arXiv subjects

Bin Yang

Publications and source records attributed to Bin Yang.

At least 55 records · Page 3Linked to original sources

arXiv2026

The Non-Principal-Axis Rotation and Convex Shape Model of Earth Quasi-Satellite and the Target of China's Tianwen-2 Mission (469219) Kamo`oalewa

(469219) Kamo`oalewa is the most stable Earth quasi-satellite and the target of China's Tianwen-2 asteroid sample return mission. Due to its small size, fast rotation, and the limited observing geometry accessible from the ground, many physical properties of Kamo`oalewa remain poorly constrained, including the rotational state and shape. We obtained three epochs of high-cadence, high signal-to-noise photometric lightcurves of Kamo`oalewa with the Gemini North Telescope from 2026 April to May, supplemented by one lightcurve from the Lowell Discovery Telescope in 2026 May. Our analysis suggests that Kamo`oalewa is in a non-principal-axis rotation with an elongated shape. Four possible solutions exist, including a long-axis mode (LAM) solution and a short-axis mode (SAM) solution, as well as their corresponding mirrored angular momentum directions. The most preferable solution has a LAM model with a precession period $P_ϕ=27.65\pm0.03~\text{min}$ and a rotational period $P_ψ=50.49\pm0.08~\text{min}$, and the angular momentum points to ecliptic coordinates $(λ, β) = (226^\mathrm{o}\pm20^\mathrm{o}, -39^\mathrm{o}\pm20\mathrm{o})$, although we cannot rule out other solutions or other close-by periods due to aliasing. We also derived a convex shape inversion for the LAM models with consistent rotational parameters but could not find a satisfactory inversion for the SAM models. The corresponding angular momentum points to $(λ, β)=(225^\mathrm{o}, -43^\mathrm{o})$, and the periods are $P_ϕ=27.90~\text{min}$ and $P_ψ=49.66~\text{min}$. The non-principal-axis rotation provides additional constraints on the dynamic history or the internal structure of Kamo`oalewa.

astro-ph.EP↗

arXiv2026

DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version

Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches have achieved promising performance, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts their cross-scenario generalization capabilities. To address these limitations, we present DGCPath, an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This framework establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diverse trajectory views from Gaussian noise; (2) a variational contrastive mechanism that enforces latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate that DGCPath outperforms state-of-the-art baselines on two distinct downstream tasks, validating its enhanced generalization capability and representation effectiveness.

cs.AI↗

arXiv2026

REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version

Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets demonstrate that REFINE consistently outperforms state-of-the-art methods across multiple downstream tasks while remaining computationally efficient and scalable. This paper is an extended version of REFINE: Trajectory Representation Learning via Closed-Loop Transcription, to appear in KDD 2026.

cs.LG↗

arXiv2026

TDLight: A Framework for Incremental Light-Curve Management and Continuous Classification Optimization

Time-series observations of celestial objects are fundamental to studying variable and transient phenomena. As time-domain surveys continue to scale up, timely automated classification is essential for rapid follow-up. Many existing approaches treat storage and analysis as separate stages, archiving data first and analyzing it later. Yet each source is a continuously growing stream, and early observations often contain sufficient information to assign scientifically useful labels before the full light curve is available. Motivated by this, we present TDLight, a framework for light-curve management and trigger-based classification over incremental data streams. Built on the time-series database TDengine, the system loads the Gaia DR2 archive (approximately 4.8 x 10^7 rows) in 33 s and supports low-latency retrieval. As new observations accumulate, TDLight automatically triggers a lightweight LightGBM-based classification pipeline that requires no GPU. When prediction confidence exceeds a predefined threshold, these early results are written back to the database, providing reliable classifications before offline analysis is complete. The deployed system also supports manual label confirmation and optional incremental training. Evaluated on the LEAVES dataset (approximately 1.10 million sources spanning 10 classes), the system achieves an overall accuracy of 93.6% and a weighted F1 score of 93.7%. Furthermore, it identifies 560 high-confidence catalog-disagreement candidates for follow-up verification. Notably, this includes correcting 150 RRc variables erroneously labeled as EW binaries due to inherited period-doubling artifacts. TDLight and the related data products are publicly available.

astro-ph.IM↗

arXiv2026

Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents

Reinforcement learning is a natural way to post-train LLM agents for long-horizon interactive tasks judged only by end-of-task verification, yet a shared belief holds that outcome-only RL soon hits a ceiling on small open models. Recent work therefore compensates around the training with denser rewards, SFT priors, skill libraries, curated memory, or multi-agent orchestration. We argue the ceiling is an artifact of two failures of common practice. Signal starvation: group-relative RL with sparse outcome-only rewards yields a gradient only when a task's rollout group mixes successes and failures, so under-scaled exploration silences exactly the hardest, most instructive tasks. Policy drift: squeezing many updates out of a small task pool degrades the policy itself, as an unanchored objective lets the sampling distribution collapse exactly when saturation has already made informative groups rare. We present CANOPY (Coverage-ANchored On-PolicY RL), a minimalist protocol attacking both directly: scale same-task exploration until the natural signal reappears, keep every update on-policy, KL-anchored, and confined to the agent's own action tokens, then cash in an enlarged interaction budget at test time. On AppWorld, a long-horizon interactive coding benchmark, a Qwen3-14B policy trained with CANOPY through environment interaction alone--without task-specific supervision, auxiliary credit signals, or elaborate agent scaffolding--topped the public leaderboard (Feb. 2026; Test-Normal TGC 86.9, Test-Challenge 67.6), and the same design principles lift Qwen3.5-9B on SWE-bench Verified by 16.6 points. Agentic RL alone thus internalizes long-horizon capability directly into a small open model; we plan to release the complete training stack at https://github.com/AlibabaResearch/SignalCoverageRL.

cs.LG↗

arXiv2026

ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologies

LLM-powered Multi-Agent Systems (MAS) have emerged as an effective approach towards collaborative intelligence, and have attracted wide research interests. Among them, ``self-evolving'' MAS, treated as a more flexible and powerful technical route, can construct task-adaptive workflows or communication topologies, instead of relying on a predefined static structue template. Current self-evolving MAS mainly focus on Spatial Evolving or Temporal Evolving paradigm, which only considers the single dimension of evolution and does not fully incentivize LLMs' collaborative capability. In this work, we start from a novel Spatio-Temporal perspective by proposing ST-EVO, which supports dialogue-wise communication scheduling with a compact yet powerful flow-matching based Scheduler. To make precise Spatio-Temporal scheduling, ST-EVO can also perceive the uncertainty of MAS, and possesses self-feedback ability to learn from accumulated experience. Extensive experiments on nine benchmarks demonstrate the state-of-the-art performance of ST-EVO, achieving about 5%--25% accuracy improvement.

cs.MA↗

arXiv2026

TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking

Query denoising has become a standard training strategy for DETR-based detectors. Denoising queries, initialized by perturbing ground truths, share similarities with track queries in a typical DETR-based Multi-Object Tracking (MOT) method, warranting exploration of their potential synergy. However, query denoising in existing MOT methods is performed only within a single frame, preventing trackers from learning inter-frame temporal association from the denoising process. To address this issue, we propose TQD-Track, a Temporal Query Denoising (TQD) method tailored for MOT. In our method, denoising queries are initialized from ground truths in the previous frame and then propagated into the current frame in the same way as track queries, serving as additional independent data association candidates. These denoising queries carry temporal information and instance-specific feature representations, effectively emulating and augmenting track queries. Moreover, to simulate various real-world MOT challenges for robust tracking, we introduce several corresponding noise types to generate diverse denoising queries. We analyze the impact of our temporal query denoising for two tracking paradigms, tracking-by-attention and alternating detection and association, demonstrating its generalization. Extensive experiments on the nuScenes and Argoverse~2 datasets demonstrate that our approach consistently enhances different MOT baselines, requiring only modifications in the training process. Code and models are available at https://github.com/yutongy98/TQD-Track.

cs.CV↗

arXiv2026

Super-Resolution of Range-Doppler Maps: A Case Study with Chirp-Sequence Radar and Transformer

Range-Doppler (rD) maps produced by chirp- sequence (CS) radar systems are fundamentally limited in reso- lution by bandwidth, carrier frequency, and coherent processing interval constraints. Improving resolution through hardware is often impractical due to regulatory, cost, and real-time operation requirements. In this work, we investigate deep learning-based super- resolution of rD maps in both range and Doppler using a real- world dataset collected with an Infineon millimeter-wave CS radar. We propose a memory-efficient transformer architecture based on dual-path shifted window (DPSWIN) attention, which combines axial/dual-path attention with 1D shifted window (SWIN) attention for scalable processing of high-dimensional radar data. Our model is benchmarked against existing 2D SWIN attention-based super-resolution models, which we adapt to the rD-map super-resolution task. We prioritize reconstruction fidelity over perceptual quality and employ root mean squared error (RMSE)-based training objectives. We avoid adversarial or perceptual losses that may introduce visually plausible but physically incorrect structures. In addition, we incorporate CFAR-based target-detection losses to optimize downstream target detectability in the super-resolved rD maps. We further study the impact of signal processing and training design choices on the super-resolution, including magnitude compression, spatial upsampling strategies, and loss formulations. Experimental results demonstrate that the proposed framework achieves computationally efficient rD map super-resolution on previously unseen real-world environments.

eess.SP↗

arXiv2026

Centimeter-wave OH observations of comets 12P/Pons-Brooks and C/2023 A3 (Tsuchinshan-ATLAS) with FAST

We present centimeter-wave spectroscopic observations of the OH 18-cm lines in two bright comets, 12P/Pons-Brooks and C/2023 A3 (Tsuchinshan-ATLAS), conducted with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during their 2024 apparitions. For the Halley-type comet 12P/Pons-Brooks, five epochs of OH observations were obtained. The main OH lines at 1665 and 1667 MHz were robustly detected in absorption during one pre-perihelion epoch, while upper limits were derived for two post-perihelion epochs. For the dynamically new Oort Cloud comet C/2023 A3 (Tsuchinshan-ATLAS), six epochs of OH observations were obtained. The 1665 and 1667 MHz lines were robustly detected in absorption during two epochs immediately following the comet's closest approach to Earth. We also reported a tentative detection of the 1721 MHz satellite line in one of these two epochs. The low OH detection rates for these two bright comets are primarily attributable to their unfavorable heliocentric radial velocity during the observations, which resulted in the anti-maser negative inversion mode of the OH excitation. We calculated the OH production rates for both comets and found that, after considering the small beam size of FAST and the collisional quenching effect, production rates are consistent with the data from literature. Specifically, this matches the power-law fit of the OH production rates for comet 12P/Pons-Brooks, as well as the production rates at comparable heliocentric distances among comet C/2023 A3 (Tsuchinshan-ATLAS) and four other dynamically new Oort Cloud comets.

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arXiv2026

Beyond Viewpoint Generalization: What Multi-View Demonstrations Offer and How to Synthesize Them for Robot Manipulation?

Does multi-view demonstration truly improve robot manipulation, or merely enhance cross-view robustness? We present a systematic study quantifying the performance gains, scaling behavior, and underlying mechanisms of multi-view data for robot manipulation. Controlled experiments show that, under both fixed and randomized backgrounds, multi-view demonstrations consistently improve single-view policy success and generalization. Performance varies non-monotonically with view coverage, revealing effective regimes rather than a simple "more is better" trend. Notably, multi-view data breaks the scaling limitation of single-view datasets and continues to raise performance ceilings after saturation. Mechanistic analysis shows that multi-view learning promotes manipulation-relevant visual representations, better aligns the action head with the learned feature distribution, and reduces overfitting. Motivated by the importance of multi-view data and its scarcity in large-scale robotic datasets, as well as the difficulty of collecting additional viewpoints in real world settings, we propose RoboNVS, a geometry-aware self-supervised framework that synthesizes novel-view videos from monocular inputs. The generated data consistently improves downstream policies in both simulation and real-world environments.

cs.RO↗

arXiv2026

DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and may introduce irrelevant context. Moreover, commonly used parameter-efficient adaptation methods, such as LoRA, mainly introduce fixed low-rank transformations in the feature space and do not explicitly maintain a dialogue-level state or condition their transformations on the evolving conversational context. To address these limitations, we propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level memory for accurate ERC. Based on LoRA, DiaRelay introduces two extra tightly collaborative components, Selective Relay Memory Transition and Dual-axis Relay Memory Read. Selective Relay Memory Transition progressively aggregates useful historical evidence into a bounded relay memory and propagates it across successive utterance predictions. This allows earlier emotional cues to influence later predictions after they leave the local context window, without re-encoding the complete dialogue history or expanding the backbone context length. Dual-axis Relay Memory Read uses the propagated memory to dynamically modulate low-rank feature transformations, enabling context-dependent representation adaptation without test-time gradient updates. Extensive experiments show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters, indicating the effectiveness and generalizability of our DiaRelay in enhancing LLM-based emotional understanding.

cs.CL↗

arXiv2026

GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure

3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers well to per-point tasks such as semantic and panoptic segmentation, but transfer to 3D detection remains weaker. We analyze recent SSL methods and find that most objectives are defined only on measured LiDAR returns from visible surfaces, leaving occluded and unobserved regions unconstrained. This visible-surface bias can be sufficient for point-wise prediction, but 3D detection requires robustness to missing structure. To address this gap, we propose GhostPoint, an SSL framework that hallucinates latent features in local neighborhoods around discovered instances, generated via a novel instance voxel dilation. In GhostPoint, an encoder processes observed returns, and an additional predictor infers neighborhood representations from observed context. In addition to standard encoder-level supervision, we introduce a predictor-level supervision scheme on sampled voxels from generated neighborhoods. Specifically, observed (visible/masked) voxels match teacher-encoder targets, while unobserved voxels match teacher-predictor hallucinations. This design encourages the learned representation to explicitly model structure beyond observed returns. Extensive evaluations on nuScenes and Waymo demonstrate that our method achieves state-of-the-art performance, consistently improving downstream 3D detection, especially under sparse scans and limited labels.

cs.CV↗

arXiv2026

TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit diverse temporal dynamics, often accompanied by various irregularities such as missing values and non-uniform sampling frequencies. Such irregularities lead to complex and asynchronous temporal dependencies across channels. Thus, a single model with a fixed patching scheme often fails to adapt well to diverse multivariate time series, hindering accurate forecasting. In this paper, we propose TiWeaver, a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively. Specifically, we introduce a Graph-Guided Adaptive Tokenizer (G$^2$AT) that divides time series into high contextually coherent patches by jointly considering temporal density and representation consistency. In addition, we propose a Fine-grained Asynchronous Dependency Extractor (FADE), which is designed to model fine-grained asynchronous inter-channel dependencies while incorporating long-term historical dependencies. We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance, outperforming existing methods up to 25%. These results demonstrate its robustness and effectiveness across diverse domains and data characteristics.

cs.LG↗

arXiv2026

Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising

Effective time series forecasting enables various real-world applications, benefiting from the proliferation of mobile devices. However, the volume of time series data may vary significantly across domains due to high data acquisition costs and data regulations. To maximally create value from sparse data, this study focuses on a new problem of source-free time series forecasting, aiming to adapt a pretrained model from sufficient source time series to the sparse target time series without access to the source data, enabling data protection. To achieve this, we propose TimeID, a novel source-free time series forecasting framework with a large language model (LLM) centric proxy denoising inspired by the powerful generalization capabilities of LLMs. Specifically, TimeID consists of three key components: (1) dual-branch invariant disentangled feature learning that enforces representation- and gradient-wise invariance by means of season-trend decomposition; (2) lightweight, parameter-free proxy denoising that dynamically calibrates systematic biases of LLMs; and (3) knowledge distillation that bidirectionally aligns the denoised prediction and the original target prediction. Extensive experiments on real-world datasets demonstrate that TimeID outperforms state-of-the-art baselines, improving MSE and MAE by 10.7% and 9.3% on average. The code is available at https://github.com/decisionintelligence/TimeID.

cs.LG↗

arXiv2026

MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables. Existing ISTS forecasting methods often solely utilize historical observations to predict future ones while falling short in learning contextual semantics and fine-grained temporal patterns. To address these problems, we propose MM-ISTS, a multimodal ISTS forecasting framework augmented by vision-text large language models, which bridges temporal, visual, and textual modalities. MM-ISTS encompasses a two-stage encoding mechanism. In particular, a Cross-Modal Vision-Text Encoding module is proposed to automatically generate informative visual images and textual data, enabling the capture of intricate temporal patterns and comprehensive contextual understanding, in collaboration with multimodal LLMs (MLLMs). In parallel, ISTS encoding extracts complementary yet enriched temporal features from historical ISTS observations, including multi-view embedding fusion and a Temporal-Variable Encoder. Further, we propose an Adaptive Query-Based Feature Extractor to compress MLLM token embeddings while preserving useful knowledge, which in turn reduces computational costs. In addition, a Multimodal Alignment module with Modality-Aware Gating is designed to alleviate the modality gaps. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions.

cs.CV↗

arXiv2026

Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenges stem from asynchronous observations, non-uniform sampling intervals, and the fact that temporal patterns themselves carry critical dynamic information. Existing approaches either rely on discretization-based preprocessing (e.g., interpolation, imputation, or aggregation), which disrupts the underlying continuous-time semantics, or adopt continuous-time modeling via ODE-based frameworks, which typically require specialized architectures and incur substantial computational overhead due to numerical solvers. To address these limitations, we propose WrapFlow, a continuous-time modeling framework for irregular time series forecasting. On the input side, WrapFlow introduces Continuous-Time Tokenization, which directly encodes raw observation events and explicitly models long unobserved intervals via gap-aware tokens. The resulting continuous-time tokens are then processed by a standard Transformer backbone to capture long-range temporal dependencies. On the output side, we develop a simulation-free training paradigm for Residual Flow Matching, which learns conditional residual vector fields around base predictions while avoiding numerical-solver simulation and backpropagation during training. This design enables high-quality continuous forecasting using only a small number of fixed rollout steps at inference. Extensive experiments on multiple real-world datasets demonstrate that WrapFlow achieves state-of-the-art performance.

cs.LG↗

arXiv2026

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, existing studies rely primarily on prompt engineering or supervised fine-tuning, while systematic research on reinforcement learning (RL) post-training and automated evaluation of feedback quality remains limited. We propose RLAES, a unified LLM framework that jointly optimizes essay scoring and feedback generation through RL. To make feedback quality measurable, interpretable, and usable for training, we introduce Rubric-based Feedback Evaluation (RFE), an essay-grounded feedback evaluation framework comprising 166 fine-grained binary rubric items and an LLM-as-judge. Building on RFE, we propose Adaptive Gated Feedback Optimization (AGFO), which activates rubric-based feedback rewards on demand during RL, reducing evaluation overhead while improving feedback quality. We also propose Adjacent Contrastive Reasoning (ACR) to improve ordinal score calibration by explicitly contrasting adjacent score levels. Experimental results show that the RFE framework captures essay-feedback consistency, exhibits strong pairwise discriminative power, and closely aligns with expert preferences. On the ASAP benchmark, RLAES-AGFO achieves the best scoring performance among LLM-based methods (QWK = 0.803), while maintaining feedback quality comparable to GPT-5.5 and avoiding the feedback degradation observed under score-only RL. Code and datasets are publicly available at https://github.com/hellomuyi/RLAES.

cs.CL↗

arXiv2026

Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.

cs.CV↗