NeurIPS 2026 论文列表

Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but their generalization remains limited by the scarcity of annotated data for this task. To address this gap, we introduce Instruct-Particulate, a model that takes a 3D mesh together with a target kinematic specification, including part descriptions, connectivity, joint types, and optional point prompts, and predicts the corresponding kinematic part segmentation and joint motion parameters. The kinematic specification disambiguates the task and allows the model to target annotations of different granularity, thereby making it possible to use more abundant heterogeneous training data. At test time, the kinematic specification can be obtained automatically, so the model can be applied to any input mesh. To train our model at scale, we construct a heterogeneous dataset of more than 150,000 articulated 3D objects, extending existing publicly available collections with data obtained by partially labelling other 3D models, monolithic or already decomposed into parts, with kinematic labels by means of vision-language models. Experiments show that our model generalizes better across categories and to AI-generated meshes, enabling articulated asset reconstruction from real-world images via image-to-3D models.

Diffusion-based visuomotor policies perform well in robotic manipulation, yet current methods still inherit image-generation-style decoders and multi-step sampling. We revisit this design from a frequency-domain perspective. Robot action trajectories are highly smooth, with most energy concentrated in a few low-frequency discrete cosine transform modes. Under this structure, we show that the error of the optimal denoiser is bounded by the low-frequency subspace dimension and residual high-frequency energy, implying that denoising error saturates after very few reverse steps. This also suggests that action denoising requires a much simpler denoising model than image generation. Motivated by this insight, we propose Hydra-DP3 (${\bf HDP3}$), a pocket-scale 3D diffusion policy with a lightweight Diffusion Mixer decoder that supports two-step DDIM inference. Our synthetic experiments validate the theory and support the sufficiency of two-step denoising. Futhermore, across RoboTwin2.0, Adroit, MetaWorld, and real-world tasks, HDP3 achieves state-of-the-art performance with fewer than 1\% of the parameters of prior 3D diffusion-based policies and substantially lower inference latency.

Cluttered indoor environments, where large and heavy objects are scattered across diverse surfaces, require humanoid robots to sequentially navigate, grasp, transport, and accurately place each item at its target location within a single uninterrupted episode. This long-horizon, whole-body loco-manipulation task remains a significant challenge for current methods. Previous approaches often suffer from two main issues: easy-reward bias, where training overemphasizes early transport stages at the expense of later ones, and catastrophic forgetting, where focusing on later stages leads to a decline in earlier-stage performance. In this work, we introduce Humanoid Horizon, a unified policy framework designed to overcome these limitations through three interrelated mechanisms. The Parallel Training Strategy expands $N$ scenes into $S \times N$ concurrent stage streams governed by a shared policy, ensuring all transport stages receive continuous gradient updates and removing the bottleneck of sequential optimization. The Dynamic Starting Mechanism updates each environment's initial state with terminal states from upstream rollouts, gradually broadening transition coverage and enhancing robustness at stage boundaries. Reward Gating sets the reward to zero in later-stage streams if any previously placed object is displaced beyond a set threshold, thus maintaining object placement throughout the episode without extra reward terms. Collectively, these strategies achieve per-stage success rates exceeding 80\% on the LHM-Humanoid benchmark (350 training scenes, 66 held-out scenes), with performance remaining stable even as the number of sequentially transported objects increases beyond two—unlike the sharp drop seen in all baselines. Additionally, we demonstrate that the RL teacher policy can be distilled into a Vision-Language-Action student via DAgger. When provided only with egocentric RGB or depth observations and natural language instructions, the student successfully replicates the teacher's long-horizon, multi-object behaviors, highlighting the potential for perception-driven deployment on real humanoid robots.

High-dimensional robotic reinforcement learning (RL) is often bottlenecked by inefficient exploration in large, redundant actuator spaces. We introduce Devyn (Developing synergies), a lightweight framework that learns a structured low-dimensional action parameterization directly during policy optimization. Unlike methods that require precomputed synergies or structured exploration in the full action space, Devyn starts from a randomly initialized latent-to-action map and shapes it online through task interaction. The policy acts strictly in a reduced latent space, while a learnable synergy matrix decodes latent actions into actuator commands. To make this evolving decoder stable and useful for policy learning, Devyn combines slow decoder evolution with structural regularization, reducing latent-action semantic drift while encouraging compact coordination patterns. Across diverse continuous-control benchmarks, including high-dimensional humanoid and overactuated musculoskeletal systems, Devyn improves upon standard SAC and achieves competitive or better performance than recent methods designed for high-dimensional robotic control. Ablations show that the gains do not come from a generic latent-action bottleneck alone, but rather from the slow, structured evolution of the synergy matrix. Our results suggest that learning the action parameterization itself can be a simple and effective route to scaling RL for high-dimensional robots.

We study risk control for online policy learning, where an agent adaptively collects data and aims to learn a new policy where the risk is below a tolerance threshold at every round with a high probability. Such policy updates and data collection induce feedback covariate shift (FCS): the observed data depend on the history through the evolving policies and data-collection process. FCS brings two substantive problems for risk control: 1) efficient risk evaluation for each candidate policy and 2) valid policy selection with high probability risk control. To address these, we propose an efficient permutation-based weighted risk evaluator based on the Sinkhorn algorithm and select policies whose estimated risk upper confidence bounds (UCB) fall below the target threshold, with the UCB obtained through a proposed Gaussian bootstrap method. Theoretically, we prove that our risk evaluator's worst-case variance is no greater than that of the standard importance-weighted evaluator. Also, our policy control method yields an asymptotically valid simultaneous risk bound and provides asymptotic high-probability risk control for the selected policy. Empirically, our method maintains the target risk level, achieves lower risk than baseline methods, has lower variance in risk estimation, and enables reliable policy selection with high-probability risk control.

Forces and torques drive human motion, central to biomechanics, robotics, and physics-aware animation, yet they are often absent from learning-based motion pipelines for body reconstruction and generation. Some motion datasets provide force annotations, but the paired motion sequences are incompatible with the parametric body models used in modern learning pipelines. In addition, existing datasets typically obtain joint torques through inverse dynamics, which is inherently sensitive to the quality of the input kinematics; however, they do not quantify the reliability of these torque estimates. We close this gap with \textbf{ForceBody}, the first dataset to bring ground truth force supervision into a parametric body representation, shipped with per-sample torque uncertainty. ForceBody pairs the SKEL body model with ground reaction forces and inverse-dynamics joint torques across $10{,}389$ motion trials ($9.7$M frames, $27$ hours) from $140$ subjects. Each torque label is paired with a per-frame, per-joint uncertainty annotation obtained by Monte Carlo sampling through the inverse dynamics pipeline. We benchmark six architectures (MLP, Conv1D, LSTM, GRU, Mamba2, Transformer) on ground reaction force and joint torque prediction from SKEL motion. As one example use of the released uncertainty, finetuning a Transformer with per-sample uncertainty weighting reduces the MAE of joint torque estimation by $12\%$.

Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the pretrained distribution. Feynman--Kac sequential Monte Carlo (SMC) makes this correction exact in principle, but its prescribed weights routinely degenerate when the proposal dynamics are misaligned with the tilt, capping the practical gains from additional particles. We introduce *FluxLite*, a lightweight, training-free proposal-control framework for discrete diffusion. On the sparse directed graph of pretrained reverse rates, any sparse jump-rate perturbation can be exactly compensated by a $q_t$-weighted graph-divergence term in the Feynman--Kac potential; the target path is therefore preserved while the residual reweighting variance becomes a local convex objective. We instantiate this principle as two practical samplers: a one-hop local reallocation rule (HEU) and a small nonnegative quadratic program over pretrained-rate bases (D-VCG). We further prove population stability under the standard score-entropy training loss, identifying a tilted-path coverage factor that governs robustness to score error, together with finite-particle convergence for a fixed controlled Feynman--Kac recursion. Empirically, FluxLite improves over standard Feynman--Kac SMC baselines by up to two orders of magnitude in terminal KL on an analytically tractable finite-state CTMC benchmark, and reduces connected-correlation MSE on 2D Ising sampling by $5$--$7\times$ in geometric mean and up to $55\times$ at peak.

Optimal control of partially observable Markov decision processes (POMDPs), in general, requires a complete history. Traditionally, a belief state is used as a sufficient statistic to control POMDPs. However, being defined over a continuous space, its full representation is impossible with a finite-memory controller. This raises the question we address in this work: what must the available finite memory preserve to achieve near-optimal control performance? We study this question through an information-theoretic converse that gives a fundamental lower bound on the loss of any finite-memory controller. The analysis introduces the concept of a decision witness that characterizes belief states whose confusion would lead to unavoidable loss. Motivated by this bound, we propose RECAP, a constructive quantization method to design representative memory states. We prove upper bounds on the optimality gap of any RECAP codebook, and use the resulting components together with the converse to define the codebook objective. Experiments on 11 POMDP benchmarks demonstrate that RECAP improves over finite-memory representation baselines under the same memory budget.

Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the procedure should account for the inherent structure in the hypothesis space, when such structure exists. Here, we present a framework for controlling the false discovery rate for hypotheses with uncertain p-values from finite reference samples within arbitrarily structured hypothesis spaces, requiring only that the structure be represented through a suitable reproducing kernel. We present two decision rules, both proven to control the FDR under any misspecification of the structural information, and show that one strictly dominates the other under correct specification. Furthermore, we suggest a policy for efficient allocation of samples from the hypotheses' null distributions.

Asynchronous pipeline parallelism can accelerate distributed model training, but suffers from instability caused by inconsistent gradient computations. We identify a fundamental source of this inconsistency: error signals are coupled to forward-time features, not just parameter states. This means that parameters may evolve as long as they preserve forward-equivalence. Leveraging this insight, we introduce FRESCO, which frames asynchronous updates as a constrained optimization problem: it finds the minimal update modification that acts trivially on the activation subspace. Thus, it serves as a principled generalization beyond the usual synchronous--asynchronous dichotomy: subspace-level protection preserves gradient validity without synchronization stalls, unconstrained-asynchrony instability, or memory overhead that works against pipeline scaling. We demonstrate that FRESCO consistently outperforms representative asynchronous baselines in LLM pre-training, with the gains most pronounced under deep pipelines and high inconsistency levels. Moving beyond the current limitations, FRESCO establishes a new foundation for high-utilization pipeline parallel training, providing a scalable path for large-model development on diverse, resource-constrained systems.

The simplex method is one of the most widely used algorithms for linear programming, but its practical efficiency is highly sensitive to pivot selection and difficult to predict due to structural uncertainty in the search process. We propose EpiPivot, which casts pivot selection as a controllable decision process under epistemic uncertainty. EpiPivot identifies this uncertainty as two-dimensional: temporal dependence across the optimization trajectory and joint uncertainty across candidate pivot rules, and addresses both through temporal attention and an Epistemic Neural Network with shared latent sampling. At decision time, Thompson sampling combines these uncertainty-aware predictions into robust pivot choices. EpiPivot achieves significant improvements over classical and learning-based baselines, reducing pivot cost by up to 79\% over the best-performing classical rule on large-scale instances where most classical rules fail to converge within the time limit.

The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as their availability is crucial for many learning algorithms. This paper discusses the computation of solution sensitivities of general nonlinear programs (NLPs) using the implicit function theorem (IFT) and smoothed optimality conditions treated in interior-point methods (IPM). We detail sensitivity computation within a sequential quadratic programming (SQP) method which employs an IPM for the quadratic subproblems. Previous works presented in the machine learning community are limited to convex or unconstrained formulations, or lack an implementation for efficient sensitivity evaluation. The publication is accompanied by an efficient open-source implementation within the `acados` framework, providing both forward and adjoint sensitivities for general optimal control problems, achieving speedups exceeding 3x over the state-of-the-art solvers `mpc.pytorch` and `cvxpygen`.

Diffusion models achieve strong performance in text-to-image generation, but they can still produce unsafe content. Training-free safety guidance offers a practical inference-time alternative that intervenes during sampling without modifying model weights. However, many existing safeguards still rely on a coupled safety-control chain. In this chain, the same unsafe condition simultaneously specifies what should be avoided, indicates the current unsafe signal, constructs the intervention, and determines when that intervention remains active. This coupling can work when unsafe factors are explicit, but becomes less reliable once harmful and benign semantics are entangled in the current generation state, often leaving residual unsafe content or unnecessarily degrading benign-generation quality or prompt fidelity. We address this problem with Decoupled Safety Control (DSC), a plug-in safety-control algorithm for training-free safety guidance. DSC reformulates training-free safety guidance as an explicit state-dependent control process, so the executed intervention is determined from the current generation state rather than inherited directly from a single unsafe condition. Experiments on standard safety benchmarks show that DSC improves representative latent- and text-space safeguards, achieving stronger unsafe-content suppression while preserving benign-generation quality and maintaining efficient inference.

Cell-type deconvolution, the task of estimating the proportions of constituent cell types in a heterogeneous biological sample, is a core problem in computational biology. Methods that rely on epigenetic marks such as DNA methylation typically operate on aggregated methylation estimates, discarding the pattern-level information carried by individual DNA reads. Existing read-level approaches that exploit this information are scarce, and all remain restricted to few-class settings; scaling them further is an open problem because, at scale, non-discriminative reads dominate and hard labels conflict with the many-to-many mapping between methylation patterns and cell types, preventing classifier convergence. To overcome this, we propose data-driven soft labels that estimate the conditional cell-type distribution for each read, and integrate this scheme into *Syto*, a new modular framework for read-level classification-based deconvolution. On a whole-body atlas of 39 human cell types, *Syto* reduces MSE by $2.56\times$ over SoTA, with gains transferring to an out-of-distribution dataset spanning 16 tissues. *Syto* lays the foundation for modeling increasingly large cell-type panels, with improved applications in biology and healthcare. The proposed soft-labeling scheme is further translatable to any setting with a many-to-many signal-to-label mapping.

Modeling neurodegenerative pathology from observational cohorts is challenging because subjects are typically observed as unpaired endpoint snapshots rather than aligned longitudinal trajectories. Existing approaches to pathology transport are either endpoint-conditioned or population-level, yet neither extreme alone yields the best terminal-distribution fit. We introduce the Doob-Bridge Posterior Sampler (DBPS), a controlled-SDE posterior sampler with balanced endpoint-population control that blends individual endpoint guidance with population-level guidance. This ρ-weighted controller, obtained as the Cole–Hopf log-gradient of a geometric interpolation between endpoint and population desirability functions, is the exact optimal feedback policy of a linearly-solvable stochastic optimal control problem with closed-form terminal and running costs. Endpoint-conditioned bridge sampling and population Doob sampling are the ρ = 0 and ρ = 1 boundary cases; ρ ∈ (0, 1) realizes the genuine balanced regime. We evaluate DBPS on tau PET pathology transport in Alzheimer’s disease across ADNI and NACC cohorts (84 brain regions). Compared to representative baselines from all major unpaired-transport families, DBPS achieves state-of-the-art performance across multi-cohort settings (union training: SWD 0.760 vs. 0.821; held-out ADNI: 0.822 vs. 0.859), and remains competitive in single-cohort training (ADNI: 0.868 vs. 0.876). Beyond distribution matching, the learned controls recover canonical Braak-stage pathology organization without anatomical supervision, suggesting that balanced SOC objectives capture biologically meaningful structure in latent neurodegenerative progression.

Physical control systems, such as building HVAC systems, thermal processes, and power-grid assets, often involve delayed dynamics, safety constraints, and costly online interaction. Because the effect of a control action may appear after several time steps, effective control often requires reasoning over histories of states, actions, and outcomes. Pretrained sequence controllers, such as Decision Transformers, provide a natural way to model this history-dependent control problem by learning policies from offline trajectories, reducing the need for expensive online retraining. However, their performance can degrade when deployment conditions differ from the training data. In building control, for example, actuator wear, sensor drift, occupancy changes, and weather variation can all affect the observed response of the system to the same control action. Online adaptation is therefore needed, but raw prediction error is not always a reliable signal for updating the controller. The key challenge is to identify which part of the observed mismatch is action-sensitive and should guide policy adaptation. To address this challenge, we propose DARPAN, a Drift-Aware Residual Policy Adapter Network for pretrained physical control. DARPAN keeps the pretrained Decision Transformer frozen and performs online adaptation only through lightweight residual adapters, preserving the offline policy and limiting the number of trainable parameters. Instead of updating from raw prediction error, DARPAN uses a controllability filter trained in simulation to extract the action-sensitive part of the residual and filter out mismatch that is weakly related to the controller's actions. During deployment, a frozen predictor estimates the next state, the controllability filter produces a filtered residual, and a short history of filtered residuals forms a drift signature. This signature is used to select an existing adapter or create a new one when a persistent drift pattern appears. We benchmark DARPAN on SustainGym under actuator faults, sensor drift, delays, and external disturbances. DARPAN reduces the drift-induced performance loss by 31.7% and uses 63% fewer online parameters than Online DT. We release anonymized code and experiment configurations at https://huggingface.co/Darpan2026.

Network-level traffic signal control (TSC) involves coordination under partial observability and strict real-time constraints. In real-world urban networks with dozens or hundreds of intersections, direct coordination incurs an intractably large joint action space, making centralized cooperation difficult to deploy in practice. We propose CorridorLight, which tackles this challenge by casting cooperation as task negotiation. A high-level Corridor Agent represents the network as a direction-level graph and periodically generates a small set of junction-disjoint corridor tasks via a seed-and-grow procedure. These tasks provide lane-level preference guidance, while Intersection Agents maintain fast decentralized control and selectively support the active corridor task. By learning when to cooperate versus act selfishly, CorridorLight enables an adaptive trade-off between local delay minimization and corridor-level relief, leading to improved global performance. To this end, we derive a closed-form, Lipschitz-continuous gating function by maximizing a counterfactual net gain between cooperative and selfish behavior, yielding smooth, interpretable cooperation without brittle heuristics. Experiments on synthetic grids and real-world networks show consistent system-level improvements and outperform cooperative TSC baselines across diverse topologies and demand patterns.

A local specialist LLM, fine-tuned with reinforcement learning from verifiable rewards (RLVR) on operator-local data, is installed inside a single regulated organization under a per-deployment error budget $\alpha$. The operator needs a safety certificate that holds on *this* deployment's stream, simultaneously at every wall-clock round: no pooling across deployments, no waiting for a long-run average. Existing wrappers cannot deliver this on these adaptive, online-updated streams: offline conformal-risk methods require exchangeability; online-conformal methods bound only long-run averages; non-exchangeable extensions are marginally valid; and the closest anytime wrapper, A-RCPS, controls marginal rather than selective risk. Through a (test statistic, validity guarantee, deployment rule) framework we identify one empty cell forced by the deployment requirements (e-process per threshold, selective risk, anytime-pathwise validity, max-certified-threshold rule); **Conformal Selective Acting** (CSA) fills it as a per-round wrapper maintaining a Ville-type e-process per candidate score threshold on a Bonferroni grid, evaluated against the RLVR filtration. Under predictable updates and isotonic-calibrated monotone risk we prove (i) an anytime-pathwise selective-risk bound $R_T^{\mathrm{act}} \le \alpha + O(N_T^{-1/2})$, (ii) rate-optimal certification matching $\Theta(\bar\eta^{-2} \log(1/\delta))$, and (iii) a horizon-independent release-rate gap. Across eight specialist benchmarks (480 streams), sixteen adversarial distribution-shift cells (160 streams), and five live Expert-Iteration RLVR cells with online LoRA over four base models in three architecture families (10,300 rounds), CSA is the only method among ten directly compared that satisfies both pathwise validity and non-refusing deployment on every cell. We do not propose a new LLM, training algorithm, or policy class; CSA is the deployment-side complement, orthogonal to the model itself, for operators who cannot use a frontier API.

Causal discovery methods produce data-driven hypotheses about causal relationships, which may become new scientific findings or inform decisions about downstream interventions. In such settings, false positive causal claims can be more consequential than false negatives. Yet few methods target false positive error control in causal discovery, and existing error guarantees are often asymptotic and require strong versions of the faithfulness assumption. We introduce the $m$-conservative PC algorithm and prove that it controls false discovery rate in the linear Gaussian setting under an extremely weak version of the faithfulness assumption. In our framework, false positive error for causal graphs is defined based on the conditional-independence information they entail, yielding a metric that penalizes both false adjacencies and incorrect edge orientations. The parameter $m$ quantifies a trade-off between algorithmic complexity and the strength of the required faithfulness assumption. The $m$-conservative PC algorithm combines undirected graph learning, a Benjamini-Yekutieli-style procedure for conditional independence testing, and a conservative orientation step that marks unresolved or contradictory edge directions as ambiguous. Our algorithm produces valid outputs and retains error guarantees even in the finite-sample setting, despite possible random errors in the conditional independence tests.