arXiv subjects

Xinran Li

Publications and source records attributed to Xinran Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Re$^2$MoGen: Open-Vocabulary Motion Generation via LLM Reasoning and Physics-Aware Refinement

Text-to-motion (T2M) generation aims to control the behavior of a target character via textual descriptions. Leveraging text-motion paired datasets, existing T2M models have achieved impressive performance in generating high-quality motions within the distribution of their training data. However, their performance deteriorates notably when the motion descriptions differ significantly from the training texts. To address this issue, we propose Re$^2$MoGen, a Reasoning and Refinement open-vocabulary Motion Generation framework that leverages enhanced Large Language Model (LLM) reasoning to generate an initial motion planning and then refine its physical plausibility via reinforcement learning (RL) post-training. Specifically, Re$^2$MoGen consists of three stages: We first employ Monte Carlo tree search to enhance the LLM's reasoning ability in generating reasonable keyframes of the motion based on text prompts, specifying only the root and several key joints' positions to ease the reasoning process. Then, we apply a human pose model as a prior to optimize the full-body poses based on the planned keyframes and use the resulting incomplete motion to supervise fine-tuning a pre-trained motion generator via a dynamic temporal matching objective, enabling spatiotemporal completion. Finally, we use post-training with physics-aware reward to refine motion quality to eliminate physical implausibility in LLM-planned motions. Extensive experiments demonstrate that our framework can generate semantically consistent and physically plausible motions and achieve state-of-the-art performance in open-vocabulary motion generation.

cs.CV↗

arXiv2026

Development Status of the KIPM Detector Consortium

A Kinetic Inductance Phonon-Mediated Detector is a calorimeter that uses kinetic inductance detectors to read out phonon signals from the device substrate. We have established a consortium comprising university and national lab groups dedicated to advancing the state of the art in these detectors, with the ultimate goal of designing a detector sub-eV threshold on energy deposited in the substrate, enabling searches for both light dark matter and low-energy neutrino interactions. This consortium brings together experts in kinetic inductance detector design, phonon and quasiparticle dynamics, and noise modeling, along with specialized fabrication facilities, test platforms, and unique calibration capabilities. Recently, our consortium has demonstrated a resolution on energy absorbed by the sensor of 2.1 eV, the current record for such devices. The current focus of the consortium is modeling and improving the phonon collection efficiency and implementing low-$\boldsymbol{T_c}$ superconductors, both of which serve to improve the overall energy resolution and threshold of the detectors.

physics.ins-det↗

arXiv2026

Enhanced inference for distributions and quantiles of individual treatment effects in various experiments

Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, despite they are more challenging to infer due to nonidentifiability from observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect distributions and quantiles in both completely randomized and stratified randomized experiments, but can be overly conservative by assuming the worst-case scenario where units with large effects are all assigned to the treated (or control) group. We introduce two improved methods to enhance the power of these existing approaches. The first method reinterprets existing approaches as inferring treatment effects among only treated or control units, and then combines the inference for treated and control units to infer treatment effects for all units. The second method explicitly controls for the actual number of treated units with large effects. Both simulation and applications demonstrate the substantial gain from the improved methods. These methods are further extended to sampling-based experiments as well as quasi-experiments from matching, in which the ideas for both improved methods play critical and complementary roles.

stat.ME↗

arXiv2026

Dark Matter Detection Using Phonon Sensing in Amorphous Materials

We present a concept for a tabletop-scale detector with an amorphous target designed to search for dark matter absorption into phonon excitations. In crystalline materials, absorption occurs only at narrow resonances where the dark matter mass matches a zero momentum optical phonon mode, whereas amorphous targets provide a broadband response that can substantially enhance the absorption rate away from these resonances. The predicted backgrounds arise from the relaxation of disorder-induced metastable defects in the amorphous target, as well as from low-energy noise intrinsic to superconducting phonon sensors. A prototype detector with a target mass of only a few $μ$g could provide broadband sensitivity to dark photon absorption across the 50 meV-200 meV mass range, probing up to two orders of magnitude beyond existing constraints.

hep-ph↗

arXiv2026

Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations

Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly, primarily due to the intricate object-background boundaries. Addressing the core question, "Can COS be effectively achieved in a zero-shot manner without manual annotations for any camouflaged object?" we affirmatively respond and introduce a robust zero-shot COS framework. This framework leverages the inherent local pattern bias of COS and employs a broad semantic feature space derived from salient object segmentation (SOS) for efficient zero-shot transfer. We incorporate an Masked Image Modeling (MIM) based image encoder optimized for Parameter-Efficient Fine-Tuning (PEFT), a Multimodal Large Language Model (M-LLM), and a Multi-scale Fine-grained Alignment (MFA) mechanism. The MIM pre-trained image encoder focuses on capturing essential low-level features, while the M-LLM generates caption embeddings processed alongside these visual cues. These embeddings are precisely aligned using MFA, enabling our framework to accurately interpret and navigate complex semantic contexts. To optimize operational efficiency, we introduce a learnable codebook that represents the M-LLM during inference, significantly reducing computational overhead. Our framework demonstrates its versatility and efficacy through rigorous experimentation, achieving state-of-the-art performance in zero-shot COS with $F_β^w$ scores of 72.9\% on CAMO and 71.7\% on COD10K. By removing the M-LLM during inference, we achieve an inference speed comparable to that of traditional end-to-end models, reaching 18.1 FPS. Code: https://github.com/AVC2-UESTC/ZSCOS-CaMF

cs.CV↗

arXiv2026

MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning

Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. To overcome these limitations, we propose MAGE, a Multi-scale Autoregressive GEneration-based offline RL method. MAGE incorporates a condition-guided multi-scale autoencoder to learn hierarchical trajectory representations, along with a multi-scale transformer that autoregressively generates trajectory representations from coarse to fine temporal scales. MAGE effectively captures temporal dependencies of trajectories at multiple resolutions. Additionally, a condition-guided decoder is employed to exert precise control over short-term behaviors. Extensive experiments on five offline RL benchmarks against fifteen baseline algorithms show that MAGE successfully integrates multi-scale trajectory modeling with conditional guidance, generating coherent and controllable trajectories in long-horizon sparse-reward settings.

cs.LG↗

arXiv2026

Identifiability of Treatment Effects with Unobserved Spatially Varying Confounders

The study of causal effects in the presence of unmeasured spatially varying confounders has garnered increasing attention. However, a general framework for identifiability, which is critical for reliable causal inference from observational data, has yet to be advanced. In this paper, we study a linear model with various parametric model assumptions on the covariance structure between the unmeasured confounder and the exposure of interest. We establish identifiability of the treatment effect for many commonly 20 used spatial models for both discrete and continuous data, under mild conditions on the structure of observation locations and the exposure-confounder association. We also emphasize models or scenarios where identifiability may not hold, under which statistical inference should be conducted with caution.

stat.ME↗

arXiv2026

Design-based theory for causal inference from adaptive experiments

Adaptive designs dynamically update treatment probabilities using information accumulated during the experiment. Existing theory for causal inference from adaptive experiments primarily assumes the superpopulation framework with independent and identically distributed units, and may not apply when the distribution of units evolves over time. This paper makes two contributions. First, we extend the literature to the finite-population framework, which allows for possibly nonexchangeable units, and establish the design-based theory for causal inference under general adaptive designs using inverse-propensity-weighted (IPW) and augmented IPW (AIPW) estimators. Our theory accommodates nonexchangeable units, both nonconverging and vanishing treatment probabilities, and nonconverging outcome estimators, thereby justifying inference using AIPW estimators with black-box outcome models that integrate advances from machine learning methods. To alleviate the conservativeness inherent in variance estimation under finite-population inference, we also introduce a covariance estimator for the AIPW estimator that becomes sharp when the residuals from the adaptive regression of potential outcomes on covariates are additive across units. Our framework encompasses widely used adaptive designs, such as multi-armed bandits, covariate-adaptive randomization, and sequential rerandomization, advancing the design-based theory for causal inference in these specific settings. Second, as a methodological contribution, we propose an adaptive covariate adjustment approach for analyzing even nonadaptive designs. The martingale structure induced by adaptive adjustment enables valid inference with black-box outcome estimators that would otherwise require strong assumptions under standard nonadaptive analysis.

stat.ME↗

arXiv2026

Puzzle it Out: Local-to-Global World Model for Offline Multi-Agent Reinforcement Learning

Offline multi-agent reinforcement learning (MARL) aims to solve cooperative decision-making problems in multi-agent systems using pre-collected datasets. Existing offline MARL methods primarily constrain training within the dataset distribution, resulting in overly conservative policies that struggle to generalize beyond the support of the data. While model-based approaches offer a promising solution by expanding the original dataset with synthetic data generated from a learned world model, the high dimensionality, non-stationarity, and complexity of multi-agent systems make it challenging to accurately estimate the transitions and reward functions in offline MARL. Given the difficulty of directly modeling joint dynamics, we propose a local-to-global (LOGO) world model, a novel framework that leverages local predictions-which are easier to estimate-to infer global state dynamics, thus improving prediction accuracy while implicitly capturing agent-wise dependencies. Using the trained world model, we generate synthetic data to augment the original dataset, expanding the effective state-action space. To ensure reliable policy learning, we further introduce an uncertainty-aware sampling mechanism that adaptively weights synthetic data by prediction uncertainty, reducing approximation error propagation to policies. In contrast to conventional ensemble-based methods, our approach requires only an additional encoder for uncertainty estimation, significantly reducing computational overhead while maintaining accuracy. Extensive experiments across 8 scenarios against 8 baselines demonstrate that our method surpasses state-of-the-art baselines on standard offline MARL benchmarks, establishing a new model-based baseline for generalizable offline multi-agent learning.

cs.AI↗

arXiv2026

A Consistency-Improved LiDAR-Inertial Bundle Adjustment

Simultaneous Localization and Mapping (SLAM) using 3D LiDAR has emerged as a cornerstone for autonomous navigation in robotics. While feature-based SLAM systems have achieved impressive results by leveraging edge and planar structures, they often suffer from the inconsistent estimator associated with feature parameterization and estimated covariance. In this work, we present a consistency-improved LiDAR-inertial bundle adjustment (BA) with tailored parameterization and estimator. First, we propose a stereographic-projection representation parameterizing the planar and edge features, and conduct a comprehensive observability analysis to support its integrability with consistent estimator. Second, we implement a LiDAR-inertial BA with Maximum a Posteriori (MAP) formulation and First-Estimate Jacobians (FEJ) to preserve the accurate estimated covariance and observability properties of the system. Last, we apply our proposed BA method to a LiDAR-inertial odometry.

cs.RO↗

arXiv2026

GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL

Offline Safe Reinforcement Learning (OSRL) aims to learn a policy to achieve high performance in sequential decision-making while satisfying constraints, using only pre-collected datasets. Recent works, inspired by the strong capabilities of Generative Models (GMs), reformulate decision-making in OSRL as a conditional generative process, where GMs generate desirable actions conditioned on predefined reward and cost values. However, GM-assisted methods face two major challenges in OSRL: (1) lacking the ability to "stitch" optimal transitions from suboptimal trajectories within the dataset, and (2) struggling to balance reward targets with cost targets, particularly when they are conflict. To address these issues, we propose Goal-Assisted Stitching (GAS), a novel algorithm designed to enhance stitching capabilities while effectively balancing reward maximization and constraint satisfaction. To enhance the stitching ability, GAS first augments and relabels the dataset at the transition level, enabling the construction of high-quality trajectories from suboptimal ones. GAS also introduces novel goal functions, which estimate the optimal achievable reward and cost goals from the dataset. These goal functions, trained using expectile regression on the relabeled and augmented dataset, allow GAS to accommodate a broader range of reward-cost return pairs and achieve a better tradeoff between reward maximization and constraint satisfaction compared to human-specified values. The estimated goals then guide policy training, ensuring robust performance under constrained settings. Furthermore, to improve training stability and efficiency, we reshape the dataset to achieve a more uniform reward-cost return distribution. Empirical results validate the effectiveness of GAS, demonstrating superior performance in balancing reward maximization and constraint satisfaction compared to existing methods.

cs.LG↗

arXiv2025

Extensible Multi-Granularity Fusion Network and Transferable Curriculum Learning for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) aims to determine sentiment polarity toward specific aspects in text. Existing methods enrich semantic and syntactic representations through external knowledge or GNNs, but the growing diversity of linguistic features increases model complexity and lacks a unified, extensible framework. We propose an Extensible Multi-Granularity Fusion Network (EMGF) that integrates dependency syntax, constituent syntax, attention-based semantics, and external knowledge graphs. EMGF employs multi-anchor triplet learning and orthogonal projection to effectively fuse multi-granularity features and strengthen their interactions without additional computational overhead. Furthermore, we introduce the first task-specific curriculum learning framework for text-only ABSA, which assigns difficulty scores using five indicators and trains the model from easy to hard to mimic human learning and improve generalization. Experiments on SemEval 2014, Twitter, and MAMS datasets show that EMGF+CL consistently outperforms state-of-the-art ABSA models.

cs.AI↗

arXiv2025

Learning Across Experiments and Time: Tackling Heterogeneity in A/B Testing

A/B testing plays a central role in data-driven product development, guiding launch decisions for new features and designs. However, treatment effect estimates are often noisy due to short horizons, early stopping, and slowly accumulating long-tail metrics, making early conclusions unreliable. A natural remedy is to pool information across related experiments, but naive pooling potentially fails: within experiments, treatment effects may evolve over time, so mixing early and late outcomes without accounting for nonstationarity induces bias; across experiments, heterogeneity in product, user population, or season dilutes the signal with unrelated noise. These issues highlight the need for pooling strategies that adapt to both temporal evolution and cross-experiment variability. To address these challenges, we propose a local empirical Bayes framework that adapts to both temporal and cross-experiment heterogeneity. Throughout an experiment's timeline, our method builds a tailored comparison set: time-aware within the experiment to respect nonstationarity, and context-aware across experiments to draw only from comparable counterparts. The estimator then borrows strength selectively from this set, producing stabilized treatment effect estimates that remain sensitive to both time dynamics and experimental context. Through theoretical analysis and empirical evaluation, we show that the proposed local pooling strategy consistently outperforms global pooling by reducing variance while avoiding bias. Our proposed framework enhances the reliability of A/B testing under practical constraints, thereby enabling more timely and informed decision-making.

stat.ME↗

arXiv2025

Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and Curriculum Learning

Emotion Recognition in Conversation (ERC) is a crucial task for understanding human emotions and enabling natural human-computer interaction. Although Large Language Models (LLMs) have recently shown great potential in this field, their ability to capture the intrinsic connections between explicit and implicit emotions remains limited. We propose a novel ERC training framework, PRC-Emo, which integrates Prompt engineering, demonstration Retrieval, and Curriculum learning, with the goal of exploring whether LLMs can effectively perceive emotions in conversational contexts. Specifically, we design emotion-sensitive prompt templates based on both explicit and implicit emotional cues to better guide the model in understanding the speaker's psychological states. We construct the first dedicated demonstration retrieval repository for ERC, which includes training samples from widely used datasets, as well as high-quality dialogue examples generated by LLMs and manually verified. Moreover, we introduce a curriculum learning strategy into the LoRA fine-tuning process, incorporating weighted emotional shifts between same-speaker and different-speaker utterances to assign difficulty levels to dialogue samples, which are then organized in an easy-to-hard training sequence. Experimental results on two benchmark datasets -- IEMOCAP and MELD -- show that our method achieves new state-of-the-art (SOTA) performance, demonstrating the effectiveness and generalizability of our approach in improving LLM-based emotional understanding.

cs.AI↗

arXiv2025

Cluster-robust inference with a single treated cluster using the t-test

This paper considers inference when there is a single treated cluster and a fixed number of control clusters, a setting that is common in empirical work, especially in difference-in-differences designs. We use the t-statistic and develop suitable critical values to conduct valid inference under weak assumptions allowing for unknown dependence within clusters. In particular, our inference procedure does not involve variance estimation. It only requires specifying the relative heterogeneity between the variances from the treated cluster and some, but not necessarily all, control clusters. Our proposed test works for any significance level when there are at least two control clusters. When the variance of the treated cluster is bounded by those of all control clusters up to some prespecified scaling factor, the critical values for our t-statistic can be easily computed without any optimization for many conventional significance levels and numbers of clusters. In other cases, one-dimensional numerical optimization is needed and is often computationally efficient. We have also tabulated common critical values in the paper so researchers can use our test readily. We illustrate our method in simulations and empirical applications.

econ.EM↗

arXiv2025

Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?

Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods. When applied to these methods, CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels.

eess.IV↗

arXiv2025

Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective

World models have recently attracted growing interest in Multi-Agent Reinforcement Learning (MARL) due to their ability to improve sample efficiency for policy learning. However, accurately modeling environments in MARL is challenging due to the exponentially large joint action space and highly uncertain dynamics inherent in multi-agent systems. To address this, we reduce modeling complexity by shifting from jointly modeling the entire state-action transition dynamics to focusing on the state space alone at each timestep through sequential agent modeling. Specifically, our approach enables the model to progressively resolve uncertainty while capturing the structured dependencies among agents, providing a more accurate representation of how agents influence the state. Interestingly, this sequential revelation of agents' actions in a multi-agent system aligns with the reverse process in diffusion models--a class of powerful generative models known for their expressiveness and training stability compared to autoregressive or latent variable models. Leveraging this insight, we develop a flexible and robust world model for MARL using diffusion models. Our method, Diffusion-Inspired Multi-Agent world model (DIMA), achieves state-of-the-art performance across multiple multi-agent control benchmarks, significantly outperforming prior world models in terms of final return and sample efficiency, including MAMuJoCo and Bi-DexHands. DIMA establishes a new paradigm for constructing multi-agent world models, advancing the frontier of MARL research. Codes are open-sourced at https://github.com/breez3young/DIMA.

cs.MA↗

arXiv2025

Modeling the Differential Rate for Signal Interactions in Coincidence with Noise Fluctuations or Large Rate Backgrounds

The characteristic energy of a relic dark matter interaction with a detector scales strongly with the putative dark matter mass. Consequently, experimental search sensitivity at the lightest masses will always come from interactions whose size is similar to noise fluctuations and low energy backgrounds in the detector. In this paper, we correctly calculate the net change in measured differential rate due to signal interactions that overlap in time with noise and backgrounds, accounting for both periods of time when the signal is coincident with noise/backgrounds and for the decreased amount of time in which only noise/backgrounds occur. Previous experimental searches have not accounted for this second fundamental effect, and thus either vastly overestimate their experimental search sensitivity (very bad) or use ad hoc conservative cuts which can underestimate experimental sensitivity (not ideal). We find that the detector response to dark matter can be trivially and conservatively understood as long as the true probability of dark matter pileup is small. We also show that introducing random events in the continuous raw data stream (a form of ``salting") provides a correct and practical implementation that correctly accounts for the decreased live time available for noise fluctuations and background events out of coincidence with a true dark matter signal.

hep-ex↗