OpenRaiser/Intern-Atlas huggingface dataset:
EKF-Based Fusion of Wi-Fi/LiDAR/IMU for Indoor Localization and Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Conventional Wi-Fi received signal strength indicator (RSSI) fingerprinting cannot meet the growing demand for accurate indoor localization and navigation due to its lower accuracy, while solutions based on light detection and ranging (LiDAR) can provide better localization performance but is limited by their higher deployment cost and complexity. To address these issues, we propose a novel indoor localization and navigation framework integrating Wi-Fi RSSI fingerprinting, LiDAR-based simultaneous localization and mapping (SLAM), and inertial measurement unit (IMU) navigation based on an extended Kalman filter (EKF). Specifically, coarse localization by deep neural network (DNN)-based Wi-Fi RSSI fingerprinting is refined by IMU-based dynamic positioning using a Gmapping-based SLAM to generate an occupancy grid map and output high-frequency attitude estimates, which is followed by EKF prediction-update integrating sensor information while effectively suppressing Wi-Fi-induced noise and IMU drift errors. Multi-group real-world experiments conducted on the IR building at Xi'an Jiaotong-Liverpool University demonstrates that the proposed multi-sensor fusion framework suppresses the instability caused by individual approaches and thereby provides stable accuracy across all path configurations with mean two-dimensional (2D) errors ranging from 0.2449 m to 0.3781 m. In contrast, the mean 2D errors of Wi-Fi RSSI fingerprinting reach up to 1.3404 m in areas with severe signal interference, and those of LiDAR/IMU localization are between 0.6233 m and 2.8803 m due to cumulative drift.
AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
Venue: arXiv cs.AI 2025
Year: 2025

Dynamically configuring algorithm hyperparameters is a fundamental challenge in computational intelligence. While learning-based methods offer automation, they suffer from prohibitive sample complexity and poor generalization. We introduce AutoEP, a novel framework that bypasses training entirely by leveraging Large Language Models (LLMs) as zero-shot reasoning engines for algorithm control. AutoEP's core innovation lies in a tight synergy between two components: (1) an online Exploratory Landscape Analysis (ELA) module that provides real-time, quantitative feedback on the search dynamics, and (2) a multi-LLM reasoning chain that interprets this feedback to generate adaptive hyperparameter strategies. This approach grounds high-level reasoning in empirical data, mitigating hallucination. Evaluated on three distinct metaheuristics across diverse combinatorial optimization benchmarks, AutoEP consistently outperforms state-of-the-art tuners, including neural evolution and other LLM-based methods. Notably, our framework enables open-source models like Qwen3-30B to match the performance of GPT-4, demonstrating a powerful and accessible new paradigm for automated hyperparameter design. Our code is available at https://anonymous.4open.science/r/AutoEP-3E11
CE-Nav: Flow-Guided Reinforcement Refinement for Cross-Embodiment Local Navigation
Venue: arXiv cs.RO 2025
Year: 2025

Generalizing local navigation policies across diverse robot morphologies is a critical challenge. Progress is often hindered by the need for costly and embodiment-specific data, the tight coupling of planning and control, and the "disastrous averaging" problem where deterministic models fail to capture multi-modal decisions (e.g., turning left or right). We introduce CE-Nav, a novel two-stage (IL-then-RL) framework that systematically decouples universal geometric reasoning from embodiment-specific dynamic adaptation. First, we train an embodiment-agnostic General Expert offline using imitation learning. This expert, a conditional normalizing flow model named VelFlow, learns the full distribution of kinematically-sound actions from a large-scale dataset generated by a classical planner, completely avoiding real robot data and resolving the multi-modality issue. Second, for a new robot, we freeze the expert and use it as a guiding prior to train a lightweight, Dynamics-Aware Refiner via online reinforcement learning. This refiner rapidly learns to compensate for the target robot's specific dynamics and controller imperfections with minimal environmental interaction. Extensive experiments on quadrupeds, bipeds, and quadrotors show that CE-Nav achieves state-of-the-art performance while drastically reducing adaptation cost. Successful real-world deployments further validate our approach as an efficient and scalable solution for building generalizable navigation systems. Code is available at https://github.com/amap-cvlab/CE-Nav.
WirelessMathLM: Teaching Mathematical Reasoning for LLMs in Wireless Communications with Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) excel at general mathematical reasoning but fail catastrophically on specialized technical mathematics. In wireless communications, where problems require precise manipulation of information-theoretic bounds, optimization constraints, and signal processing formulations, even state-of-the-art models struggle to achieve competent performance. We present WirelessMathLM, demonstrating that compact models (0.5B-7B parameters) can match or exceed much larger models through domain-specific reinforcement learning with verifiable rewards. Our key insight is that wireless mathematics problems possess a unique property--verifiable correctness--that enables effective reinforcement learning without human feedback. We construct WirelessMathBench-XL, a comprehensive benchmark of 4,027 problems from 970 papers. Using Group Relative Policy Optimization (GRPO) with binary verification rewards, we train models directly from base checkpoints without supervised warm-start. Our 7B model achieves 39.5% accuracy on WirelessMathBench-XL, approaching GPT-4o (40.4%) while using about 100 times fewer parameters than DeepSeek-R1 (671B, 57.4%). Remarkably, GRPO training nearly doubles performance across all model scales (0.5B +11%, 3B +103%, 7B +81%), with positive transfer to general mathematics benchmarks--our models gain +8.4 points on average across MATH, Minerva-Math, OlympiadBench, AMC, and AIME without any training on these tasks.
Precise Reasoning About Container-Internal Pointers with Logical Pinning
Venue: arXiv cs.PL 2025
Year: 2025

Most separation logics hide container-internal pointers for modularity. This makes it difficult to specify container APIs that temporarily expose those pointers to the outside, and to verify programs that use these APIs. We present logical pinning, a lightweight borrowing model for sequential programs that allows users to selectively track container-internal pointers at the logical level. Our model generalizes the magic-wand operator for representing partial data structures, making it easy to write and prove precise specifications, including pointer-stability properties. Because it only changes the way representation predicates and specifications are written, our approach is compatible with most separation logic variants. We demonstrate the practicality of logical pinning by verifying small but representative pointer-manipulating programs, and deriving more precise versions of common container specifications. In doing so, we show that our approach subsumes some well-known proof patterns, simplifies some complex proofs, and enables reasoning about program patterns not supported by traditional specifications. All of our results are mechanized in the Rocq proof assistant, using the CFML library.
Patch Rebirth: Toward Fast and Transferable Model Inversion of Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterative optimization, without access to original training data. Unfortunately, its application to state-of-the-art Vision Transformers (ViTs) poses a major computational challenge, due to their expensive self-attention mechanisms. To address this, Sparse Model Inversion (SMI) was proposed to improve efficiency by pruning and discarding seemingly unimportant patches, which were even claimed to be obstacles to knowledge transfer. However, our empirical findings suggest the opposite: even randomly selected patches can eventually acquire transferable knowledge through continued inversion. This reveals that discarding any prematurely inverted patches is inefficient, as it suppresses the extraction of class-agnostic features essential for knowledge transfer, along with class-specific features. In this paper, we propose Patch Rebirth Inversion (PRI), a novel approach that incrementally detaches the most important patches during the inversion process to construct sparse synthetic images, while allowing the remaining patches to continue evolving for future selection. This progressive strategy not only improves efficiency, but also encourages initially less informative patches to gradually accumulate more class-relevant knowledge, a phenomenon we refer to as the Re-Birth effect, thereby effectively balancing class-agnostic and class-specific knowledge. Experimental results show that PRI achieves up to 10x faster inversion than standard Dense Model Inversion (DMI) and 2x faster than SMI, while consistently outperforming SMI in accuracy and matching the performance of DMI.
Self-Consistency as a Free Lunch: Reducing Hallucinations in Vision-Language Models via Self-Reflection
Venue: arXiv cs.CV 2025
Year: 2025

Vision-language models often hallucinate details, generating non-existent objects or inaccurate attributes that compromise output reliability. Existing methods typically address these issues via extensive human annotations or external supervision from more powerful models. In this work, we present a novel framework that leverages the model's self-consistency between long responses and short answers to generate preference pairs for training. We observe that short binary questions tend to yield highly reliable responses, which can be used to query the target model to evaluate and rank its generated responses. Specifically, we design a self-reflection pipeline where detailed model responses are compared against concise binary answers, and inconsistency signals are utilized to automatically curate high-quality training data without human annotations or external model-based supervision. By relying solely on self-consistency rather than external supervision, our method offers a scalable and efficient solution that effectively reduces hallucinations using unlabeled data. Extensive experiments on multiple benchmarks, i.e., AMBER, MultiObject-Hal (ROPE), Object HalBench, and MMHal-Bench, demonstrate significant improvements in factual grounding and reliability. Moreover, our approach maintains robust instruction-following ability, as evidenced by enhanced performance on LLaVA-Bench and MMBench.
Adaptive Token-Weighted Differential Privacy for LLMs: Not All Tokens Require Equal Protection
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) frequently memorize sensitive or personal information, raising significant privacy concerns. Existing variants of differential privacy stochastic gradient descent (DPSGD) inject uniform noise into every gradient step, significantly extending training time and reducing model accuracy. We propose that concentrating noise primarily on gradients associated with sensitive tokens can substantially decrease DP training time, strengthen the protection of sensitive information, and simultaneously preserve the model's performance on non-sensitive data. We operationalize this insight through Adaptive Token-Weighted Differential Privacy (ATDP), a modification of vanilla DP-SGD that adaptively assigns different gradient weights to sensitive and non-sensitive tokens. By employing a larger noise scale at the early stage of training, ATDP rapidly disrupts memorization of sensitive content. As a result, ATDP only requires a few additional epochs of lightweight post-processing following standard fine-tuning, injecting targeted noise primarily on parameters corresponding to sensitive tokens, thus minimally affecting the model's general capabilities. ATDP can be seamlessly integrated into any existing DP-based fine-tuning pipeline or directly applied to non-private models as a fast privacy-enhancing measure. Additionally, combined with an initial redacted fine-tuning phase, ATDP forms a streamlined DP pipeline that achieves comparable canary protection to state-of-the-art DP-SGD methods, significantly reduces the computational overhead of DP fine-tuning, shortening training time by approximately 90 percent, while achieving comparable or superior privacy protection and minimal accuracy degradation.
Explicit modelling of subject dependency in BCI decoding
Venue: arXiv cs.HC 2025
Year: 2025

Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to-end approach that explicitly models the subject dependency using lightweight convolutional neural networks (CNNs) conditioned on the subject's identity. Our method integrates hyperparameter optimization strategies that prioritize class imbalance and evaluates two conditioning mechanisms to adapt pre-trained models to unseen subjects with minimal calibration data. We benchmark three lightweight architectures on a time-modulated Event-Related Potentials (ERP) classification task, providing interpretable evaluation metrics and explainable visualizations of the learned representations. Results demonstrate improved generalization and data-efficient calibration, highlighting the scalability and practicality of subject-adaptive BCIs.
LiDAR-based Human Activity Recognition through Laplacian Spectral Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Human Activity Recognition supports applications in healthcare, manufacturing, and human-machine interaction. LiDAR point clouds offer a privacy-preserving alternative to cameras and are robust to illumination. We propose a HAR method based on graph spectral analysis. Each LiDAR frame is mapped to a proximity graph (epsilon-graph) and the Laplacian spectrum is computed. Eigenvalues and statistics of eigenvectors form pose descriptors, and temporal statistics over sliding windows yield fixed vectors for classification with support vector machines and random forests. On the MM-Fi dataset with 40 subjects and 27 activities, under a strict subject-independent protocol, the method reaches 94.4% accuracy on a 13-class rehabilitation set and 90.3% on all 27 activities. It also surpasses the skeleton-based baselines reported for MM-Fi. The contribution is a compact and interpretable feature set derived directly from point cloud geometry that provides an accurate and efficient alternative to end-to-end deep learning.
GUI-PRA: Process Reward Agent for GUI Tasks
Venue: arXiv cs.AI 2025
Year: 2025

Graphical User Interface (GUI) Agents powered by Multimodal Large Language Models (MLLMs) show significant potential for automating tasks. However, they often struggle with long-horizon tasks, leading to frequent failures. Process Reward Models (PRMs) are a promising solution, as they can guide these agents with crucial process signals during inference. Nevertheless, their application to the GUI domain presents unique challenges. When processing dense artificial inputs with long history data, PRMs suffer from a "lost in the middle" phenomenon, where the overwhelming historical context compromises the evaluation of the current step. Furthermore, standard PRMs lacks GUI changing awareness, providing static evaluations that are disconnected from the dynamic consequences of actions, a critical mismatch with the inherently dynamic nature of GUI tasks. In response to these challenges, we introduce GUI-PRA (Process Reward Agent for GUI Tasks), a judge agent designed to better provide process reward than standard PRM by intelligently processing historical context and actively perceiving UI state changes. Specifically, to directly combat the ``lost in the middle'' phenomenon, we introduce a dynamic memory mechanism consisting of two core components: a Relevance-based Retrieval Module to actively fetch pertinent information from long histories and a Progressive Summarization Module to dynamically condense growing interaction data, ensuring the model focuses on relevant context. Moreover, to address the lack of UI changing awareness, we introduce an Aadaptive UI Perception mechanism. This mechanism enables the agent to reason about UI state changes and dynamically select the most appropriate tool to gather grounded visual evidence, ensuring its evaluation is always informed by the current UI context.
A Novel Narrow Region Detector for Sampling-Based Planners' Efficiency: Match Based Passage Identifier
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous technology, which has become widespread today, appears in many different configurations such as mobile robots, manipulators, and drones. One of the most important tasks of these vehicles during autonomous operations is path planning. In the literature, path planners are generally divided into two categories: probabilistic and deterministic methods. In the analysis of probabilistic methods, the common problem of almost all methods is observed in narrow passage environments. In this paper, a novel sampler is proposed that deterministically identifies narrow passage environments using occupancy grid maps and accordingly increases the amount of sampling in these regions. The codes of the algorithm is provided as open source. To evaluate the performance of the algorithm, benchmark studies are conducted in three distinct categories: specific and random simulation environments, and a real-world environment. As a result, it is observed that our algorithm provides higher performance in planning time and number of milestones compared to the baseline samplers.
Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces
Venue: arXiv cs.CL 2025
Year: 2025

Policy compliance assessment is a fundamental task of evaluating whether an input case strictly complies with a set of human-defined rules, more generally known as policies. In practice, human experts follow a systematic, step-by-step process to identify violations with respect to specific stipulations outlined in the policy. However, such documentation of gold-standard, expert-level reasoning processes is costly to acquire. In this paper, we introduce Policy Reasoning Traces (PRT), a form of specialized generated reasoning chains that serve as a reasoning bridge to improve an LLM's policy compliance assessment capabilities. Our empirical evaluations demonstrate that the use of PRTs for both inference-time and training-time scenarios significantly enhances the performance of open-weight and commercial models, setting a new state-of-the-art for HIPAA and GDPR policies. Beyond accuracy gains, we also highlight how PRTs can improve an LLM's ability to accurately cite policy clauses, as well as influence compliance decisions through their high utilization from the raw chains of thought.
Spatiotemporal Radar Gesture Recognition with Hybrid Spiking Neural Networks: Balancing Accuracy and Efficiency
Venue: arXiv cs.NE 2025
Year: 2025

Radar-based Human Activity Recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first use of Spiking Neural Networks (SNNs) for radar-based HAR on aircraft marshalling signal classification. Our novel hybrid architecture combines convolutional modules for spatial feature extraction with Leaky Integrate-and-Fire (LIF) neurons for temporal processing, inherently capturing gesture dynamics. The model reduces trainable parameters by 88\% with under 1\% accuracy loss compared to baselines, and generalizes well to the Soli gesture dataset. Through systematic comparisons with Artificial Neural Networks, we demonstrate the trade-offs of spiking computation in terms of accuracy, latency, memory, and energy, establishing SNNs as an efficient and competitive solution for radar-based HAR.
GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}isk-aware \textbf{D}ecision making) that combines traditional control with an uncertainty-aware perception technique using active learning with real-time capability for safe robot collision avoidance. By doing so, this manuscript addresses the central challenge in robotics of finding a reasonable compromise between traditional methods and learning algorithms to foster the development of safe, yet efficient and flexible applications. By unifying a reactive model predictive countouring control (RMPCC) with an Iterative Closest Point (ICP) algorithm that enables the attribution of uncertainty sources online using active learning with real-time capability via a probabilistic kernel optimization technique, \emph{GUARD} inherently handles the existing ambiguity of the term \textit{safety} that exists in robotics literature. Experimental studies indicate the high performance of \emph{GUARD}, thereby highlighting the relevance and need to broaden its applicability in future.
Decoupling Reasoning and Perception: An LLM-LMM Framework for Faithful Visual Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Significant advancements in the reasoning capabilities of Large Language Models (LLMs) are now driven by test-time scaling laws, particularly those leveraging extended Chain-of-Thought (CoT) reasoning. Inspired by these breakthroughs, researchers have extended these paradigms to Large Multimodal Models (LMMs). However, a critical limitation emerges: as their reasoning chains extend, LMMs increasingly rely on textual logic, progressively losing grounding in the underlying visual information. This leads to reasoning paths that diverge from the image content, culminating in erroneous conclusions. To address this, we introduce a strikingly simple yet effective training-free visual-reasoning pipeline. The core concept is to decouple the reasoning and perception processes. A powerful LLM orchestrates the high-level reasoning, strategically interrogating a LMM to extract specific visual information required for its logical chain. The LMM, in turn, functions exclusively as a visual question-answering engine, supplying the necessary perceptual details on demand. This lightweight, plug-and-play approach requires no additional training or architectural changes. Comprehensive evaluations validate that our framework effectively governs the visual reasoning process, leading to a significant reduction in visually-unfounded reasoning steps and a substantial improvement in reasoning fidelity.
Emotional Styles Hide in Deep Speaker Embeddings: Disentangle Deep Speaker Embeddings for Speaker Clustering
Venue: arXiv cs.SD 2025
Year: 2025

Speaker clustering is the task of identifying the unique speakers in a set of audio recordings (each belonging to exactly one speaker) without knowing who and how many speakers are present in the entire data, which is essential for speaker diarization processes. Recently, off-the-shelf deep speaker embedding models have been leveraged to capture speaker characteristics. However, speeches containing emotional expressions pose significant challenges, often affecting the accuracy of speaker embeddings and leading to a decline in speaker clustering performance. To tackle this problem, we propose DTG-VAE, a novel disentanglement method that enhances clustering within a Variational Autoencoder (VAE) framework. This study reveals a direct link between emotional states and the effectiveness of deep speaker embeddings. As demonstrated in our experiments, DTG-VAE extracts more robust speaker embeddings and significantly enhances speaker clustering performance.