OpenRaiser/Intern-Atlas huggingface dataset:
Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) have demonstrated impressive capabilities across diverse tasks, yet their ability to perform structured symbolic planning remains limited, particularly in domains requiring formal representations like the Planning Domain Definition Language (PDDL). In this paper, we present a novel instruction tuning framework, PDDL-Instruct, designed to enhance LLMs' symbolic planning capabilities through logical chain-of-thought reasoning. Our approach focuses on teaching models to rigorously reason about action applicability, state transitions, and plan validity using explicit logical inference steps. By developing instruction prompts that guide models through the precise logical reasoning required to determine when actions can be applied in a given state, we enable LLMs to self-correct their planning processes through structured reflection. The framework systematically builds verification skills by decomposing the planning process into explicit reasoning chains about precondition satisfaction, effect application, and invariant preservation. Experimental results on multiple planning domains show that our chain-of-thought reasoning based instruction-tuned models are significantly better at planning, achieving planning accuracy of up to 94% on standard benchmarks, representing a 66% absolute improvement over baseline models. This work bridges the gap between the general reasoning capabilities of LLMs and the logical precision required for automated planning, offering a promising direction for developing better AI planning systems.
Hybrid Quantum-Classical Model for Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with $ε=0.1$ perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\%, 32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%) and STL10 (+10.29\%). Hybrid models also train 5--12$\times$ faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but show comparable fragility on complex datasets like CIFAR100 ($\sim$1\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\% vs. 23.2\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.
Evaluating undergraduate mathematics examinations in the era of generative AI: a curriculum-level case study
Venue: arXiv cs.CY 2025
Year: 2025

Generative artificial intelligence (GenAI) tools such as OpenAI's ChatGPT are transforming the educational landscape, prompting reconsideration of traditional assessment practices. In parallel, universities are exploring alternatives to in-person, closed-book examinations, raising concerns about academic integrity and pedagogical alignment in uninvigilated settings. This study investigates whether traditional closed-book mathematics examinations retain their pedagogical relevance when hypothetically administered in uninvigilated, open-book settings with GenAI access. Adopting an empirical approach, we generate, transcribe, and blind-mark GenAI submissions to eight undergraduate mathematics examinations at a Russell Group university, spanning the entirety of the first-year curriculum. By combining independent GenAI responses to individual questions, we enable a meaningful evaluation of GenAI performance, both at the level of modules and across the first-year curriculum. We find that GenAI attainment is at the level of a first-class degree, though current performance can vary between modules. Further, we find that GenAI performance is remarkably consistent when viewed across the entire curriculum, significantly more so than that of students in invigilated examinations. Our findings evidence the need for redesigning assessments in mathematics for unsupervised settings, and highlight the potential reduction in pedagogical value of current standards in the era of generative artificial intelligence.
An Empirical Analysis of VLM-based OOD Detection: Mechanisms, Advantages, and Sensitivity
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs), such as CLIP, have demonstrated remarkable zero-shot out-of-distribution (OOD) detection capabilities, vital for reliable AI systems. Despite this promising capability, a comprehensive understanding of (1) why they work so effectively, (2) what advantages do they have over single-modal methods, and (3) how is their behavioral robustness -- remains notably incomplete within the research community. This paper presents a systematic empirical analysis of VLM-based OOD detection using in-distribution (ID) and OOD prompts. (1) Mechanisms: We systematically characterize and formalize key operational properties within the VLM embedding space that facilitate zero-shot OOD detection. (2) Advantages: We empirically quantify the superiority of these models over established single-modal approaches, attributing this distinct advantage to the VLM's capacity to leverage rich semantic novelty. (3) Sensitivity: We uncovers a significant and previously under-explored asymmetry in their robustness profile: while exhibiting resilience to common image noise, these VLM-based methods are highly sensitive to prompt phrasing. Our findings contribute a more structured understanding of the strengths and critical vulnerabilities inherent in VLM-based OOD detection, offering crucial, empirically-grounded guidance for developing more robust and reliable future designs.
Using role-play and Hierarchical Task Analysis for designing human-robot interaction
Venue: arXiv cs.RO 2025
Year: 2025

We present the use of two methods we believe warrant more use than they currently have in the field of human-robot interaction: role-play and Hierarchical Task Analysis. Some of its potential is showcased through our use of them in an ongoing research project which entails developing a robot application meant to assist at a community pharmacy. The two methods have provided us with several advantages. The role-playing provided a controlled and adjustable environment for understanding the customers' needs where pharmacists could act as models for the robot's behavior; and the Hierarchical Task Analysis ensured the behavior displayed was modelled correctly and aided development through facilitating co-design. Future research could focus on developing task analysis methods especially suited for social robot interaction.
Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji
Venue: arXiv cs.CV 2025
Year: 2025

As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing frameworks to compare land use and land cover change from 2013 to 2024 in Nadi, Fiji. The ultimate goal of this study is to provide technical support in land cover/land use modelling and change detection. We used Landsat-8 satellite image for the study region and created our training dataset with labels for supervised machine learning. We used Google Earth Engine and unsupervised machine learning via k-means clustering to generate the land cover map. We used convolutional neural networks to classify the selected regions' land cover types. We present a visualisation of change detection, highlighting urban area changes over time to monitor changes in the map.
The Intercepted Self: How Generative AI Challenges the Dynamics of the Relational Self
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI is changing our way of interacting with technology, others, and ourselves. Systems such as Microsoft copilot, Gemini and the expected Apple intelligence still awaits our prompt for action. Yet, it is likely that AI assistant systems will only become better at predicting our behaviour and acting on our behalf. Imagine new generations of generative and predictive AI deciding what you might like best at a new restaurant, picking an outfit that increases your chances on your date with a partner also chosen by the same or a similar system. Far from a science fiction scenario, the goal of several research programs is to build systems capable of assisting us in exactly this manner. The prospect urges us to rethink human-technology relations, but it also invites us to question how such systems might change the way we relate to ourselves. Building on our conception of the relational self, we question the possible effects of generative AI with respect to what we call the sphere of externalised output, the contextual sphere and the sphere of self-relating. In this paper, we attempt to deepen the existential considerations accompanying the AI revolution by outlining how generative AI enables the fulfilment of tasks and also increasingly anticipates, i.e. intercepts, our initiatives in these different spheres.
Efficient STAR-RIS Mode for Energy Minimization in WPT-FL Networks with NOMA
Venue: arXiv cs.IT 2025
Year: 2025

With the massive deployment of IoT devices in 6G networks, several critical challenges have emerged, such as large communication overhead, coverage limitations, and limited battery lifespan. FL, WPT, multi-antenna AP, and RIS can mitigate these challenges by reducing the need for large data transmissions, enabling sustainable energy harvesting, and optimizing the propagation environment. Compared to conventional RIS, STAR-RIS not only extends coverage from half-space to full-space but also improves energy saving through appropriate mode selection. Motivated by the need for sustainable, low-latency, and energy-efficient communication in large-scale IoT networks, this paper investigates the efficient STAR-RIS mode in the uplink and downlink phases of a WPT-FL multi-antenna AP network with non-orthogonal multiple access to minimize energy consumption, a joint optimization that remains largely unexplored in existing works on RIS or STAR-RIS. We formulate a non-convex energy minimization problem for different STAR-RIS modes, i.e., energy splitting (ES) and time switching (TS), in both uplink and downlink transmission phases, where STAR-RIS phase shift vectors, beamforming matrices, time and power for harvesting, uplink transmission, and downlink transmission, local processing time, and computation frequency for each user are jointly optimized. To tackle the non-convexity, the problem is decoupled into two subproblems: the first subproblem optimizes STAR-RIS phase shift vectors and beamforming matrices across all WPT-FL phases using block coordinate descent over either semi-definite programming or Rayleigh quotient problems, while the second one allocates time, power, and computation frequency via the one-dimensional search algorithms or the bisection algorithm.
Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
Venue: arXiv cs.CV 2025
Year: 2025

3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates massive amounts of data. Hence compressing 3DGS data is necessary for the cost effectiveness of 3DGS models. Recently, several anchor-based neural compression methods have been proposed, achieving good 3DGS compression performance. However, they all rely on uniform scalar quantization (USQ) due to its simplicity. A tantalizing question is whether more sophisticated quantizers can improve the current 3DGS compression methods with very little extra overhead and minimal change to the system. The answer is yes by replacing USQ with lattice vector quantization (LVQ). To better capture scene-specific characteristics, we optimize the lattice basis for each scene, improving LVQ's adaptability and R-D efficiency. This scene-adaptive LVQ (SALVQ) strikes a balance between the R-D efficiency of vector quantization and the low complexity of USQ. SALVQ can be seamlessly integrated into existing 3DGS compression architectures, enhancing their R-D performance with minimal modifications and computational overhead. Moreover, by scaling the lattice basis vectors, SALVQ can dynamically adjust lattice density, enabling a single model to accommodate multiple bit rate targets. This flexibility eliminates the need to train separate models for different compression levels, significantly reducing training time and memory consumption.
GRU-Based Learning for the Identification of Congestion Protocols in TCP Traffic
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents the identification of congestion control protocols TCP Reno, TCP Cubic, TCP Vegas, and BBR on the Marist University campus, with an accuracy of 97.04% using a GRU-based learning model. We used a faster neural network architecture on a more complex and competitive network in comparison to existing work and achieved comparably high accuracy.
Odin: Effective End-to-End SLA Decomposition for 5G/6G Network Slicing via Online Learning
Venue: arXiv cs.NI 2025
Year: 2025

Network slicing plays a crucial role in realizing 5G/6G advances, enabling diverse Service Level Agreement (SLA) requirements related to latency, throughput, and reliability. Since network slices are deployed end-to-end (E2E), across multiple domains including access, transport, and core networks, it is essential to efficiently decompose an E2E SLA into domain-level targets, so that each domain can provision adequate resources for the slice. However, decomposing SLAs is highly challenging due to the heterogeneity of domains, dynamic network conditions, and the fact that the SLA orchestrator is oblivious to the domain's resource optimization. In this work, we propose Odin, a Bayesian Optimization-based solution that leverages each domain's online feedback for provably-efficient SLA decomposition. Through theoretical analyses and rigorous evaluations, we demonstrate that Odin's E2E orchestrator can achieve up to 45% performance improvement in SLA satisfaction when compared with baseline solutions whilst reducing overall resource costs even in the presence of noisy feedback from the individual domains.
Py maidr: Bridging Visual and Non-Visual Data Experiences Through a Unified Python Framework
Venue: arXiv cs.HC 2025
Year: 2025

Although recent efforts have developed accessible data visualization tools for blind and low-vision (BLV) users, most follow a "design for them" approach that creates an unintentional divide between sighted creators and BLV consumers. This unidirectional paradigm perpetuates a power dynamic where sighted creators produce non-visual content boundaries for BLV consumers to access. This paper proposes a bidirectional approach, "design for us," where both sighted and BLV collaborators can employ the same tool to create, interpret, and communicate data visualizations for each other. We introduce Py maidr, a Python package that seamlessly encodes multimodal (e.g., tactile, auditory, conversational) data representations into visual plots generated by Matplotlib and Seaborn. By simply importing the maidr package and invoking the maidr.show() method, users can generate accessible plots with minimal changes to their existing codebase regardless of their visual dis/abilities. Our technical case studies demonstrate how this tool is scalable and can be integrated into interactive computing (e.g., Jupyter Notebook, Google Colab), reproducible and literate programming (e.g., Quarto), and reactive dashboards (e.g., Shiny, Streamlit). Our performance benchmarks demonstrate that Py maidr introduces minimal and consistent overhead during the rendering and export of plots against Matplotlib and Seaborn baselines. This work significantly contributes to narrowing the accessibility gap in data visualization by providing a unified framework that fosters collaboration and communication between sighted and BLV individuals.
Oscillator Formulations of Many NP Problems
Venue: arXiv cs.ET 2025
Year: 2025

Efficiently optimizing Nondeterministic Polynomial time (NP) problems in polynomial time has profound implications in many domains. CMOS oscillator networks have been shown to be effective and efficient in approximating certain NP-hard problems such as minimization of Potts Hamiltonian, and computational complexity theory guarantees that any NP problem can be reduced to it. In this paper, we formulate a variety of NP problems using first-order and multi-phase Potts Hamiltonian. We also propose a 3-state asymmetrically weighted oscillator optimizer design to optimize the problems. Building on existing knowledge in CMOS design, our proposed algorithms offer a promising pathway for large-scale optimization of NP problems.
Using Visual Language Models to Control Bionic Hands: Assessment of Object Perception and Grasp Inference
Venue: arXiv cs.RO 2025
Year: 2025

This study examines the potential of utilizing Vision Language Models (VLMs) to improve the perceptual capabilities of semi-autonomous prosthetic hands. We introduce a unified benchmark for end-to-end perception and grasp inference, evaluating a single VLM to perform tasks that traditionally require complex pipelines with separate modules for object detection, pose estimation, and grasp planning. To establish the feasibility and current limitations of this approach, we benchmark eight contemporary VLMs on their ability to perform a unified task essential for bionic grasping. From a single static image, they should (1) identify common objects and their key properties (name, shape, orientation, and dimensions), and (2) infer appropriate grasp parameters (grasp type, wrist rotation, hand aperture, and number of fingers). A corresponding prompt requesting a structured JSON output was employed with a dataset of 34 snapshots of common objects. Key performance metrics, including accuracy for categorical attributes (e.g., object name, shape) and errors in numerical estimates (e.g., dimensions, hand aperture), along with latency and cost, were analyzed. The results demonstrated that most models exhibited high performance in object identification and shape recognition, while accuracy in estimating dimensions and inferring optimal grasp parameters, particularly hand rotation and aperture, varied more significantly. This work highlights the current capabilities and limitations of VLMs as advanced perceptual modules for semi-autonomous control of bionic limbs, demonstrating their potential for effective prosthetic applications.
Object Pose Estimation through Dexterous Touch
Venue: arXiv cs.RO 2025
Year: 2025

Robust object pose estimation is essential for manipulation and interaction tasks in robotics, particularly in scenarios where visual data is limited or sensitive to lighting, occlusions, and appearances. Tactile sensors often offer limited and local contact information, making it challenging to reconstruct the pose from partial data. Our approach uses sensorimotor exploration to actively control a robot hand to interact with the object. We train with Reinforcement Learning (RL) to explore and collect tactile data. The collected 3D point clouds are used to iteratively refine the object's shape and pose. In our setup, one hand holds the object steady while the other performs active exploration. We show that our method can actively explore an object's surface to identify critical pose features without prior knowledge of the object's geometry. Supplementary material and more demonstrations will be provided at https://amirshahid.github.io/BimanualTactilePose .
Re-purposing SAM into Efficient Visual Projectors for MLLM-Based Referring Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Recently, Referring Image Segmentation (RIS) frameworks that pair the Multimodal Large Language Model (MLLM) with the Segment Anything Model (SAM) have achieved impressive results. However, adapting MLLM to segmentation is computationally intensive, primarily due to visual token redundancy. We observe that traditional patch-wise visual projectors struggle to strike a balance between reducing the number of visual tokens and preserving semantic clarity, often retaining overly long token sequences to avoid performance drops. Inspired by text tokenizers, we propose a novel semantic visual projector that leverages semantic superpixels generated by SAM to identify "visual words" in an image. By compressing and projecting semantic superpixels as visual tokens, our approach adaptively shortens the token sequence according to scene complexity while minimizing semantic loss in compression. To mitigate loss of information, we propose a semantic superpixel positional embedding to strengthen MLLM's awareness of superpixel geometry and position, alongside a semantic superpixel aggregator to preserve both fine-grained details inside superpixels and global context outside. Experiments show that our method cuts visual tokens by 93% without compromising performance, notably speeding up MLLM training and inference, and outperforming existing compressive visual projectors on RIS.
Prompt Stability in Code LLMs: Measuring Sensitivity across Emotion- and Personality-Driven Variations
Venue: arXiv cs.SE 2025
Year: 2025

Code generation models are widely used in software development, yet their sensitivity to prompt phrasing remains under-examined. Identical requirements expressed with different emotions or communication styles can yield divergent outputs, while most benchmarks emphasize only peak performance. We present PromptSE (Prompt Sensitivity Evaluation), a framework that creates semantically equivalent prompt variants with emotion and personality templates, and that evaluates stability using probability aware continuous scoring or using binary pass rates when logits are unavailable. The results are aggregated into a proposed area under curve metric (AUC-E) for cross model comparison. Across 14 models from three families (Llama, Qwen, and DeepSeek), our study shows that performance and stability behave as largely decoupled optimization objectives, and it reveals architectural and scale related patterns that challenge common assumptions about model robustness. The framework supports rapid screening for closed-source models as well as detailed stability analysis in research settings. PromptSE enables practitioners to quantify performance stability trade offs for deployment and model selection, positioning prompt stability as a complementary evaluation dimension alongside performance and fairness, and contributing to more trustworthy AI-assisted software development tools.