OpenRaiser/Intern-Atlas huggingface dataset:
Privacy-Preserving Behaviour of Chatbot Users: Steering Through Trust Dynamics
Venue: arXiv cs.HC 2025
Year: 2025

Introduction: The use of chatbots is becoming increasingly important across various aspects of daily life. However, the privacy concerns associated with these communications have not yet been thoroughly addressed. The aim of this study was to investigate user awareness of privacy risks in chatbot interactions, the privacy-preserving behaviours users practice, and how these behaviours relate to their awareness of privacy threats, even when no immediate threat is perceived. Methods: We developed a novel "privacy-safe" setup to analyse user behaviour under the guarantees of anonymization and non-sharing. We employed a mixed-methods approach, starting with the quantification of broader trends by coding responses, followed by conducting a qualitative content analysis to gain deeper insights. Results: Overall, there was a substantial lack of understanding among users about how chatbot providers handle data (27% of the participants) and the basics of privacy risks (76% of the participants). Older users, in particular, expressed fears that chatbot providers might sell their data. Moreover, even users with privacy knowledge do not consistently exhibit privacy-preserving behaviours when assured of transparent data processing by chatbots. Notably, under-protective behaviours were observed among more expert users. Discussion: These findings highlight the need for a strategic approach to enhance user education on privacy concepts to ensure informed decision when interacting with chatbot technology. This includes the development of tools to help users monitor and control the information they share with chatbots
Is Integer Linear Programming All You Need for Deletion Propagation? A Unified and Practical Approach for Generalized Deletion Propagation
Venue: arXiv cs.DB 2025
Year: 2025

Deletion Propagation (DP) refers to a family of database problems rooted in the classical view-update problem: how to propagate intended deletions in a view (query output) back to the source database while satisfying constraints and minimizing side effects. Although studied for over 40 years, DP variants, their complexities, and practical algorithms have been typically explored in isolation. This work presents a unified and generalized framework for DP with several key benefits: (1) It unifies and generalizes all previously known DP variants, effectively subsuming them within a broader class of problems, including new, well-motivated variants. (2) It comes with a practical and general-purpose algorithm that is ``coarse-grained instance-optimal'': it runs in PTIME for all known PTIME cases and can automatically exploit structural regularities in the data, i.e. it does not rely on hints about such regularities as part of the input. (3) It is complete: our framework handles all known DP variants in all settings (including those involving self-joins, unions, and bag semantics), and allows us to provide new complexity results. (4) It is easy to implement and, in many cases, outperforms prior variant-specific solutions, sometimes by orders of magnitude. We provide the first experimental results for several DP variants previously studied only in theory.
Learning Chemical Reaction Representation with Reactant-Product Alignment
Venue: arXiv cs.LG 2025
Year: 2025

Organic synthesis stands as a cornerstone of the chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or direct adaptations of model architectures from other domains, which lack feasibility as data scales increase or ignore the rich chemical information inherent in reactions. To address these issues, this paper introduces RAlign, a novel chemical reaction representation learning model for various organic reaction-related tasks. By integrating atomic correspondence between reactants and products, our model discerns the molecular transformations that occur during the reaction, thereby enhancing comprehension of the reaction mechanism. We have designed an adapter structure to incorporate reaction conditions into the chemical reaction representation, allowing the model to handle various reaction conditions and to adapt to various datasets and downstream tasks. Additionally, we introduce a reaction-center-aware attention mechanism that enables the model to concentrate on key functional groups, thereby generating potent representations for chemical reactions. Our model has been evaluated on a range of downstream tasks. Experimental results indicate that our model markedly outperforms existing chemical reaction representation learning architectures on most of the datasets. We plan to open-source the code contingent upon the acceptance of the paper.
MAS-Attention: Memory-Aware Stream Processing for Attention Acceleration on Resource-Constrained Edge Devices
Venue: arXiv cs.DC 2025
Year: 2025

The advent of foundation models have revolutionized various fields, enabling unprecedented task accuracy and flexibility in computational linguistics, computer vision and other domains. Attention mechanism has become an essential component of foundation models, due to their superb capability of capturing correlations in a sequence. However, attention results in quadratic complexity in memory and compute as the context length grows. Although many fusion-based exact attention acceleration algorithms have been developed for datacenter-grade GPUs and accelerators leveraging multi-core parallelism and data locality, yet it remains a significant challenge to accelerate attention on resource-constrained edge neural accelerators with limited compute units and stringent on-chip caches. In this paper, we propose a scheme for exact attention inference acceleration on memory-constrained edge accelerators, by parallelizing the utilization of heterogeneous compute units, i.e., vector processing units and matrix processing units. Our method involves scheduling workloads onto these different compute units in a multi-tiered tiling scheme to process tiled vector workloads and matrix workloads in attention as two streams, respecting the workload dependencies. We search for tiling factors to maximize the parallelization of both compute units while considering I/O overhead, and propose a proactive cache overwrite strategy to avoid undesirable cache spills in reality. Extensive results based on open-sourced simulation frameworks show up to 2.75x speedup and 54% reduction in energy consumption as compared to the state-of-the-art attention fusion method (FLAT) in the edge computing scenario. Further experiments on a real-world edge neural processing unit demonstrate speedup of up to 1.76x for attention as compared to FLAT, without affecting model output accuracy.
CheckMate: LLM-Powered Approximate Intermittent Computing
Venue: arXiv cs.DC 2025
Year: 2025

Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs for context-aware code approximations. CheckMate integrates validation of LLM-generated approximations to ensure correct execution and employs Bayesian optimization to fine-tune approximation parameters autonomously, eliminating the need for developer input. Tested across six IoT applications, it reduces power cycles by up to 60% with an accuracy loss of just 8%, outperforming semi-automated tools like ACCEPT in speedup and accuracy. CheckMate's results establish it as a robust, user-friendly tool and a foundational step toward automated approximation frameworks for intermittent computing.
Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning
Venue: arXiv cs.LG 2025
Year: 2025

Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly fine-tuning PTMs on incremental task streams often causes renewed catastrophic forgetting. This paper proposes a Dual-Prototype Network with Task-wise Adaptation (DPTA) for PTM-based CIL. For each incremental learning task, an adapter module is built to fine-tune the PTM, where the center-adapt loss forces the representation to be more centrally clustered and class separable. The dual prototype network improves the prediction process by enabling test-time adapter selection, where the raw prototypes deduce several possible task indexes of test samples to select suitable adapter modules for PTM, and the augmented prototypes that could separate confusable classes are utilized to determine the final result. Experiments on multiple benchmarks show that DPTA consistently surpasses recent methods by 1\% - 5\%. Notably, on the VTAB dataset, it achieves approximately 3\% improvement over state-of-the-art methods. The code is open-sourced in https://github.com/Yorkxzm/DPTA}
The importance of visual modelling languages in generative software engineering
Venue: arXiv cs.SE 2025
Year: 2025

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cases involving Software Engineering tasks carried out via multimodal GPTs prompted with a mix of diagrams and natural language.
GLS: Geometry-aware 3D Language Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Recently, 3D Gaussian Splatting (3DGS) has achieved impressive performance on indoor surface reconstruction and 3D open-vocabulary segmentation. This paper presents GLS, a unified framework of 3D surface reconstruction and open-vocabulary segmentation based on 3DGS. GLS extends two fields by improving their sharpness and smoothness. For indoor surface reconstruction, we introduce surface normal prior as a geometric cue to guide the rendered normal, and use the normal error to optimize the rendered depth. For 3D open-vocabulary segmentation, we employ 2D CLIP features to guide instance features and enhance the surface smoothness, then utilize DEVA masks to maintain their view consistency. Extensive experiments demonstrate the effectiveness of jointly optimizing surface reconstruction and 3D open-vocabulary segmentation, where GLS surpasses state-of-the-art approaches of each task on MuSHRoom, ScanNet++ and LERF-OVS datasets. Project webpage: https://jiaxiongq.github.io/GLS_ProjectPage.
Comprehensive Kernel Safety in the Spectre Era: Mitigations and Performance Evaluation (Extended Version)
Venue: arXiv cs.CR 2025
Year: 2025

The efficacy of address space layout randomization has been formally demonstrated in a shared-memory model by Abadi et al., contingent on specific assumptions about victim programs. However, modern operating systems, implementing layout randomization in the kernel, diverge from these assumptions and operate on a separate memory model with communication through system calls. In this work, we relax Abadi et al.'s language assumptions while demonstrating that layout randomization offers a comparable safety guarantee in a system with memory separation. However, in practice, speculative execution and side-channels are recognized threats to layout randomization. We show that kernel safety cannot be restored for attackers capable of using side-channels and speculative execution, and introduce enforcement mechanisms that can guarantee speculative kernel safety for safe system calls in the Spectre era. We implement three suitable mechanisms and we evaluate their performance overhead on the Linux kernel.
A finite presentation of graphs of treewidth at most three
Venue: arXiv cs.LO 2025
Year: 2025

We provide a finite equational presentation of graphs of treewidth at most three, solving an instanceof an open problem by Courcelle and Engelfriet. We use a syntax generalising series-parallel expressions, denoting graphs with a small interface. Weintroduce appropriate notions of connectivity for such graphs (components, cutvertices, separationpairs). We use those concepts to analyse the structure of graphs of treewidth at most three, showinghow they can be decomposed recursively, first canonically into connected parallel components, andthen non-deterministically. The main difficulty consists in showing that all non-deterministic choicescan be related using only finitely many equational axioms.
SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins
Venue: arXiv cs.LG 2025
Year: 2025

Path planning under wireless performance constraints is a complex challenge in robot navigation. However, naively incorporating such constraints into classical planning algorithms often incurs prohibitive search costs. In this paper, we propose SCoTT, a wireless-aware path planning framework that leverages vision-language models (VLMs) to co-optimize average path gains and trajectory length using wireless heatmap images and ray-tracing data from a digital twin (DT). At the core of our framework is Strategic Chain-of-Thought Tasking (SCoTT), a novel prompting paradigm that decomposes the exhaustive search problem into structured subtasks, each solved via chain-of-thought prompting. To establish strong baselines, we compare classical A* and wireless-aware extensions of it, and derive DP-WA*, an optimal, iterative dynamic programming algorithm that incorporates all path gains and distance metrics from the DT, but at significant computational cost. In extensive experiments, we show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories. Moreover, SCoTT's intermediate outputs can be used to accelerate DP-WA* by reducing its search space, saving up to 62% in execution time. We validate our framework using four VLMs, demonstrating effectiveness across both large and small models, thus making it applicable to a wide range of compact models at low inference cost. We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations. Finally, we discuss data acquisition pipelines, compute requirements, and deployment considerations for VLMs in 6G-enabled DTs, underscoring the potential of natural language interfaces for wireless-aware navigation in real-world applications.
Certified Training with Branch-and-Bound for Lyapunov-stable Neural Control
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of learning verifiably Lyapunov-stable neural controllers that provably satisfy the Lyapunov asymptotic stability condition within a region-of-attraction (ROA). Unlike previous works that adopted counterexample-guided training without considering the computation of verification in training, we introduce Certified Training with Branch-and-Bound (CT-BaB), a new certified training framework that optimizes certified bounds, thereby reducing the discrepancy between training and test-time verification that also computes certified bounds. To achieve a relatively global guarantee on an entire input region-of-interest, we propose a training-time BaB technique that maintains a dynamic training dataset and adaptively splits hard input subregions into smaller ones, to tighten certified bounds and ease the training. Meanwhile, subregions created by the training-time BaB also inform test-time verification, for a more efficient training-aware verification. We demonstrate that CT-BaB yields verification-friendly models that can be more efficiently verified at test time while achieving stronger verifiable guarantees with larger ROA. On the largest output-feedback 2D Quadrotor system experimented, CT-BaB reduces verification time by over 11X relative to the previous state-of-the-art baseline while achieving 164X larger ROA.
SEUF: Is Unlearning One Expert Enough for Mixture-of-Experts LLMs?
Venue: arXiv cs.LG 2025
Year: 2025

Recent advancements in LLMs unlearning have shown remarkable success in removing unwanted data-model influences while preserving the model's utility for legitimate knowledge. Despite these strides, sparse Mixture-of-Experts (MoE) LLMs--a key subset of the LLM family--have remained unexplored in the context of unlearning. As MoE LLMs are celebrated for their exceptional performance, we ask:How can unlearning be performed effectively and efficiently on MoE LLMs? Our pilot study shows that the dynamic routing nature of MoE LLMs introduces unique challenges, leading to excessive forgetting, uncontrolled knowledge erasure and substantial utility drops when existing unlearning methods are applied. To address this, we propose a novel Selected-Expert Unlearning Framework (SEUF). Through expert attribution, unlearning is concentrated on the most actively engaged experts for the specified knowledge. Concurrently, an anchor loss is applied to the router to stabilize the active state of this targeted expert, ensuring focused and controlled unlearning. SEUF is compatible with various standard unlearning algorithms. Extensive experiments demonstrate that SEUF enhances both forget quality up to 5% and model utility by 35% on MoE LLMs across various benchmarks and LLM architectures (compared to standard unlearning algorithms), while only unlearning 0.06% of the model parameters.
Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark
Venue: arXiv cs.CL 2025
Year: 2025

Systems that answer questions by reviewing the scientific literature are becoming increasingly feasible. To draw reliable conclusions, these systems should take into account the quality of available evidence from different studies, placing more weight on studies that use a valid methodology. We present a benchmark for measuring the methodological strength of biomedical papers, drawing on the risk-of-bias framework used for systematic reviews. Derived from over 500 biomedical studies, the three benchmark tasks encompass expert reviewers' judgments of studies' research methodologies, including the assessments of risk of bias within these studies. The benchmark contains a human-validated annotation pipeline for fine-grained alignment of reviewers' judgments with research paper sentences. Our analyses show that large language models' reasoning and retrieval capabilities impact their effectiveness with risk-of-bias assessment. The dataset is available at https://github.com/RoBBR-Benchmark/RoBBR.
An AI-Driven Multimodal Smart Home Platform for Continuous Monitoring and Assistance in Post-Stroke Motor Impairment
Venue: arXiv cs.HC 2025
Year: 2025

At-home rehabilitation for post-stroke patients presents significant challenges, as continuous, personalized care is often limited outside clinical settings. Moreover, the lack of integrated solutions capable of simultaneously monitoring motor recovery and providing intelligent assistance in home environments hampers rehabilitation outcomes. Here, we present a multimodal smart home platform designed for continuous, at-home rehabilitation of post-stroke patients, integrating wearable sensing, ambient monitoring, and adaptive automation. A plantar pressure insole equipped with a machine learning pipeline classifies users into motor recovery stages with up to 94\% accuracy, enabling quantitative tracking of walking patterns during daily activities. An optional head-mounted eye-tracking module, together with ambient sensors such as cameras and microphones, supports seamless hands-free control of household devices with a 100\% success rate and sub-second response time. These data streams are fused locally via a hierarchical Internet of Things (IoT) architecture, ensuring low latency and data privacy. An embedded large language model (LLM) agent, Auto-Care, continuously interprets multimodal data to provide real-time interventions -- issuing personalized reminders, adjusting environmental conditions, and notifying caregivers. Implemented in a post-stroke context, this integrated smart home platform increased mean user satisfaction from 3.9 $\pm$ 0.8 in conventional home environments to 8.4 $\pm$ 0.6 with the full system ($n=20$). Beyond stroke, the system offers a scalable, patient-centered framework with potential for long-term use in broader neurorehabilitation and aging-in-place applications.
DIESEL -- Dynamic Inference-Guidance via Evasion of Semantic Embeddings in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

In recent years, large language models (LLMs) have had great success in tasks such as casual conversation, contributing to significant advancements in domains like virtual assistance. However, they often generate responses that are not aligned with human values (e.g., ethical standards, safety), leading to potentially unsafe or inappropriate outputs. While several techniques have been proposed to address this problem, they come with a cost, requiring computationally expensive training or dramatically increasing the inference time. In this paper, we present DIESEL, a lightweight inference-guidance technique that can be seamlessly integrated into any autoregressive LLM to semantically filter undesired concepts from the response. DIESEL can function either as a standalone safeguard or as an additional layer of defense, enhancing response safety by reranking the LLM's proposed tokens based on their similarity to predefined negative concepts in the latent space. Our evaluation demonstrates DIESEL's effectiveness on state-of-the-art conversational models, even in adversarial jailbreaking scenarios that challenge response safety. We also highlight DIESEL's generalization capabilities, showing that it can be used in use cases other than safety, providing general-purpose response filtering.
Pralekha: Cross-Lingual Document Alignment for Indic Languages
Venue: arXiv cs.CL 2025
Year: 2025

Mining parallel document pairs for document-level machine translation (MT) remains challenging due to the limitations of existing Cross-Lingual Document Alignment (CLDA) techniques. Existing methods often rely on metadata such as URLs, which are scarce, or on pooled document representations that fail to capture fine-grained alignment cues. Moreover, the limited context window of sentence embedding models hinders their ability to represent document-level context, while sentence-based alignment introduces a combinatorially large search space, leading to high computational cost. To address these challenges for Indic languages, we introduce Pralekha, a benchmark containing over 3 million aligned document pairs across 11 Indic languages and English, which includes 1.5 million English-Indic pairs. Furthermore, we propose Document Alignment Coefficient (DAC), a novel metric for fine-grained document alignment. Unlike pooling-based methods, DAC aligns documents by matching smaller chunks and computes similarity as the ratio of aligned chunks to the average number of chunks in a pair. Intrinsic evaluation shows that our chunk-based method is 2-3x faster while maintaining competitive performance, and that DAC achieves substantial gains over pooling-based baselines. Extrinsic evaluation further demonstrates that document-level MT models trained on DAC-aligned pairs consistently outperform those using baseline alignment methods. These results highlight DAC's effectiveness for parallel document mining. The dataset and evaluation framework are publicly available to support further research.