SNNs) provide a promising approach for energy-efficient spiking LLMs via ANN-to-SNN (A2S) conversion. Among various spike coding methods, time-to-first-spike (TTFS) coding is particularly appealing as it conveys information with a single spike, further reducing energy consumption. However, existing TTFS-based A2S conversion relies on continuous-time assumptions, requiring prohibitively large latencies (e.g., 4096 time steps) to approximate ANN's continuous values. This dependency leads to unacceptable inference delays in LLMs, posing significant challenges for developing practical temporal-coding spiking LLMs. In this paper, we propose a discrete analysis framework for TTFS-based LLMs, reformulating TTFS-based A2S conversion from continuous lossless conversion into discrete error-aware representation. Building on this analysis, we establish an equivalence between discrete TTFS-based SNNs and static quantized ANNs, allowing TTFS conversion to be handled through quantization techniques. Motivated by this equivalence, we introduce Quantization-Consistent ANN-to-SNN (QC-A2S) conversion, which combines static quantization with discretization-compatible TTFS neurons to achieve latency-efficient and high-performance temporal-coding spiking LLMs. Extensive evaluations on representative LLMs across multiple benchmarks demonstrate competitive performance with substantially reduced inference latency.

Diffusion and rectified flow (RF) models generate high-fidelity images and videos, but their iterative velocity-field evaluations are computationally expensive. Existing caching methods accelerate sampling by skipping timesteps, yet their coarse approximations introduce accumulated errors over long skip intervals and degrade quality under aggressive acceleration. We propose \textbf{TACache (Trajectory-Aware Cache)}, a training-free acceleration framework following a \textbf{skip-then-compensate} paradigm. TACache performs an orthogonal decomposition of discrete velocity acceleration along the RF trajectory into parallel and orthogonal components, isolating the magnitude and directional sources of per-step approximation error. The framework operates in two stages: \emph{offline}, cumulative variation thresholds on the magnitude and direction indicators yield the skipping schedule and bound how far each skipped interval may extend; \emph{online}, at each skipped step the offline statistics are combined with the sample's historical orthogonal direction to reconstruct the skipped velocity without additional forward passes. Experiments on BAGEL, FLUX.1-dev, and Wan2.1-1.3B show that TACache achieves up to $4.14\times$ speedup on text-to-image generation and $2.11\times$ speedup on text-to-video generation, with consistent improvements over prior cache-based methods on all reference-based fidelity metrics. Code will be released soon.

Text-based image editing has recently been reinterpreted in large multimodal transformers as conditional generation, where source image tokens are concatenated with text and noise tokens. While effective, this design incurs substantial computational overhead in attention layers. To mitigate this inefficiency, we propose {\em TokenDrop}, an efficient editing framework that shift the computational burden from dense source-token conditioning to a lightweight regularized sampling update. By transferring the influence of source tokens into a closed-form sampling update, the method preserves source consistency with negligible cost. To support this regularized dynamics, we introduce a deviation-guided adaptive masking strategy that selectively drops redundant tokens while maintaining editing fidelity. Across FluxKontext and Qwen-Image-Edit, our training-free method achieves an average 22.4\% improvement in inference speed on PIEBench, while better preserving non-edited regions. The method delivers up to 1.8$\times$ speedup at 1024$^2$ resolution and 2$\times$ speedup at 2048$^2$ resolution. The code will also be released publicly.

Modern neural-network optimizers occupy a three-way design space: online adaptivity, architecture-aware geometry, and acceleration. We introduce AcceleGrad#, an accelerated linear-coupling template that couples an adaptive anchor with a norm-induced sharp, mirror, or linear-minimization geometry branch. The scalar-anchor specialization recovers the accelerated $O(1/K^2)$ rate for smooth convex objectives, while the diagonal-AdaGrad clipped-LMO training variant admits local $\mu$-Kurdyka-Lojasiewicz certificates: a generic conservative $O(K^{-1/4})$ rate and, in a SCION cross-entropy regime, a $O(K^{-1/3})$ last-iterate rate to a self-bounded mini-batch noise level. On image classification and language modeling, the training variant improves over Euclidean AcceleGrad and remains competitive with recent non-Euclidean optimizers. Using stochastic gradients directly in spectral oracles also reduces polar-computation time and optimizer overhead in practice.

Post-training quantization (PTQ) compresses large language models (LLMs) but remains largely semantic-agnostic, relying on global activation magnitude to guide precision allocation. This overlooks the functional heterogeneity of Transformer representations, where semantically meaningful channels are sparsely activated while structurally dominant channels exhibit large but context-insensitive activations. We propose Agent-guided Semantic-aware Quantization (ASQ), a PTQ framework that conditions on semantically informative positions identified by an auxiliary LLM. A dual-pathway scoring mechanism captures both activation selectivity and attention routing toward these positions, producing channel-wise importance scores for scaling. This yields a closer approximation to a semantically conditioned objective and consistently improves low-bit (e.g., W4A16) quantization performance across LLMs. Extensive experiments show consistent gains over state-of-the-art PTQ, preserving general reasoning and even surpassing FP16 performance on several benchmarks in specialized domains, while enabling training-free, privacy-preserving self-quantization with zero inference overhead.

Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for agentic settings, where actions directly interact with external environments and their effects can be irreversible and costly. We propose ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, a framework that decouples exploration from commitment by enabling test-time exploration through simulated interactions prior to real-world execution. This design allows extending inference-time computation to improve action-level reliability and robustness without incurring environmental risk. We further show that naive LLM-based simulators struggle to capture rare but high-impact failure modes, substantially limiting their effectiveness for agentic decision making. To address this limitation, we introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions via targeted data generation and rebalanced training. Experiments on multi-turn and multi-step agentic benchmarks demonstrate that iterative simulation substantially improves agent reliability, and that risk-aware simulation is essential for consistently realizing these gains across models and tasks.

Zeroth-order (ZO) optimization enables large-language-model fine-tuning without storing backpropagation activations, while LoRA supplies compact trainable adapters. Combining them creates a rank paradox: increasing LoRA rank improves adapter capacity, but standard two-point ZO either perturbs a rank-dependent number of coordinates or, under atomwise updates, can make the finite-difference signal unobservable. This paper shows that the bottleneck is a measurement-topology problem rather than a need for an external subspace. LoRA already decomposes into matched rank-$1$ atoms, each a complete factor-coordinate block of dimension $d_\text{out}+d_\text{in}$. Querying one atom per step keeps the stored adapter rank $r$ while removing $r$ from the single-query perturbation dimension. The naive atomwise query is still miscalibrated: if it inherits canonical LoRA scaling $\alpha/r$, the active finite-difference signal shrinks as $1/r$ and the active finite-difference signal-to-noise ratio (FD-SNR) as $1/r^2$, producing directional collapse under a fixed residual evaluation-noise floor. AR1-ZO pairs alternating rank-$1$ atom queries with topology-aware scaling $\gamma=\alpha r$, restoring rank-invariant active signal without auxiliary bases, activation hooks, curvature estimates, or extra forward queries. Theory proves atom minimality, rank-independent active query dimension, directional collapse and restoration, and the remaining rank dependence as an amortized coverage cost. Experiments on OPT and Qwen3 models validate the signal mechanism and show that AR1-ZO makes high-rank LoRA effective among matched-budget ZO methods under the standard two-forward-pass query budget.

Post-training quantization is a standard tool for the efficient deployment of large language models (LLMs). In safety-aligned models, it introduces a critical and underexplored risk: preserving utility under compression does not ensure that safety alignment is retained. Small perturbations from quantization can disproportionately degrade refusal to harmful prompts, leading to increased compliance with harmful prompts or over-refusal of benign prompts, even when aggregate performance appears stable. This gap reveals a fundamental limitation of existing quantization approaches, which primarily optimize for utility while overlooking failures due to safety alignment degradation. This paper presents ACQueReLlo, a reinforcement learning framework for mixed-precision post-training quantization. It formulates block-wise bit allocation as a constrained Markov decision process and learns a quantization policy under explicit constraints on utility, harmful prompt refusal, benign prompt over-refusal, and overall bit budget. To ensure tractable training, the policy relies on proxy evaluations over sampled subsets of utility, harmful, and benign prompts. Experiments on various LLMs show that the learned policy achieves a better overall balance between compression, utility, and safety alignment than uniform quantization and other baselines.

Performance predictors are pivotal in accelerating evaluation in NAS. Cross-dataset predictors are trained on (dataset, architecture, performance) triplets to efficiently estimate architectural performance for unseen datasets. However, existing cross-dataset predictors rely on a naive concatenation of dataset and architecture features. This fusion ignores the architecture-aware nature, where the same dataset manifests as distinct representations under different architectures, thereby limiting generalization. To tackle this issue, we propose ACDP, an Architecture-aware Cross-Dataset Performance Predictor that dynamically adjusts dataset feature extraction based on specific architecture. Initially, ACDP develops an elastic mechanism to extract raw dataset traits, providing a configurable lever for the trade-off between efficiency and accuracy. Furthermore, ACDP leverages a hypernetwork to determine dataset feature extractor parameters conditioned on architectural features, transforming raw dataset traits into tailored representations. ACDP excels in both specialization and generalization on six unseen datasets. Notably, in NAS-Bench-201, ACDP achieves 0.758/0.741 Kendall's Tau on CIFAR-10/100, surpassing single-dataset SOTAs trained with over 100 target samples. In MobileNetV3, ACDP discovers architectures outperforming cross-dataset SOTAs, notably gaining +2.04% accuracy on Aircraft. Code: https://anonymous.4open.science/r/ACDP/

3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time photorealistic novel view synthesis. However, its training-time densification remains largely heuristic, often resulting in redundant Gaussian proliferation and inaccurate geometric reconstruction. Existing methods predominantly rely on first-order screen-space gradients to trigger cloning and splitting, which fail to distinguish true geometric discontinuities from high-frequency textures or noisy residuals. To address this limitation, we propose ACD-GS, an Asymmetric Curvature-aware Densification framework for compact and geometry-aware 3D Gaussian optimization. Our key insight is that second-order depth responses provide a more reliable structural prior for density control, enabling asymmetric regulation of cloning and splitting to better capture geometric discontinuities. Building on this, we introduce a multi-view photometric consistency constraint to suppress transient single-view errors, together with a capacity-aware adaptive regulation strategy that balances densification and pruning during training. Unlike post-hoc compression or quantization-based approaches, ACD-GS directly prevents redundant Gaussian generation during training, yielding inherently compact representations. Extensive experiments demonstrate that our method reduces nearly 60\% of Gaussian count and storage while maintaining competitive rendering fidelity, consistently outperforming existing methods across multiple datasets. Our code is available in https://anonymous.4open.science/r/ACD_GS-0E2C

Differential Privacy (DP) bounds the privacy leakage of a mechanism against worst-case membership inference, but the precise tradeoff between complex adversarial models and DP protections remains poorly understood. In this paper, we present a unified framework that generalizes the patchwork of existing bounds across membership inference, attribute inference, and data reconstruction attacks. Crucially, our framework is the first to evaluate attacks that target multiple individuals simultaneously and measure success beyond exact matches under a single cohesive bound. Our bounds capture this broad family of previously unexplored attack settings by relying solely on the privacy parameters and the adversary's baseline success rate (i.e. its prior without access to the mechanism's output). To illustrate this, we compare our high-probability guarantees to empirical attacks in two novel settings: extracting multiple non-uniform secrets (passwords and PII) from DP-finetuned language models, and reconstructing tabular data from noisy marginals. Ultimately, this framework provides a rigorous theoretical foundation to investigate the risk landscape of DP algorithms in new adversarial settings.

Variational quantum algorithms (VQAs) rely on gradient-based optimization over parameterized circuits whose loss landscape lives on a manifold of quantum states. Quantum natural gradient (QNG) accounts for this geometry through the quantum Fisher information matrix (QFIM). However, estimating the full QFIM is prohibitively expensive within practical quantum circuit shot budgets. Existing tractable approximations either discard inter-block couplings, as in block-diagonal QNG, or replace the QFIM with a measurement-induced classical Fisher surrogate, as in random natural gradient (RNG). We propose a locality-aware surrogate that targets the QFIM through its covariance representation in terms of propagated generators, retaining a structured subset of informative cross-parameter couplings while remaining practical to estimate under finite-shot budgets. Our construction combines three ingredients: weight-$k$ Pauli truncation to control estimation variance, support-overlap masking to exploit sparsity, and single-dataset classical-shadow estimation that reuses the same shadow dataset across all retained metric entries. Truncation level $k$ provides a bias--variance tradeoff: larger $k$ captures more of the propagated-generator structure but increases estimation cost, making it a budget-constrained design choice. Circuit parameters whose truncated generators remain nonzero form an active set receiving surrogate-QNG updates, while the rest follow standard gradient descent. We provide finite-shot concentration bounds for the shadow-estimated surrogate and convergence guarantees to a stationary neighborhood under standard smoothness assumptions. Experiments on variational quantum eigensolver (VQE) instances with local ans\"atze for Heisenberg and Ising spin chains show improved convergence per cumulative shot relative to SGD, Adam, block-diagonal QNG, and RNG baselines under finite-shot budgets, including on systems up to 16 qubits.