NeurIPS 2026 论文列表

Human motion change is currently hindered by the lack of large-scale datasets, comprehensive benchmarks, and robust algorithms capable of handling diverse motion requirements. To address these gaps, we propose AnyMotion, a novel open-world paradigm for highly controllable and identity-consistent human motion editing across arbitrary scenes and actions. Our contributions are two-fold. First, we establish a hierarchical label taxonomy covering six major categories and 527 fine-grained classes, and curate HME-1M, a large-scale dataset containing 1.04 million editing quadruplets via systematic data balancing, cleaning, and reverse construction. Second, we introduce the Skeleton Chain-of-Thought (SCoT) framework, a two-stage pipeline consisting of motion reasoning and guided generation. In the reasoning stage, a multimodal large language model serves as a cognitive planner to derive target skeletal keypoints and MetaQueries. These outputs subsequently function as explicit structural and semantic priors, providing the necessary guidance for the diffusion transformer to perform high-fidelity motion generation. Finally, built upon our label taxonomy, we present Human Motion-Bench, the first motion-centric benchmark equipped with multi-category fine-grained evaluation metrics. Extensive experiments demonstrate that AnyMotion achieves state-of-the-art performance across diverse editing scenarios.

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}.

We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with foundation approaches have shown that scaling training data markedly improves hand pose estimation, existing real-world datasets are limited in coverage, and prior synthetic datasets rarely provide occlusions, arm details, and aligned depth together at scale. To address this bottleneck, our proposed AnyHand contains 2.5M single-hand and 4.1M hand-object interaction RGB-D images, with rich geometric annotations. We show that extending the original training data recipes of existing RGB baselines with AnyHand yields significant gains on multiple benchmarks (FreiHAND and HO-3D), even when keeping the architectures and training schemes fixed. Together with extensive ablations on the scale and composition of the training data setups, these results suggest that training data diversity and quality are as critical as scale for advancing hand pose estimation. We further examine the utility of AnyHand's aligned depth maps in the appendix, showing that scaling RGB-D supervision with AnyHand allows a lightweight depth-fusion variant of existing RGB baselines to outperform prior RGB-D methods.

Depth-of-field control is a fundamental tool in photography, yet post-capture bokeh editing from a single image remains challenging. A practical editor should handle images captured under arbitrary focus and aperture settings. Existing methods typically assume an all-in-focus input, or first recover an all-in-focus image before rendering new bokeh. Such pipelines can discard useful blur cues from the source image and propagate reconstruction artifacts into the final edit. We introduce **AnyBokeh**, a physics-guided framework for **any-to-any bokeh editing**. Instead of treating source blur merely as a degradation to be removed, AnyBokeh estimates the **source blur state** with a signed circle-of-confusion map and a disparity map. By modeling the linear relation between signed circle of confusion and disparity difference, AnyBokeh estimates a source-specific **optical fingerprint** and transfers the source optical characteristics to the desired focus and aperture setting. A generative editor conditioned on both source and target circle-of-confusion maps then performs relative blur synthesis, enabling spatially adaptive deblurring, preservation, and defocus rendering. To support physically supervised learning, we further construct a high-fidelity synthetic dataset with accurate depth, focus distance, and full EXIF metadata. Experiments on real-world benchmarks show that AnyBokeh achieves faithful and controllable editing across any-to-any bokeh editing, all-in-focus-to-bokeh rendering, and defocus deblurring, while avoiding all-in-focus reconstruction and test-time bokeh-level calibration commonly required by existing approaches. The code and dataset will be made publicly available.

Generating antibody sequences is challenging because they combine conserved framework regions with hypervariable loops. Latent diffusion is attractive for this task since it enables flexible conditioning and bidirectional generation. But standard approaches fail. Global noise schedules treat all positions equally, so models learn the predictable frameworks well while the diverse loops remain poorly captured. We address this by learning a latent space that redistributes information evenly, allowing standard diffusion to succeed where it previously failed. On organism-conditioned generation across six species, our approach achieves 10× lower Fréchet Distance than latent diffusion without redistribution. It supports chain-type control, loop infilling, and paired-chain generation. Validation across five protein encoders confirms the method is encoder-agnostic. These results establish latent diffusion as a practical tool for antibody sequence design.

On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedback), offers a promising direction for advancing reasoning capability without a stronger external teacher. Yet in math reasoning the gains are inconsistent, even when the same approach succeeds elsewhere. A pointwise mutual information analysis traces the failure to the privileged context itself: it inflates the teacher's confidence on tokens already implied by the solution (structural connectives, verifiable claims) and deflates it on deliberation tokens (*Wait*, *Let*, *Maybe*) that drive multi-step search. We propose Anti-Self-Distillation (AntiSD), which ascends a divergence between student and teacher rather than descending it: this reverses the per-token sign and yields a naturally bounded advantage in one step. An entropy-triggered gate disables the term once the teacher entropy collapses, completing a drop-in replacement for default self-distillation. Across five models from 4B to 30B parameters on math reasoning benchmarks, AntiSD reaches the GRPO baseline's accuracy in 2 to 10× fewer training steps and improves final accuracy by up to 11.5 points. AntiSD opens a path to scalable self-improvement, where a language model bootstraps its own reasoning through its training signal.

LLM-based systems are increasingly deployed in domains where incorrect outputs carry real costs, often necessitating expensive human review and verification. We argue that a central issue is not simply error, but _consequence-insensitivity_: models fail to adjust their behavior according to the cost of being wrong. We study this failure across both explicit utility framings as well as natural-language descriptions of stakes that mirror real-world deployment scenarios. Our evaluation spans agentic coding and mathematical reasoning, covering five frontier model families. Across settings, models systematically under-abstain: they continue to answer or submit patches even when incorrect answers carry significant consequences. The pathology is striking: models continue to submit answers in trivial settings where abstention is strictly dominant, and even when told an incorrect answer will cause nuclear extinction. Further, we find that this behavior is orthogonal to existing benchmarks; increasing model size or capability within a family does not improve sensitivity. Comparing base and instruction-tuned models localizes much of this anti-abstention bias to post-training: base models abstain far more often, though they are not themselves reliably consequence-aware. Finally, we evaluate prior post-hoc interventions alongside in-context learning and fine-tuning, finding that none robustly resolves the failure. Our results suggest that current post-training and evaluation pipelines optimize models to answer, not to act under stakes -- a significant bottleneck for trustworthy autonomy.

Online forecasters sometimes know a regime boundary before any post-boundary labels arrive. Standard adaptive conformal methods still react through later coverage errors, so they can pay a detection-delay cost even when the boundary is public. We study conformal prediction with an announced partition of the stream and separate two post-break audits. The reset empirical quantile in segmented split-CP attains the minimax per-time probability-gap rate $\Theta(M^{-1/2})$ after $M$ post-reset scores, with a matching Le Cam lower bound. A pure bounded constant-step announced-break ACI recursion attains an $O(M^{-1})$ time-averaged empirical-frequency gap by telescoping, but this is a realized-frequency statement, not a guarantee for the next interval. The practical AB-ACI implementation used in experiments adds burn-in and projection, so its rate claim includes explicit correction terms and is not unconditional. Synthetic experiments recover both slopes and show short-window gains when the break is large. Two real-data applications are null under leakage-free protocols. ERCOT RTC+B gives 0.863 post-reform coverage for AB-ACI versus 0.880 for ACI, and FOMC yield breaks give 0.892 versus 0.900. The message is diagnostic: use the reset empirical quantile for per-prediction reliability, use AB-ACI only for long-run frequency audits, and expect gains only when the realized score shift is large enough to make detection delay costly.

While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it is still unclear VLMs are able to recover temporal dynamics when motion is present. To this end, we introduce the animation-to-code task, as well as a benchmark, \texttt{Animation2Code}, for evaluating temporal visual reasoning via reconstructing executable web animation code from videos. \texttt{Animation2Code} consists of 1,069 web animation videos with diverse visuals and motion patterns, augmented with corresponding HTML/CSS/JavaScript implementations. We further pair the dataset with several novel human-aligned metrics, which allow us to disentangle visual similarity from temporal alignment when comparing rendered animations to ground-truth samples. We benchmark current SOTA model performance on these new samples, and show that current models struggle with temporal consistency, even when achieving high appearance similarity, even in fine-tuning and iterative refinement scenarios. Our benchmark dashboard is available at: \url{https://anim2code-dashboard.vercel.app}.

We propose a new framework for low-precision neural networks based on angular geometry and randomized low-bit estimators. Rather than approximating Euclidean linear operations under limited precision, we reinterpret neural computation in terms of directional similarity and construct quantized random-feature estimators of cosine interactions. Our approach introduces \textit{Angular Layers}, which replace standard linear transformations with low-bit projections that estimate angular similarity in the forward pass, while gradients are computed with respect to the underlying continuous cosine geometry. This decoupling enables stable optimization without straight-through estimators or heuristic surrogate gradients, even under aggressive quantization of both weights and activations. On the theory side, we introduce \emph{G-Gradient} as a gradient proxy for angular layers and show that, in a one-layer planted model, a sufficiently small \emph{G}-gradient can guarantee exact recovery of a target binary solution. We further prove uniform approximation and convergence guarantees that provide a rigorous path from tractable continuous optimization to exact recovery in the original discrete model. Empirically, we instantiate these ideas in \textit{Angular Nets}, including low-precision variants of ResNet, Vision Transformers, and BERT-style encoders, and obtain strong performance across vision and language tasks at substantially reduced precision. Overall, our results suggest that preserving angular structure provides a principled foundation for low-bit deep learning. Our full implementation is available at: \url{https://github.com/GGM2026/GGM}.

Distilling "System 2" reasoning into compact student models is bottlenecked by the scarcity of fully-correct teacher traces: as task difficulty grows, rejection sampling discards an increasingly large fraction of teacher generations. "Wrong" (W) traces are abundant, but training on them indiscriminately causes *policy poisoning* the student internalises the teacher's hallucinated arithmetic alongside any useful reasoning structure. We propose **Atomic Reasoning Units (ARU)**, a framework that resolves this trade-off via strict syntactic constraints. ARU enforces a Backus--Naur-Form (BNF) grammar that decomposes each reasoning step into a Premise, an Operation, and a Result, algorithmically disentangling reasoning planning from arithmetic execution. *Counterfactual Logic Verification* (CLV) symbolically re-executes W-traces to recover the $68\%$ that have valid plans but faulty arithmetic, and *Syntactic Loss Masking* (SLM) trains the student on this verified structure while suppressing the gradient on hallucinated result tokens. Across the $3 \times 8 \times 6$ grid we evaluate ($\{0.6, 1.7, 8\}$\,B Qwen3 students $\times$ eight benchmarks $\times$ six baselines), ARU is the strongest method in every cell, improving GSM8K by $+11.1$ and MATH-500 by $+9.8$ over the closest baseline at $1.7$\,B; on the held-out AIME competition set ARU's Maj@$8$ is $14.8$ versus $9.5$ for PoT (the closest baseline). We further document a small-scale capability gap in which ARU at $0.5$\,B matches Gold-SFT at $\geq 1.5$\,B on GSM8K (paired-bootstrap $p<0.05$ after Bonferroni correction), with a pre-registered ProofWriter control showing the gap collapses on pure-logic reasoning---consistent with arithmetic offloading as the dominant mechanism, though we do not claim the control uniquely isolates it. The code is available at https://anonymous.4open.science/r/ARU-286D.

Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos, but performance often degrades under unseen environments due to severe domain shifts. Continual learning offers a practical paradigm for updating rPPG models over sequential domains. However, it is challenged by two coupled issues: First, adapting to new domains may overwrite previously acquired physiological representations, resulting in the degradation of physiological consistency across domains. Second, drastic environmental variations induce heterogeneous facial video distributions, making the adaptation process unstable and prone to suboptimal convergence. To address these challenges, we propose ApexPhys, a continual rPPG framework that simultaneously anchors physiological invariance and expands domain plasticity. Specifically, we introduce a Backbone Consistency Representation Preservation (BCRP) mechanism, which leverages the Fisher Information Matrix to identify and preserve parameters critical to physiological consistency. To enhance adaptation flexibility under diverse environmental shifts, we further propose a Hierarchical Expansion Strategy (HES) that autonomously perceives layer-wise distribution discrepancies and dynamically expands hierarchical adaptation branches. Additionally, we develop a Prior-Guided Pathfinding (PGP) strategy, which leverages parameter inheritance to guide the adaptation along a stable trajectory. Experiments on five public datasets demonstrate that ApexPhys achieves strong anti-forgetting performance and robust adaptation under continual learning.

While Large Language Models (LLMs) have significantly advanced radiology report generation (RRG) with strong generative priors, standard autoregressive decoders still suffer from sequential error accumulation when they must infer all clinical structures directly from images. Diffusion Language Models (DLMs) have recently emerged as a non-autoregressive paradigm with iterative masking and denoising mechanisms. Instead of replacing the mature language decoder with a DLM, we reveal that the key value of DLMs for RRG lies in controllable clinical planning, where the model predicts globally consistent anchor points before free-text realization. To harness this complementary strength, we propose ANCHOR-GEN, the first framework that uses a DLM as an explicit diffusion language planner for RRG. Rather than forcing the autoregressive decoder to infer every critical structure from visual tokens alone, ANCHOR-GEN predicts a structured clinical canvas that serves as clinically grounded anchor points. An autoregressive LLM then leverages these anchors to complete report generation with both fluency and clinical faithfulness. By systematically combining non-autoregressive planning and autoregressive decoding, our unified pipeline is trained with structured denoising, hierarchical coarse-to-fine supervision, and planner-decoder binding objectives. Extensive experiments on the public \textsc{MIMIC-CXR} benchmark demonstrate that ANCHOR-GEN improves clinical faithfulness over prior works. Comprehensive analyses and ablations further show that diffusion planning and causal decoding offer complementary strengths for RRG, and that integrating them in one pipeline provides an effective route toward more faithful medical text generation.

Inspired by recent work on discrete diffusion for anchored unmasking, we introduce a new training-free framework for protein engineering that can generate higher-order variants that improve one or more phenotypes while exhibiting favorable epistatic interactions. Prior approaches require mutation sites to be specified in advance and can drive sequences away from the natural protein manifold. We address these limitations with **A**nchored **P**rotein **E**ngineering via quantized e**X**pectation (**APEX**), a decoding-time method consisting of anchor selection, tilted decoding, and iterative refinement. APEX selects anchor residues using a reward-gradient score with an explicit correction for pairwise epistasis, decodes substitutions for anchors by tilting the base model's per-site predictive distributions along reward gradients, and revisits low-scoring anchors iteratively for optional test-time scaling. All phases query the base model only through masked-conditional logits, a primitive shared by masked language models, discrete-diffusion models, and inverse-folding networks; as a result, APEX works out of the box across commonly-used model families. Across various protein-engineering benchmarks, including thermostability and solubility, APEX achieves state-of-the-art results and avoids the predictor-gaming artifacts that reward-search-only baselines tend to produce.

Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scenarios. To bridge this gap, we present AnchorWorld, a framework that advances egocentric simulation through enhanced interaction integrity and a flexible mechanism for world customization. First, we utilize 3D human motion as the primary interaction modality. To complement the out-of-view or truncated body parts in egocentric views, we introduce an auxiliary training supervision that incorporates exogenous viewpoints decoupled from the agent’s first-person sensorium. It allows the model to observe the agent's full-body positioning relative to the environment, facilitating a more robust spatial grounding of human-world interactions. Furthermore, we propose a simple yet effective mechanism for customizing self-evolving worlds. This is achieved by defining anchor views within a unified world coordinate system, coupled with textual descriptions dictating the dynamic evolution of local scenes. Experimental results show that AnchorWorld significantly outperforms state-of-the-art baselines, while ablation studies validate the effectiveness of our key designs. Notably, our customization scheme exhibits promising spatio-temporal geometric consistency and adheres strictly to the prescribed evolutionary dynamics.

Adversarial attacks optimized on a single open-weight LLM can transfer to and jailbreak architecturally different models, allowing an attacker with white-box access to one model to compromise independently deployed systems. This creates a shared vulnerability across models, yet existing defenses are not designed for this cross-model threat. We find that transfer aligns with shared internal representation geometry, making it a natural defense target. We find that cross-model transfer aligns with shared internal representation geometry, making it a natural defense target. \textbf{AnchorRep} targets this geometry directly with a lightweight LoRA adapter that pushes the defended model's internal representations of harmful prompts away from those of a frozen anchor model on the same prompts. Training uses a small set of harmful prompts and no adversarial examples. Across five models and four architectural families, AnchorRep reduces cross-model attack success rate to $\leq$1.1\% on 2{,}000 transferred attacks (0\% on two), including the largest drop on Mistral ($36\% \to 1.1\%$). Existing defenses can reduce transfer, but only at high cost—either inducing up to 77\% degenerate benign output or increasing over-refusal by up to 18\%. Because such degenerate benign outputs are not captured by standard refusal-based metrics, we introduce the \textbf{Benign Garble Rate} to quantify them. Our results suggest that cross-model robustness can be achieved by shaping representation geometry, without requiring attack-specific training. \smallskip \noindent\faGithub~Code, configs and logs: \href{https://anonymous.4open.science/r/AnchorRep/README.md}{anonymous.4open.science/r/AnchorRep}

Recent feed-forward 3D Gaussian Splatting (3DGS) methods have enabled effective multi-view 3D reconstruction by predicting Gaussian primitives in a single forward pass. However, existing approaches predominantly adopt a pixel-aligned paradigm that predicts one Gaussian per input pixel, coupling the Gaussian count to the input resolution and number of views. This also leads to redundant primitives in textureless regions and insufficient coverage in geometrically complex areas. We present Anchor3DGS, a pose-free feed-forward 3DGS framework that replaces pixel-aligned prediction with a compact anchor-based representation. Guided by information entropy, our method places a set of 3D anchors in the scene, concentrating them in regions of higher visual complexity while maintaining broad spatial coverage. Each anchor performs occlusion-aware aggregation of multi-view image features, exchanges information with other anchors, and is decoded into a set of local Gaussians. A lightweight feature enhancement module further refines the coarse RGB output from Gaussian splatting and improves rendering fidelity. Extensive experiments on diverse benchmark datasets demonstrate that our Anchor3DGS achieves state-of-the-art novel view synthesis quality while only using an order of magnitude fewer Gaussian primitives.

Principal component analysis (PCA) is one of the most widely used unsupervised dimension reduction techniques. We study PCA for data from multiple related domains. Since principal components generally differ across domains, one way to obtain a shared low-rank embedding is to perform PCA on the pooled data. However, this approach can focus on spurious directions that exhibit high variation in only a few domains. To find a robust embedding that still explains most variance in unseen but similar domains, we propose instead to focus on shared directions of variation. To this end, we introduce Anchor PCA which trades off overall explained variance with agreement between the shared and domain-specific low-rank embeddings. Anchor PCA amounts to PCA on a modified target matrix and thus can be solved efficiently. Moreover, we show that Anchor PCA recovers a maximal invariant subspace and admits a minimax reconstruction interpretation under bounded domain-specific covariance inflations. On simulated and real-world gas sensor data with temporal drift, we demonstrate, respectively, that Anchor PCA recovers the maximally invariant subspace and yields embeddings that explain more variance on unseen domains than the pooling baseline and a worst-case alternative. Taken together, these findings establish Anchor PCA as a promising approach to robust unsupervised dimension reduction from multi-domain data.

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.