NeurIPS 2026 论文列表

Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phenomenon for gradient flow in a linear teacher--student setting. In this framework, learning occurs when an isolated eigenvalue separates from a noisy bulk, before eventually disappearing in the overfitting regime. The key ingredient is anisotropy in the input covariance, which induces fast and slow directions in the learning dynamics. In a two-block covariance model, we derive the full time-dependent bulk spectrum of the symmetrized weight matrix through a $2\times 2$ Dyson equation, and we obtain an explicit outlier condition for a rank-one teacher via a rank-two determinant formula. This yields a transient Baik--Ben Arous--P\'ech\'e (BBP) transition: depending on signal strength and covariance anisotropy, the teacher spike may never emerge, emerge and persist, or emerge only during an intermediate time interval before being reabsorbed into the bulk. We map the corresponding phase diagrams and validate the theory against finite-size simulations. Our results provide a minimal solvable mechanism for early stopping as a transient spectral effect driven by anisotropy and noise.

Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learning are known to emerge abruptly past a certain model scale. In this paper, we show that emergent capabilities arise at variable intervals throughout training, with larger models acquiring capabilities earlier on average. We demonstrate that the emergence of capabilities such as pattern completion and indirect object identification corresponds to the abrupt learning of task-relevant attention patterns. To isolate this phenomenon, we train transformer models on synthetic linear map and cellular automata datasets, and we show that the difficulty of learning attention patterns depends on context length and pattern sparsity. Moreover, scaling the number of attention heads improves learning efficiency on our synthetic tasks, while increasing the head dimension yields diminishing returns past a minimum capacity. We additionally investigate architectures with alternative attention mechanisms, showing that MLP-Mixer outperforms a transformer on linear map tasks with complex attention patterns. Our findings provide a mechanistic insight into emergence, showing that downstream capabilities arise abruptly due to the intrinsic difficulty of learning sparse attention patterns in transformer models.

Machine learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access (https://pypi.org/project/raman-data/), evaluation protocols and code (https://pypi.org/project/raman-bench/), as well as a live leaderboard (https://huggingface.co/spaces/HTW-KI-Werkstatt/RamanBench). It unifies 77 datasets (including 17 first released with this benchmark) across four domains, comprising 325,862 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), tabular foundation models (TFMs; e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFMs consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no single method dominates across these heterogeneous datasets, revealing that Raman spectroscopy remains an open challenge for machine learning. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.

Noisy-label methods often estimate sample reliability from forward-space signals such as loss, confidence, or entropy. These signals indicate whether a sample is difficult to predict, but they do not directly test whether its observed label induces a reliable parameter update. This gap matters because hard clean samples and mislabeled samples can have similar loss while inducing different updates. We recast reliability estimation as diagnosis of the observed-label update. The sample-wise empirical Fisher trace gives a backward-space measure of update energy: for the classifier layer, it factorizes into a prediction-residual term and a feature-sensitivity term, so it captures information beyond scalar loss. Trace, however, is still a radial magnitude signal and cannot decide whether a large update is useful or harmful. We therefore propose Relative Geometric Conflict (RGC), which compares the observed-label gradient with a reference gradient induced by an EMA teacher. The conflict term helps distinguish large but aligned hard-clean updates from large conflicting updates caused by corrupted labels. Across synthetic and real-world noisy-label benchmarks, RGC improves hard-clean preservation and accuracy under our evaluation protocol.

Masked Autoencoders (MAE) have emerged as a powerful self-supervised paradigm in vision, yet their direct application to radar perception remains bottlenecked by a fundamental domain gap. Specifically, while conventional MAEs are optimized for RGB images using isotropic grid patching and standard mean squared error (MSE) loss, radar range-azimuth (RA) heatmaps exhibit sinc-oriented anisotropic spatial structures as well as complex physical artifacts, such as heavy-tailed multipath interference and sidelobe spikes. In this paper, we propose RadarMAE, the first radar-centric MAE tailored specifically to the physical properties of radar signals. We present an axial tokenization strategy that preserves the resolution disparities and continuous sinc-shaped lobes of RA maps. Furthermore, considering that the range and azimuth dimensions exhibit distinct physical artifacts, we decouple their reconstruction objectives by introducing a multipath distribution loss along the range axis to capture heavy-tailed multipath reflections as well as a sidelobe distribution loss along the azimuth axis to learn deterministic sidelobe spikes. Our extensive experiments demonstrate that injecting these domain-specific physical priors enables robust representation learning for radar object detection. Notably, our method achieves a state-of-the-art 64.17% bird's-eye-view (BEV) AP on the K-Radar dataset using only a single-frame RA map setting, significantly outperforming the previous baselines.

Radar-based human pose estimation is a promising alternative to camera-based perception, but remains challenging due to sparse observations, noisy measurements, and severe pose ambiguity. Existing methods infer both global skeletal structure and flexible peripheral joints from sparse radar observations, often compromising structural plausibility and fine-grained local accuracy. Motivated by the coarse-to-fine nature of human visual perception, we propose RadarFlowPose, a framework that decomposes radar-based human pose estimation into coarse pose prior generation and flow-based refinement. The coarse model first produces a structurally plausible and temporally coherent pose prior from sparse radar observations, providing a global initialization for subsequent refinement. Instead of estimating the full pose from scratch, the refinement stage focuses on correcting the remaining local ambiguities and motion-induced errors. By incorporating localised Doppler motion cues into the flow-matching process, the model performs motion-aware residual refinement around the coarse prior, leading to more accurate and anatomically consistent pose estimates. Experiments on three benchmark radar pose datasets show consistent effectiveness, especially in motion-sensitive and fine-grained joint-level evaluation.

Non-contrast CT (NCCT) is widely used in clinical practice, yet its limited soft-tissue contrast often renders visual information insufficient for accurate diagnosis. In routine radiology workflows, NCCT is commonly accompanied by contrast-enhanced CT, radiology reports, and pathology reports, which provide complementary knowledge at different levels, from structural and morpho-functional cues to semantic findings and pathological evidence. However, existing pretraining paradigms address this challenge only partially, each introducing complementary but still limited constraints on NCCT representations. To address this limitation, we propose RadOmni, a unified omni-pretraining framework that injects multimodal radiology knowledge into representation learning for non-contrast CT. RadOmni formulates a curriculum consolidation learning strategy, in which knowledge is progressively injected according to optimization difficulty and knowledge level through four stages: masked image modeling on non-contrast CT, cross-phase transfer of anatomical structures and enhanced patterns from contrast-enhanced CT, report-guided semantic learning, and pathology-informed diagnostic enhancement. To mitigate optimization conflicts across heterogeneous objectives and reduce knowledge forgetting, each stage retains and jointly optimizes the objectives from preceding stages, while early encoder layers are selectively frozen to preserve generic representations. To enable such multi-source pretraining, we curate Omni-CT10K, a large-scale CT dataset with more than 10K NCCT scans featuring clinically co-occurring multimodal supervision. Extensive experiments on Omni-CT10K, two in-house benchmarks from different centers, and the public MSD benchmark show that RadOmni consistently achieves strong performance across classification, segmentation, pathological TNM staging, and radiology report generation tasks. Further analyses demonstrate favorable scaling behavior, robust cross-center generalization, and transferable representations.

The recently introduced 4-bit floating-point format, NVFP4, demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of redundancy in the existing NVFP4 encoding: (1) The FP4 format naturally exposes an unused quantization value due to its sign-magnitude representation that contains both positive and negative zeros. (2) The FP8 block scaling factor, with 4-bit exponent and 3-bit mantissa, contains an unused sign bit given that the scaling factor is always positive. Additionally, we find that LLM weights are more tolerant to a lower-precision block scaling factor, such as 6 bits with 3-bit exponent and 3-bit mantissa. Based on these observations, we propose **R**edund**a**nt **Ze**ro **R**emapping (RaZeR), an enhanced numerical format that pushes the limits of NVFP4 for more accurate LLM quantization under the same memory footprint. RaZeR leverages the unused bits in the block scaling factor to adaptively remap the negative FP4 zero to a set of pre-defined special values, which maximally utilizes the NVFP4 encoding and better fits the LLM tensor distribution. Extensive experiments validate RaZeR’s superior performance for 4-bit LLM quantization. For example, RaZeR reduces the average perplexity loss of NVFP4 by 29.4% and 32.3% under weight-only and weight-activation quantization, respectively.

Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve (RVR), a multi-round retrieval framework designed to maximize answer coverage. Initially, a retriever takes the original query and returns a candidate document set, followed by a verifier that identifies a high-quality subset. For subsequent rounds, the query is augmented with previously verified documents to uncover answers that are not yet covered in previous rounds. RVR is effective even with off-the-shelf retrievers, and fine-tuning retrievers for our inference procedure brings further gains. Our method outperforms baselines, including agentic search approaches, achieving at least 10\% relative and 3\% absolute gain in complete recall percentage on a multi-answer retrieval dataset (QAMPARI). We also see consistent gains on two out-of-domain datasets (QUEST and WebQuestionsSP) across different base retrievers. Our work presents a promising iterative approach for comprehensive answer recall leveraging a verifier and adapting retrievers to a new inference scenario.

Modern Voice Cloning (VCL) can synthesize speech that closely matches a target speaker from only seconds of reference audio, enabling applications such as personalized speech interfaces and dubbing. In practical deployments, modern audio generation models inevitably encounter noisy reference audios, imperfect text prompts, multilingual and long-form generation settings, downstream post-processing, and adversarial perturbations, all of which can significantly hurt robustness. Despite rapid progress in VCL driven by autoregressive codec-token language models and diffusion-based models, robustness under realistic deployment shifts remains underexplored. This paper introduces RVCBench, a comprehensive dataset and benchmark that evaluates Robustness in Voice Clone across the full generation pipeline. RVCBench contributes a large-scale, task-aligned robustness dataset that instantiates realistic deployment shifts through controlled text-audio pairing, multilingual and long-form scenarios, expressive prompts, post-processing conditions, and passive or proactive audio perturbations. Covering 18 robustness evaluations, 225 speakers, and 14,370 utterances, RVCBench enables unified evaluation of input sensitivity, generation stability, output resilience, and perturbation robustness. We evaluate 18 representative modern VCL models and reveal systematic vulnerabilities in content consistency, speaker similarity, long-form stability, post-processing resilience, detectability, and adversarial robustness.

Embedding models are central to modern information retrieval, retrieval-augmented generation, and downstream NLP systems, yet their evaluation remains unreliable: widely used benchmarks such as BEIR and MTEB rely on fully public datasets, making them vulnerable to overfitting that inflates scores without corresponding gains in real-world generalization. We introduce RTEB, a retrieval-focused embedding benchmark designed to address these limitations. Unlike MTEB's broad multi-task scope, RTEB is purpose-built for retrieval -- the setting most critical to RAG pipelines and agentic systems. RTEB emphasizes underrepresented yet high-impact domains, particularly code and agentic code retrieval, and keeps a large portion of its datasets private to structurally prevent direct test-set overfitting. We evaluate 20 models on 48 datasets (20 public, 28 private) spanning six domains and four languages. Rankings on RTEB diverge substantially from BEIR and MTEB ($\rho = 0.38$), agentic code retrieval emerges as the hardest domain with the best model reaching only 0.48 NDCG@10, and domain-specialized models fail to outperform strong generalists on any domain. Together, these design choices and findings position RTEB as a more reliable, discriminative, and future-proof benchmark for embedding model evaluation.

Large language models (LLMs) have demonstrated promising capabilities in tool calling. However, existing benchmarks predominantly evaluate models in an idealized setting where ground-truth tool definitions are provided in context. This assumption diverges significantly from real-world deployment scenarios, where the model must first identify user intent and retrieve relevant tools from a large, heterogeneous tool repository before invoking them. In this work, we introduce a Retrieval-augmented Tool-calling Benchmark (RTCBench). In RTCBench, we collect 1,840 tasks from existing benchmarks and augment them with two tool environments: 1) None: no available tools provided in the prompt and 2) Distractor: strong distractor tools, while the target tools are excluded. By removing oracle tool access, RTCBench compels LLMs to actively retrieve relevant tools from a large-scale repository via a unified search tool before making tool calls, thereby faithfully simulating the end-to-end complexity of real-world tool-use pipelines. Through comprehensive evaluation of diverse LLMs ranging from 4B-parameter models to proprietary frontier models, we identify two core deficiencies: unreliable tool discovery, where models fail to initiate effective retrieval actions, and post-retrieval grounding failures, where models cannot reliably select and invoke the correct tools from semantically similar retrieved candidates. Furthermore, to mitigate these issues, we release 13,278 retrieval-augmented tool-calling expert trajectories with high-quality chain-of-thought reasoning for LLM post-training. Experiments show that a 4B-parameter model post-trained with GRPO improves its tool-calling accuracy on RTCBench from approximately 47.9\% to over 85.8\%, approaching the performance of the state-of-the-art model Gemini-3.1-Pro.

V2X collaborative perception improves single-vehicle perception by aggregating multi-view features from collaborative agents. However, existing methods typically rely on simple spatial warping to align the collaborative features, which can cause semantic misalignment during fusion. Meanwhile, these deterministic warping-based methods are highly sensitive to the positional noises. In this paper, we propose a plug-in module called Robust Semantic and Spatial Aligner (RSSA), which performs semantic and probabilistic spatial transformations for robust feature alignment. Specifically, a Semantic Transformation (SemT) block modulates feature channels based on the observed relative pose to correct semantic inconsistencies. Subsequently, a Probabilistic Spatial Transformation (P-SpaT) block performs probabilistic warping by sampling from the Gaussian-modeled relative pose space. A Relative Transformation Augmentation (RT-Aug) strategy is further introduced to augment the relative poses and facilitate the training of the RSSA. Extensive experiments on both simulated and real-world datasets demonstrate that RSSA improves the state-of-the-art methods by a large margin under the configuration of both clean and noisy positions.

Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change captioning datasets typically describe overall image-level differences, leaving fine-grained localized semantic reasoning largely unexplored. To close this gap, we present RSRCC, a new benchmark for remote sensing change question-answering containing 126k questions, split into 87𝑘 training, 17.1𝑘 validation, and 22𝑘 test instances. Unlike prior datasets, RSRCC is built around localized, change-specific questions that require reasoning about a particular semantic change. To the best of our knowledge, this is the first remote sensing change question-answering benchmark designed explicitly for such fine-grained reasoning-based supervision. To construct RSRCC, we introduce a hierarchical semi-supervised curation pipeline that uses Best- of-𝑁 ranking as a critical final ambiguity-resolution stage. First, candidate change regions are extracted from semantic segmentation masks, then initially screened using an image-text embedding model, and finally validated through retrieval-augmented vision-language curation with Best-of-𝑁 ranking. This process enables scalable filtering of noisy and ambiguous candidates while preserving semantically meaningful changes. The dataset is available at https://huggingface.co/datasets/google/RSRCC.

Point cloud registration remains challenging in scenarios with low overlap, repetitive structures, and local geometric ambiguities, where candidate correspondences established based on local geometric similarity often lack global structural consistency and are prone to unreliable matches. To address this issue, this paper proposes RSPReg, a reliability-aware structural prototype learning framework for robust point cloud registration. RSPReg learns structural prototypes from rotation-invariant coarse-level superpoint features to characterize the structural attributes of local regions within the entire point cloud. These prototypes are further introduced as structural priors into cross-cloud correspondence reasoning, thereby improving the structural consistency of candidate correspondences. Furthermore, RSPReg estimates the uncertainty of structural prototypes and adaptively modulates the role of structural priors in the matching and pose estimation processes, reducing the negative impact of ambiguous and non-overlapping regions on registration results. Experiments on 3DMatch, 3DLoMatch, KITTI, and ETH demonstrate that RSPReg achieves the highest registration recall and exhibits clear advantages in low-overlap and cross-domain scenarios.

Vision-Language Models (VLMs) have achieved remarkable advances in general autonomous driving. Nevertheless, effectively exploiting the reasoning capability of Chain-of-Thought (CoT) to enhance the robustness and accuracy of decision and planning in long-tail scenarios still remains a challenging open problem. To this end, we propose a \textbf{R}easoning-\textbf{S}upervised \textbf{P}olicy \textbf{O}ptimization (RSPO) algorithm to boost reasoning and decision-making performance under long-tail driving scenarios. Concretely, we first conduct an empirical analysis of the core challenges faced by current VLMs in autonomous driving decision and planning tasks, and formally define reasoning and decision-making tasks tailored to long-tail scenarios. On this basis, we design a curriculum-guided supervised fine-tuning strategy to enable the model to rapidly fit the mean distribution of model outputs. Furthermore, we propose a reasoning-supervised optimization algorithm, which enhances the robustness and accuracy of reasoning and decision in long-tail scenarios by guiding the optimality consistency between reasoning processes and final decisions. Finally, we construct multiple verifiable autonomous driving reasoning and decision datasets based on open-source benchmarks and conduct extensive multi-dimensional comparative experiments. Experimental results show that the proposed algorithm achieves consistent improvements in prediction accuracy across multiple datasets compared with existing methods. In particular, it achieves a 6.7\% relative improvement over the strong baseline on the CODA-LM task and also yields lower trajectory-prediction errors on downstream planning tasks.

Vision Transformers (ViTs) achieve strong performance on image recognition and mobile vision applications, but their high-dimensional linear projections and attention computations still impose substantial storage and inference costs. Extremely low-bit quantization is a promising solution, yet ViTs often suffer severe accuracy degradation because conventional real-valued scalar codebooks are poorly matched to the directional geometry of Transformer projections. We present RPFQ-ViT, a Rotated Phase-Frame Quantization method that quantizes paired channels in two-dimensional phase planes, enabling low-bit codes to better preserve projection directions while recovering magnitude with lightweight scaling. RPFQ-ViT serves as a drop-in QAT replacement for \texttt{nn.Linear} and does not modify the standard real-valued attention, normalization, or activation computation graph. On ImageNet-1K, RPFQ-ViT-B/16 reaches 79.33\% Top-1 / 94.48\% Top-5 under W2/A4, Swin-T reaches 79.30\% Top-1 / 94.79\% Top-5 under W2/A8, and DeiT-S reaches 77.41\% Top-1 / 93.11\% Top-5 under W2/A8. Ablations, phase-geometry analysis, and direction-preservation metrics show that channel pairing, learnable rotation, phase-anchor learning, and residual phase refinement each improve quantization quality. We further deploy RPFQ-ViT image-classification models on native iOS and Android runtime stacks; with 2-bit packed weights, model size shrinks by roughly $5.4$--$7.1\times$ relative to FP32 and end-to-end on-device latency drops by $1.4$--$1.6\times$. All ImageNet results trained in our codebase use a matched 300-epoch recipe and are reported as mean accuracies over three independent runs. These results show that RPFQ-ViT provides a favorable trade-off among accuracy, compression, and practical mobile deployment for extremely low-bit ViTs.

Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline: first, a fixed population of teammates is generated, and second, an AHT agent is trained to collaborate with them. This separation limits coverage of behaviors and ignores whether the generated teammates are informative for the AHT agent to learn from. On the other hand, AHT agents are typically trained under the assumption that the training teammate set is uncontrollable, despite the fact that its composition strongly influences generalization. This paper presents a unified framework for AHT by reformulating the problem as an open-ended learning process between an AHT agent and an adversarial teammate generator. We introduce ROTATE, a regret-driven, open-ended training algorithm that alternates between improving the AHT agent and generating teammates that probe its collaboration deficiencies. Experiments across Overcooked and Level-Based Foraging tasks demonstrate that ROTATE significantly outperforms baselines on an unseen set of teammates, establishing a new standard for robust, generalizable teamwork.

Reinforcement learning is crucial for improving large language models’ reasoning and generalization. It relies on massive rollouts whose lengths become increasingly long-tailed as context windows grow. In on-policy training, these long-tail rollouts can result in GPU bubbles, reducing system utilization and limiting RL scalability. Asynchronous or partial-rollout methods improve throughput by relaxing synchronization, but inevitably introduce stale off-policy samples (trajectories) that may hurt final accuracy. Existing approaches mainly mitigate this off-policy issue by reweighting off-policy samples during training, yet they can still leave a performance gap compared to fully on-policy training. In this work, rather than passively reweighting samples during training, we propose RollVerify, a lightweight RL framework built on partial rollout that actively verifies and repairs samples before they enter training. Specifically, it introduces an off-policy shift metric OPS, to quantify the off-policy deviation of partially generated trajectories. Guided by the OPS constraint, RollVerify performs both sequence-level and token-level verification to identify and truncate invalid suffixes of trajectories. This yields high-quality samples that protect the models’ accuracy while preserving the efficiency gains of partial rollout. Experiments across model scales, architectures, and task domains show that RollVerify matches the performance of on-policy training while significantly improving training efficiency.