OpenRaiser/Intern-Atlas huggingface dataset:
Evolutionary hypergame dynamics: Introspection reasoning and social learning
Venue: arXiv cs.GT 2025
Year: 2025

In the realm of evolutionary game theory, standard frameworks typically presuppose that every player possesses comprehensive knowledge and unrestricted access to the entire strategy space. However, real-world human society inherently harbors diverse levels of knowledge, experience, and background among individuals. Hypergames incorporate this heterogeneity by permitting individuals to differ in their access to the full strategy set, reflecting cognitive or informational constraints and giving rise to asymmetric strategic interactions. Yet, their evolutionary consequences remain underexplored. Our inquiry employs prototype models featuring three available strategies, focusing on social dilemmas involving cooperation, defection, and loner. These strategies manifest cyclic dominance, akin to the well-studied rock-paper-scissors dynamics, a foundational model in game theory. Our study spans both well-mixed and spatial lattice populations, delving into the intricacies of learning and evolution of the strategy set within the evolutionary hypergame dynamics. In stark contrast to traditional evolutionary game dynamics, our findings unveil nuanced and intricate phases, encompassing scenarios of loner dominance, coexistence of multiple strategy sets, combinations of cooperation and loner dominance, and more. Remarkably, we discern that heightened rationality significantly promotes cooperative behaviors.
DynaMIC: Dynamic Multimodal In-Context Learning Enabled Embodied Robot Counterfactual Resistance Ability
Venue: arXiv cs.RO 2025
Year: 2025

The emergence of large pre-trained models based on natural language has breathed new life into robotics development. Extensive research has integrated large models with robots, utilizing the powerful semantic understanding and generation capabilities of large models to facilitate robot control through natural language instructions gradually. However, we found that robots that strictly adhere to human instructions, especially those containing misleading information, may encounter errors during task execution, potentially leading to safety hazards. This resembles the concept of counterfactuals in natural language processing (NLP), which has not yet attracted much attention in robotic research. In an effort to highlight this issue for future studies, this paper introduced directive counterfactuals (DCFs) arising from misleading human directives. We present DynaMIC, a framework for generating robot task flows to identify DCFs and relay feedback to humans proactively. This capability can help robots be sensitive to potential DCFs within a task, thus enhancing the reliability of the execution process. We conducted semantic-level experiments and ablation studies, showcasing the effectiveness of this framework.
Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint
Venue: arXiv cs.CV 2025
Year: 2025

This work presents DCFlow, a novel unsupervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous approaches that implicitly learn flow estimation solely from appearance similarity, we introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly. This is achieved by collaboratively training a modality transfer network and a flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly optimize both networks, significantly improving flow prediction accuracy. For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-art performance among unsupervised approaches.
NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding
Venue: arXiv cs.CV 2025
Year: 2025

We introduce NeoWorld, a deep learning framework for generating interactive 3D virtual worlds from a single input image. Inspired by the on-demand worldbuilding concept in the science fiction novel Simulacron-3 (1964), our system constructs expansive environments where only the regions actively explored by the user are rendered with high visual realism through object-centric 3D representations. Unlike previous approaches that rely on global world generation or 2D hallucination, NeoWorld models key foreground objects in full 3D, while synthesizing backgrounds and non-interacted regions in 2D to ensure efficiency. This hybrid scene structure, implemented with cutting-edge representation learning and object-to-3D techniques, enables flexible viewpoint manipulation and physically plausible scene animation, allowing users to control object appearance and dynamics using natural language commands. As users interact with the environment, the virtual world progressively unfolds with increasing 3D detail, delivering a dynamic, immersive, and visually coherent exploration experience. NeoWorld significantly outperforms existing 2D and depth-layered 2.5D methods on the WorldScore benchmark.
Contrastive Learning for Correlating Network Incidents
Venue: arXiv cs.NI 2025
Year: 2025

Internet service providers monitor their networks to detect, triage, and remediate service impairments. When an incident is detected, it is important to determine whether similar incidents have occurred in the past or are happening concurrently elsewhere in the network. Manual correlation of such incidents is infeasible due to the scale of the networks under observation, making automated correlation a necessity. This paper presents a self-supervised learning method for similarity-based correlation of network situations. Using this method, a deep neural network is trained on a large unlabeled dataset of network situations using contrastive learning. High precision achieved in experiments on real-world network monitoring data suggests that contrastive learning is a promising approach to network incident correlation.
Generalist Multi-Class Anomaly Detection via Distillation to Two Heterogeneous Student Networks
Venue: arXiv cs.CV 2025
Year: 2025

Anomaly detection (AD) plays an important role in various real-world applications. Recent advancements in AD, however, are often biased towards industrial inspection, struggle to generalize to broader tasks like semantic anomaly detection and vice versa. Although recent methods have attempted to address general anomaly detection, their performance remains sensitive to dataset-specific settings and single-class tasks. In this paper, we propose a novel dual-model ensemble approach based on knowledge distillation (KD) to bridge this gap. Our framework consists of a teacher and two student models: an Encoder-Decoder model, specialized in detecting patch-level minor defects for industrial AD and an Encoder-Encoder model, optimized for semantic AD. Both models leverage a shared pre-trained encoder (DINOv2) to extract high-quality feature representations. The dual models are jointly learned using the Noisy-OR objective, and the final anomaly score is obtained using the joint probability via local and semantic anomaly scores derived from the respective models. We evaluate our method on eight public benchmarks under both single-class and multi-class settings: MVTec-AD, MVTec-LOCO, VisA and Real-IAD for industrial inspection and CIFAR-10/100, FMNIST and View for semantic anomaly detection. The proposed method achieved state-of-the-art accuracies in both domains, in multi-class as well as single-class settings, demonstrating generalization across multiple domains of anomaly detection. Our model achieved an image-level AUROC of 99.7% on MVTec-AD and 97.8% on CIFAR-10, which is significantly better than the prior general AD models in multi-class settings and even higher than the best specialist models on individual benchmarks.
LaMoGen: Laban Movement-Guided Diffusion for Text-to-Motion Generation
Venue: arXiv cs.CV 2025
Year: 2025

Diverse human motion generation is an increasingly important task, having various applications in computer vision, human-computer interaction and animation. While text-to-motion synthesis using diffusion models has shown success in generating high-quality motions, achieving fine-grained expressive motion control remains a significant challenge. This is due to the lack of motion style diversity in datasets and the difficulty of expressing quantitative characteristics in natural language. Laban movement analysis has been widely used by dance experts to express the details of motion including motion quality as consistent as possible. Inspired by that, this work aims for interpretable and expressive control of human motion generation by seamlessly integrating the quantification methods of Laban Effort and Shape components into the text-guided motion generation models. Our proposed zero-shot, inference-time optimization method guides the motion generation model to have desired Laban Effort and Shape components without any additional motion data by updating the text embedding of pretrained diffusion models during the sampling step. We demonstrate that our approach yields diverse expressive motion qualities while preserving motion identity by successfully manipulating motion attributes according to target Laban tags.
Robust Multimodal Semantic Segmentation with Balanced Modality Contributions
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal semantic segmentation enhances model robustness by exploiting cross-modal complementarities. However, existing methods often suffer from imbalanced modal dependencies, where overall performance degrades significantly once a dominant modality deteriorates in real-world scenarios. Thus, modality balance has become acritical challenge for practical multimodal segmentation. To address this issue, we propose EQUISeg, a multimodal segmentation framework that balances modality contributions through equal encoding of modalities. Built upon a four-stage Cross-modal Transformer Block(CMTB), EQUISeg enables efficient multimodal fusion and hierarchical selection. Furthermore, we design a Self-guided Module(SGM) that mitigates modality imbalance by introducing a mutual guidance mechanism, enabling each modality to adaptively adjust its contribution and enhance robustness under degraded conditions. Extensive experiments on multiple datasets demonstrate that EQUISeg achieves significant performance gains and effectively alleviates the adverse effects of modality imbalance in segmentation tasks.
SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) can translate natural language requirements into code, yet empirical analyses of representative models reveal that semantic errors-programs that compile but behave incorrectly-constitute the majority of observed faults (e.g., >60% on DeepSeek-Coder-6.7B and QwenCoder-7B). Post-hoc repair pipelines detect such faults only after execution, incurring latency, relying on incomplete test suites, and often mis-localizing the defect. Since semantic drift originates in the autoregressive decoding process, intervening while the code is being generated is a direct way to stop error propagation. Constrained-decoding approaches such as ROCODE attempt this, but still wait until the entire program runs to obtain feedback and use entropy heuristics that do not truly capture semantics. A more effective solution must inject semantic signals-early and precisely-into the decoding process.We present SemGuard, a semantic-evaluator-driven framework that performs real-time, line-level semantic supervision. To train the evaluator, we build SemDiff, the first dataset with fine-grained annotations that mark the exact line where a correct and an incorrect implementation diverge. The evaluator, once embedded in the LLM's decoder, flags deviations on partial code, rolls back to the faulty line, and guides regeneration-without executing the program or requiring test cases. Across four benchmarks, SemGuard consistently outperforms state-of-the-art baselines. It lowers the semantic error rate by 19.86% on SemDiff relative to ROCODE, and lifts Pass@1 by 48.92% on the real-world LiveCodeBench with CodeLlama-7B. Similar gains hold for StarCoder2-7B on MBPP and for DeepSeekCoder-6.7B on the Java benchmark SemDiff-Java, demonstrating model- and language-agnostic effectiveness.
CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D
Venue: arXiv cs.CV 2025
Year: 2025

3D scene understanding is fundamental for embodied AI and robotics, supporting reliable perception for interaction and navigation. Recent approaches achieve zero-shot, open-vocabulary 3D semantic mapping by assigning embedding vectors to 2D class-agnostic masks generated via vision-language models (VLMs) and projecting these into 3D. However, these methods often produce fragmented masks and inaccurate semantic assignments due to the direct use of raw masks, limiting their effectiveness in complex environments. To address this, we leverage SemanticSAM with progressive granularity refinement to generate more accurate and numerous object-level masks, mitigating the over-segmentation commonly observed in mask generation models such as vanilla SAM, and improving downstream 3D semantic segmentation. To further enhance semantic context, we employ a context-aware CLIP encoding strategy that integrates multiple contextual views of each mask using empirically determined weighting, providing much richer visual context. We evaluate our approach on multiple 3D scene understanding tasks, including 3D semantic segmentation and object retrieval from language queries, across several benchmark datasets. Experimental results demonstrate significant improvements over existing methods, highlighting the effectiveness of our approach.
Unlocking the Potential of Soft Actor-Critic for Imitation Learning
Venue: arXiv cs.RO 2025
Year: 2025

Learning-based methods have enabled robots to acquire bio-inspired movements with increasing levels of naturalness and adaptability. Among these, Imitation Learning (IL) has proven effective in transferring complex motion patterns from animals to robotic systems. However, current state-of-the-art frameworks predominantly rely on Proximal Policy Optimization (PPO), an on-policy algorithm that prioritizes stability over sample efficiency and policy generalization. This paper proposes a novel IL framework that combines Adversarial Motion Priors (AMP) with the off-policy Soft Actor-Critic (SAC) algorithm to overcome these limitations. This integration leverages replay-driven learning and entropy-regularized exploration, enabling naturalistic behavior and task execution, improving data efficiency and robustness. We evaluate the proposed approach (AMP+SAC) on quadruped gaits involving multiple reference motions and diverse terrains. Experimental results demonstrate that the proposed framework not only maintains stable task execution but also achieves higher imitation rewards compared to the widely used AMP+PPO method. These findings highlight the potential of an off-policy IL formulation for advancing motion generation in robotics.
Short window attention enables long-term memorization
Venue: arXiv cs.LG 2025
Year: 2025

Recent works show that hybrid architectures combining sliding window softmax attention layers with linear recurrent neural network (RNN) layers outperform both of these architectures taken separately. However, the impact of the window length and the interplay between softmax attention and linear RNN layers remain under-studied. In this work, we introduce SWAX, a hybrid architecture consisting of sliding-window attention and xLSTM linear RNN layers. A counter-intuitive finding with SWAX is that larger sliding windows do not improve the long-context performance. In fact, short window attention encourages the model to better train the long-term memory of the xLSTM, by relying less on the softmax attention mechanism for long context-retrieval. The issue with small sliding windows is that they are detrimental for short-context tasks, which could be solved with information from moderately larger sliding windows otherwise. Therefore, we train SWAX by stochastically changing the sliding window size, forcing the model to leverage both a longer context window and the xLSTM memory. SWAX trained with stochastic window sizes significantly outperforms regular window attention both on short and long-context problems.
AdaThink-Med: Medical Adaptive Thinking with Uncertainty-Guided Length Calibration
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in inference time scaling with extended long chain-of thought have significantly improved the reasoning capabilities of both general and medical large language models (LLMs). However, these models tend to engage in lengthy reasoning processes regardless of the difficulty of the input question, leading to increased inference costs in real-world applications. Therefore, enabling adaptive thinking where models think less for simpler questions and think more for complex ones is critical for the effective use of medical LLMs in practice. Despite its importance, there is a lack of end-to-end approaches designed to enhance the adaptive thinking capabilities of medical LLMs while providing a comprehensive examination of the trade-off between performance and computational cost. To bridge this gap, we propose AdaThink-Med, the first end-to-end framework designed to enhance adaptive thinking ability in medical reasoning models with uncertainty-guided length calibration. AdaThink-Med first generates multiple candidate outputs for each question, evaluates the correctness and uncertainty of each candidate, and then estimates problem difficulty via an uncertainty-guided length calibration module. For outputs with low difficulty and correct answers, the framework penalizes longer reasoning paths; whereas for those with high difficulty and incorrect answers, it encourages extending the chain of thought to explore alternative solutions. On six public medical QA benchmarks, AdaThink-Med achieves up to 6.4x length reduction on average while retaining performance with only minimal degradation. Intriguingly, we observe that AdaThink-Med spontaneously develops two distinct reasoning modes, which we characterize as "non-thinking" and "thinking", demonstrating the model's ability to suppress redundant reasoning processes dynamically.
CURA: Size Isnt All You Need -- A Compact Universal Architecture for On-Device Intelligence
Venue: arXiv cs.LG 2025
Year: 2025

Existing on-device AI architectures for resource-constrained environments face two critical limitations: they lack compactness, with parameter requirements scaling proportionally to task complexity, and they exhibit poor generalizability, performing effectively only on specific application domains (e.g., models designed for regression tasks cannot adapt to natural language processing (NLP) applications). In this paper, we propose CURA, an architecture inspired by analog audio signal processing circuits that provides a compact and lightweight solution for diverse machine learning tasks across multiple domains. Our architecture offers three key advantages over existing approaches: (1) Compactness: it requires significantly fewer parameters regardless of task complexity; (2) Generalizability: it adapts seamlessly across regression, classification, complex NLP, and computer vision tasks; and (3) Complex pattern recognition: it can capture intricate data patterns while maintaining extremely low model complexity. We evaluated CURA across diverse datasets and domains. For compactness, it achieved equivalent accuracy using up to 2,500 times fewer parameters compared to baseline models. For generalizability, it demonstrated consistent performance across four NLP benchmarks and one computer vision dataset, nearly matching specialized existing models (achieving F1-scores up to 90%). Lastly, it delivers superior forecasting accuracy for complex patterns, achieving 1.6 times lower mean absolute error and 2.1 times lower mean squared error than competing models.
Biomechanical-phase based Temporal Segmentation in Sports Videos: a Demonstration on Javelin-Throw
Venue: arXiv cs.CV 2025
Year: 2025

Precise analysis of athletic motion is central to sports analytics, particularly in disciplines where nuanced biomechanical phases directly impact performance outcomes. Traditional analytics techniques rely on manual annotation or laboratory-based instrumentation, which are time-consuming, costly, and lack scalability. Automatic extraction of relevant kinetic variables requires a robust and contextually appropriate temporal segmentation. Considering the specific case of elite javelin-throw, we present a novel unsupervised framework for such a contextually aware segmentation, which applies the structured optimal transport (SOT) concept to augment the well-known Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN). This enables the identification of motion phase transitions without requiring expensive manual labeling. Extensive experiments demonstrate that our approach outperforms state-of-the-art unsupervised methods, achieving 71.02% mean average precision (mAP) and 74.61% F1-score on test data, substantially higher than competing baselines. We also release a new dataset of 211 manually annotated professional javelin-throw videos with frame-level annotations, covering key biomechanical phases: approach steps, drive, throw, and recovery.
OrthAlign: Orthogonal Subspace Decomposition for Non-Interfering Multi-Objective Alignment
Venue: arXiv cs.LG 2025
Year: 2025

Large language model (LLM) alignment faces a critical dilemma when addressing multiple human preferences: improvements in one dimension frequently come at the expense of others, creating unavoidable trade-offs between competing objectives like helpfulness and harmlessness. While prior work mainly focuses on constraint-based optimization algorithms and data selection strategies to mitigate conflicts, these approaches overlook the fundamental issue of resolving conflicts directly at the parameter level. In this paper, we present OrthAlign, an innovative approach that pioneers a new paradigm by leveraging orthogonal subspace decomposition to fundamentally resolve gradient-level conflicts in multi-objective preference alignment. OrthAlign strategically decomposes parameter update spaces into orthogonal subspaces, ensuring that optimization toward different preferences occurs in mathematically non-interfering directions. Building upon this, we provide theoretical guarantees demonstrating that when parameter increments satisfy both orthogonal subspace constraints and spectral norm bounds, the resulting updates exhibit linear Lipschitz growth rather than exponential instability, ensuring stable convergence across all preference dimensions. Extensive experiments show that: I. OrthAlign achieves maximum single-preference improvements ranging from 34.61% to 50.89% after multiple-objective alignment across helpful, harmless, and truthful dimensions. II. With an average overall reward improvement of 13.96%.