arXiv subjects

Song Wang

Publications and source records attributed to Song Wang.

At least 109 records · Page 6Linked to original sources

arXiv2026

AdaSFormer: Adaptive Serialized Transformers for Monocular Semantic Scene Completion from Indoor Environments

Indoor monocular semantic scene completion (MSSC) is notably more challenging than its outdoor counterpart due to complex spatial layouts and severe occlusions. While transformers are well suited for modeling global dependencies, their high memory cost and difficulty in reconstructing fine-grained details have limited their use in indoor MSSC. To address these limitations, we introduce AdaSFormer, a serialized transformer framework tailored for indoor MSSC. Our model features three key designs: (1) an Adaptive Serialized Transformer with learnable shifts that dynamically adjust receptive fields; (2) a Center-Relative Positional Encoding that captures spatial information richness; and (3) a Convolution-Modulated Layer Normalization that bridges heterogeneous representations between convolutional and transformer features. Extensive experiments on NYUv2 and Occ-ScanNet demonstrate that AdaSFormer achieves state-of-the-art performance. The code is publicly available at: https://github.com/alanWXZ/AdaSFormer.

cs.CV↗

arXiv2026

Engineering Pitfalls in AI Coding Tools: An Empirical Study of Bugs in Claude Code, Codex, and Gemini CLI

The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and Gemini CLIs. While promising significant productivity gains, the engineering process of building these tools, which sit at the complex intersection of traditional software engineering, AI system design, and human-computer interaction, is fraught with unique and poorly understood challenges. This paper presents the first empirical study of engineering pitfalls in building such tools, on a systematic, manual analysis of over 3.8K publicly reported bugs in the open-source repositories of three AI-assisted coding tools (i.e., Claude-Code, Codex, and Gemini CLIs) on GitHub. Specifically, we employ an open-coding methodology to manually examine the issue description, associated user discussions, and developer responses. Through this process, we categorize each bug along multiple dimensions, including bug type, bug location, root cause, and observed symptoms. This fine-grained annotation enables us to characterize common failure patterns and identify recurring engineering challenges. Our results show that more than 67% of the bugs in these tools are related to functionality. In terms of root causes, 36.9% of the bugs stem from API, integration, or configuration errors. Consequently, the most commonly observed symptoms reported by users are API errors (18.3%), terminal problems (14%), and command failures (12.7%). These bugs predominantly affect the tool invocation (37.2%) and command execution (24.7%) stages of the system workflow. Collectively, our findings provide a critical roadmap for developers seeking to design the next generation of reliable and robust AI coding assistants.

cs.SE↗

arXiv2026

Communication to Completion: Modeling Collaborative Workflows with Intelligent Multi-Agent Communication

Multi-agent LLM systems have demonstrated impressive capabilities in complex collaborative tasks, yet most frameworks treat communication as instantaneous and free, overlooking a fundamental constraint in real world teamwork, collaboration cost. We propose a scalable framework implemented via Communication to Completion (C2C), which explicitly models communication as a constrained resource with realistic temporal costs. We introduce the Alignment Factor (AF), a dynamic metric inspired by Shared Mental Models, to quantify the link between task understanding and work efficiency. Through experiments on 15 software engineering workflows spanning three complexity tiers and team sizes from 5 to 17 agents, we demonstrate that cost-aware strategies achieve over 40% higher efficiency compared to unconstrained interaction. Our analysis reveals emergent coordination patterns: agents naturally adopt manager centric hub-and-spoke topologies, strategically escalate from asynchronous to synchronous channels based on complexity, and prioritize high value help requests. These patterns remain consistent across multiple frontier models (GPT-5.2, Claude Sonnet 4.5, Gemini 2.5 Pro). This study moves beyond simple agent construction, offering a theoretical foundation for quantifying and optimizing the dynamics of collaboration in future digital workplaces.

cs.MA↗

arXiv2026

Thousand-GPU Large-Scale Training and Optimization Recipe for AI-Native Cloud Embodied Intelligence Infrastructure

Embodied intelligence is a key step towards Artificial General Intelligence (AGI), yet its development faces multiple challenges including data, frameworks, infrastructure, and evaluation systems. To address these issues, we have, for the first time in the industry, launched a cloud-based, thousand-GPU distributed training platform for embodied intelligence, built upon the widely adopted LeRobot framework, and have systematically overcome bottlenecks across the entire pipeline. At the data layer, we have restructured the data pipeline to optimize the flow of embodied training data. In terms of training, for the GR00T-N1.5 model, utilizing thousand-GPU clusters and data at the scale of hundreds of millions, the single-round training time has been reduced from 15 hours to just 22 minutes, achieving a 40-fold speedup. At the model layer, by combining variable-length FlashAttention and Data Packing, we have moved from sample redundancy to sequence integration, resulting in a 188% speed increase; π-0.5 attention optimization has accelerated training by 165%; and FP8 quantization has delivered a 140% speedup. On the infrastructure side, relying on high-performance storage, a 3.2T RDMA network, and a Ray-driven elastic AI data lake, we have achieved deep synergy among data, storage, communication, and computation. We have also built an end-to-end evaluation system, creating a closed loop from training to simulation to assessment. This framework has already been fully validated on thousand-GPU clusters, laying a crucial technical foundation for the development and application of next-generation autonomous intelligent robots, and is expected to accelerate the arrival of the era of human-machine integration.

cs.RO↗

arXiv2026

The Spectroscopic and Photometric Study of a Star Cluster Sample in Andromeda Halo

Halo star clusters serve as vital tracers for the formation and evolution of the Andromeda galaxy. In this work, we present physical parameters for 29 M31 halo star clusters, derived from a combination of spectroscopic and photometric data. Low-resolution spectra were acquired using the BFOSC spectrograph on the NAOC Xinglong 2.16-m telescope. For the photometric analysis, we utilized uSC and vSAGE bands from the SAGE survey, complemented by archival data from GALEX(NUV, FUV), PAN-STARRS(grizy) and the 2MASS(JHK). Ages and metallicities were determined via ULySS (Vazdekis et al. and pegase-hr) SSP model and the Bruzual & Charlot (2003) (BC03) stellar population synthesis models. The derived parameters show good agreement with literature values. Notably, for three of these clusters, this study represents the first combined photometric and spectroscopic analysis.

astro-ph.GA↗

arXiv2026

VibeContract: The Missing Quality Assurance Piece in Vibe Coding

Recent advances in large language models (LLMs) have given rise to vibe coding, a style of software development where developers rely on AI coding assistants to generate, modify, and refactor code using natural language instructions. While this paradigm accelerates software development and lowers barriers to entry, it introduces new challenges for quality assurance (QA). AI-generated code can appear correct but often contains hidden logical errors and inconsistencies, creating an urgent need for novel QA approaches. In this vision paper, we propose the VibeContract paradigm as a missing piece in vibe coding. In this approach, high-level natural-language intent is decomposed into explicit task sequences, and task-level contracts are generated to capture expected inputs, outputs, constraints, and behavioral properties. Developers validate these contracts, and traceability is maintained between tasks, contracts, and generated code. Contracts then guide LLMs for testing, runtime verification, and debugging, enabling QA to occur continuously and proactively alongside code generation. We demonstrate how the VibeContract paradigm can substantially improve the correctness, robustness, and maintainability of LLM-generated code through an example project. We argue that VibeContract introduces a structured and verifiable development workflow that transforms vibe coding from a fast but error-prone practice into a predictable, auditable, and trustworthy software engineering process. Finally, we outline the key challenges, design principles, and a forward-looking research agenda required to realize this vision.

cs.SE↗

arXiv2026

OneOcc: Semantic Occupancy Prediction for Legged Robots with a Single Panoramic Camera

Robust 3D semantic occupancy is crucial for legged/humanoid robots, yet most semantic scene completion (SSC) systems target wheeled platforms with forward-facing sensors. We present OneOcc, a vision-only panoramic SSC framework designed for gait-introduced body jitter and 360° continuity. OneOcc combines: (i) Dual-Projection fusion (DP-ER) to exploit the annular panorama and its equirectangular unfolding, preserving 360° continuity and grid alignment; (ii) Bi-Grid Voxelization (BGV) to reason in Cartesian and cylindrical-polar spaces, reducing discretization bias and sharpening free/occupied boundaries; (iii) a lightweight decoder with Hierarchical AMoE-3D for dynamic multi-scale fusion and better long-range/occlusion reasoning; and (iv) plug-and-play Gait Displacement Compensation (GDC) learning feature-level motion correction without extra sensors. We also release two panoramic occupancy benchmarks: QuadOcc (real quadruped, first-person 360°) and Human360Occ (H3O) (CARLA human-ego 360° with RGB, Depth, semantic occupancy; standardized within-/cross-city splits). OneOcc sets a new state of the art on QuadOcc, outperforming strong vision baselines and remaining competitive with classical LiDAR baselines; on H3O it gains +3.83 mIoU (within-city) and +8.08 (cross-city). Modules are lightweight, enabling deployable full-surround perception for legged/humanoid robots. Datasets and code will be publicly available at https://github.com/MasterHow/OneOcc.

cs.RO↗

arXiv2026

ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps

Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on more complex tasks involving mathematics and logic. To bridge this gap, we introduce ReasonMap, a novel benchmark specifically designed to evaluate these capabilities. ReasonMap encompasses high-resolution transit maps from 30 cities and includes 1,008 question-answer pairs spanning two question types and three templates. Furthermore, we design a two-level evaluation pipeline that properly assesses answer correctness and quality. Our comprehensive evaluation of 16 popular MLLMs reveals a counterintuitive pattern: among open-source models, base variants outperform their reasoning-tuned counterparts, whereas the opposite trend is observed in closed-source models. Further analysis under the visual-masking setting confirms that strong performance necessitates direct visual grounding, rather than relying solely on language priors. We further establish a training baseline with reinforcement fine-tuning, providing a reference for future exploration. We hope this benchmark study offers new insights into visual reasoning and helps investigate the gap between open- and closed-source models.

cs.CV↗

arXiv2026

HeartAgent: An Autonomous Agent System for Explainable Differential Diagnosis in Cardiology

Heart diseases remain a leading cause of morbidity and mortality worldwide, necessitating accurate and trustworthy differential diagnosis. However, existing artificial intelligence-based diagnostic methods are often limited by insufficient cardiology knowledge, inadequate support for complex reasoning, and poor interpretability. Here we present HeartAgent, a cardiology-specific agent system designed to support a reliable and explainable differential diagnosis. HeartAgent integrates customized tools and curated data resources and orchestrates multiple specialized sub-agents to perform complex reasoning while generating transparent reasoning trajectories and verifiable supporting references. Evaluated on the MIMIC dataset and a private electronic health records cohort, HeartAgent achieved over 36% and 20% improvements over established comparative methods, in top-3 diagnostic accuracy, respectively. Additionally, clinicians assisted by HeartAgent demonstrated gains of 26.9% in diagnostic accuracy and 22.7% in explanatory quality compared with unaided experts. These results demonstrate that HeartAgent provides reliable, explainable, and clinically actionable decision support for cardiovascular care.

cs.CL↗

arXiv2026

Fairness-Aware Fine-Tuning of Vision-Language Models for Medical Glaucoma Diagnosis

Vision-language models achieve expert-level performance on medical imaging tasks but exhibit significant diagnostic accuracy disparities across demographic groups. We introduce fairness-aware Low-Rank Adaptation for medical VLMs, combining parameter efficiency with explicit fairness optimization. Our key algorithmic contribution is a differentiable MaxAccGap loss that enables end-to-end optimization of accuracy parity across demographic groups. We propose three methods: FR-LoRA integrates MaxAccGap regularization into the training objective, GR-LoRA applies inverse frequency weighting to balance gradient contributions, and Hybrid-LoRA combines both mechanisms. Evaluated on 10,000 glaucoma fundus images, GR-LoRA reduces diagnostic accuracy disparities by 69% while maintaining 53.15% overall accuracy. Ablation studies reveal that strong regularization strength achieves optimal fairness with minimal accuracy trade-off, and race-specific optimization yields 60% disparity reduction. Our approach requires only 0.24% trainable parameters, enabling practical deployment of fair medical AI in resource-constrained healthcare settings.

cs.CV↗

arXiv2026

Plan and Budget: Effective and Efficient Test-Time Scaling on Reasoning Large Language Models

Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode in many prevalent LLMs, overthinking, where models generate verbose and tangential reasoning traces even for simple queries. Recent work has tried to mitigate this by enforcing fixed token budgets, however, this can lead to underthinking, especially on harder problems. Through empirical analysis, we identify that this inefficiency often stems from unclear problem-solving strategies. To formalize this, we develop a theoretical model, BAM (Budget Allocation Model), which models reasoning as a sequence of sub-questions with varying uncertainty, and introduce the E3 metric to capture the trade-off between correctness and computation efficiency. Building on theoretical results from BAM, we propose Plan-and-Budget, a model-agnostic, test-time framework that decomposes complex queries into sub-questions and allocates token budgets based on estimated complexity using adaptive scheduling. Plan-and-Budget improves reasoning efficiency across a range of tasks and models, achieving up to 70% accuracy gains, 39% token reduction, and 193.8% improvement in E3. Notably, it improves the efficiency of a smaller model (DS-Qwen-32B) to match the efficiency of a larger model (DS-LLaMA-70B), demonstrating Plan-and-Budget's ability to close performance gaps without retraining. Our code is available at https://github.com/junhongmit/P-and-B.

cs.LG↗

arXiv2026

FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning

Large language models (LLMs) increasingly rely on Chain-of-Thought (CoT) prompting to improve problem-solving and provide seemingly transparent explanations. However, growing evidence shows that CoT often fail to faithfully represent the underlying reasoning process, raising concerns about their reliability in high-risk applications. Although prior studies have focused on mechanism-level analyses showing that CoTs can be unfaithful, they leave open the practical challenge of deciding whether a specific trajectory is faithful to the internal reasoning of the model. To address this gap, we introduce FaithCoT-Bench, a unified benchmark for instance-level CoT unfaithfulness detection. Our framework establishes a rigorous task formulation that formulates unfaithfulness detection as a discriminative decision problem, and provides FINE-CoT (Faithfulness instance evaluation for Chain-of-Thought), an expert-annotated collection of over 1,000 trajectories generated by four representative LLMs across four domains, including more than 300 unfaithful instances with fine-grained causes and step-level evidence. We further conduct a systematic evaluation of eleven representative detection methods spanning counterfactual, logit-based, and LLM-as-judge paradigms, deriving empirical insights that clarify the strengths and weaknesses of existing approaches and reveal the increased challenges of detection in knowledge-intensive domains and with more advanced models. To the best of our knowledge, FaithCoT-Bench establishes the first comprehensive benchmark for instance-level CoT faithfulness, setting a solid basis for future research toward more interpretable and trustworthy reasoning in LLMs.

cs.AI↗

arXiv2026

Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion

Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome these limitations, we introduce a novel distillation framework that moves beyond simple mimicry to instill a deeper conceptual understanding. Our framework features two key innovations. \underline{\textit{First}}, to address pattern memorization, Explanatory Inversion (EI) generates targeted ``explanatory probes'' that compel the student to articulate the underlying logic behind an answer, rather than just memorizing it. \underline{\textit{Second}}, to improve generalization, Explanatory GRPO (\texttt{EXGRPO}) uses a reinforcement learning algorithm with a novel Dialogue Structure Utility Bonus, which explicitly rewards the student for maintaining a coherent reasoning process across these probes. Extensive evaluations on 12 datasets demonstrate significant improvements. Using Gemma-7b as the student model, our method yields an average \textbf{20.39\%} increase over zero-shot performance and a \textbf{6.02\%} improvement over the state-of-the-art distillation baselines. Moreover, models distilled with our method show remarkable training efficiency (e.g., surpassing vanilla fine-tuning with \textbf{10-25\%} training data) and strong generalization to out-of-distribution tasks. Implementation is released at https://github.com/Zhen-Tan-dmml/ExGRPO.git.

cs.CL↗

arXiv2026

Varying core-envelope coupling efficiency identified from stellar rotation--activity relation

Core-envelope coupling provides a reasonable explanation of the spin-down stalling of stars in open clusters, which was not predicted by classical gyrochronology. However, it remains an open question whether the coupling efficiency is constant or variable. M dwarfs, possessing thicker convective envelopes and thus longer coupling timescales than other late-type stars, are ideal objects for this investigation. In this work, based on the $R_{\rm{HK}}^{'}$ measurements from LAMOST and DESI spectra, we construct new rotation--activity relations for M dwarfs. Unlike the traditional picture, we suggest that the new relation consists of three distinct regimes of fast, intermediate, and slow rotation, closely matching the three sequences of gyrochronology, namely the ``Convective'' sequence, ``Gap'', and ``Interface'' sequence. Our study reveals, for the first time, a variable activity decay rate in the intermediate-rotation regime (i.e., the ``Gap'' region). This implies a varying core-envelope coupling efficiency, peaking towards the end of this region. It also coincides with the well-known stage of stalled stellar spin-down.

astro-ph.SR↗

arXiv2026

Retarded Stellar Dynamo in Tidally Deformed M Dwarfs

Current studies of stellar dynamos primarily focus on spherical stars, leaving their behavior in distorted stars largely unexplored. We utilize stars of varying distortions to examine the relation between stellar cycle periods ($P_{\rm cyc}$) and rotational periods ($P_{\rm rot}$), which are closely linked to dynamo processes. By analyzing a sample of tidally distorted M dwarfs in cataclysmic variables, we identify an anti-correlation between $P_{\rm cyc}$ and $P_{\rm rot}$, in contrast to the lack of such a relation in single M dwarfs. This means that stars with greater deformation have longer cycle periods, suggesting variations in dynamo behavior under non-spherical geometries. Our numerical simulations further reveal that, the thermal convection weakens in highly distorted stars, and subsequently, the differential rotation is also reduced. These effects may lengthen the conversion timescale between poloidal and toroidal magnetic fields, potentially explaining the newly discovered $P_{\rm cyc}$-$P_{\rm rot}$ relation in cataclysmic variables.

astro-ph.SR↗

arXiv2026

RegTrack: Simplicity Beneath Complexity in Robust Multi-Modal 3D Multi-Object Tracking

Existing 3D multi-object tracking (MOT) methods often sacrifice efficiency and generalizability for robustness, largely relying on complex association metrics derived from multi-modal architectures and class-specific motion priors. Challenging the rooted belief that greater complexity necessarily yields greater robustness, we propose a robust, efficient, and generalizable method for multi-modal 3D MOT, dubbed RegTrack. Inspired by Yang-Mills gauge theory, RegTrack is built upon a unified tri-cue encoder (UTEnc), comprising three tightly coupled components: a local-global point cloud encoder (LG-PEnc), a mixture-of-experts-based geometry encoder (MoE-GEnc), and an image encoder from a well-pretrained visual-language model. LG-PEnc efficiently encodes the spatial and structural information of point clouds to produce foundational representations for each object, whose pairwise similarities serve as the sole association metric. MoE-GEnc seamlessly interacts with LG-PEnc to model inter-object geometric relationships across frames, adaptively compensating for inter-frame object motion without relying on any class-specific priors. The image encoder is kept frozen and is used exclusively during training to provide a well-pretrained representation space. Point cloud representations are aligned to this space to supervise the motion compensation process, encouraging representation invariance across frames for the same object while enhancing discriminability among different objects. Through this formulation, RegTrack attains robust, efficient, and generalizable inference using only point cloud inputs, requiring just 2.6M parameters. Extensive experiments on KITTI and nuScenes show that RegTrack outperforms its thirty-five competitors.

cs.CV↗

arXiv2026

RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning

Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even advanced MLLMs struggle with spatial reasoning in structured and information-rich settings such as transit maps, a task of clear practical and scientific importance. However, standard reinforcement learning (RL) on such tasks is impeded by sparse rewards and unstable optimization. To address this, we first construct ReasonMap-Plus, an extended dataset that introduces dense reward signals through Visual Question Answering (VQA) tasks, enabling effective cold-start training of fine-grained visual understanding skills. Next, we propose RewardMap, a multi-stage RL framework designed to improve both visual understanding and reasoning capabilities of MLLMs. RewardMap incorporates two key designs. First, we introduce a difficulty-aware reward design that incorporates detail rewards, directly tackling the sparse rewards while providing richer supervision. Second, we propose a multi-stage RL scheme that bootstraps training from simple perception to complex reasoning tasks, offering a more effective cold-start strategy than conventional Supervised Fine-Tuning (SFT). Experiments on ReasonMap and ReasonMap-Plus demonstrate that each component of RewardMap contributes to consistent performance gains, while their combination yields the best results. Moreover, models trained with RewardMap achieve an average improvement of 3.47% across 6 benchmarks spanning spatial reasoning, fine-grained visual reasoning, and general tasks beyond transit maps, underscoring enhanced visual understanding and reasoning capabilities.

cs.CV↗

arXiv2026

CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use

AI agents are increasingly used to solve real-world tasks by reasoning over multi-turn user interactions and invoking external tools. However, applying reinforcement learning to such settings remains difficult: realistic objectives often lack verifiable rewards and instead emphasize open-ended behaviors; moreover, RL for multi-turn, multi-step agentic tool use is still underexplored; and building and maintaining executable tool environments is costly, limiting scale and coverage. We propose CM2, an RL framework that replaces verifiable outcome rewards with checklist rewards. CM2 decomposes each turn's intended behavior into fine-grained binary criteria with explicit evidence grounding and structured metadata, turning open-ended judging into more stable classification-style decisions. To balance stability and informativeness, our method adopts a strategy of sparse reward assignment but dense evaluation criteria. Training is performed in a scalable LLM-simulated tool environment, avoiding heavy engineering for large tool sets. Experiments show that CM2 consistently improves over supervised fine-tuning. Starting from an 8B Base model and training on an 8k-example RL dataset, CM2 improves over the SFT counterpart by 8 points on tau^-Bench, by 10 points on BFCL-V4, and by 12 points on ToolSandbox. The results match or even outperform similarly sized open-source baselines, including the judging model. CM2 thus provides a scalable recipe for optimizing multi-turn, multi-step tool-using agents without relying on verifiable rewards. Code provided by the open-source community: https://github.com/namezhenzhang/CM2-RLCR-Tool-Agent.

cs.AI↗