OpenRaiser/Intern-Atlas huggingface dataset:
Robust Nearest Neighbour Retrieval Using Targeted Manifold Manipulation
Venue: arXiv cs.CV 2025
Year: 2025

Nearest-neighbour retrieval is central to classification and explainable-AI pipelines, but current practice relies on hand-tuning feature layers and distance metrics. We propose Targeted Manifold Manipulation-Nearest Neighbour (TMM-NN), which reconceptualises retrieval by assessing how readily each sample can be nudged into a designated region of the feature manifold; neighbourhoods are defined by a sample's responsiveness to a targeted perturbation rather than absolute geometric distance. TMM-NN implements this through a lightweight, query-specific trigger patch. The patch is added to the query image, and the network is weakly ``backdoored'' so that any input with the patch is steered toward a dummy class. Images similar to the query need only a slight shift and are classified as the dummy class with high probability, while dissimilar ones are less affected. By ranking candidates by this confidence, TMM-NN retrieves the most semantically related neighbours. Robustness analysis and benchmark experiments confirm this trigger-based ranking outperforms traditional metrics under noise and across diverse tasks.
GAIA: A General Agency Interaction Architecture for LLM-Human B2B Negotiation & Screening
Venue: arXiv cs.AI 2025
Year: 2025

Organizations are increasingly exploring delegation of screening and negotiation tasks to AI systems, yet deployment in high-stakes B2B settings is constrained by governance: preventing unauthorized commitments, ensuring sufficient information before bargaining, and maintaining effective human oversight and auditability. Prior work on large language model negotiation largely emphasizes autonomous bargaining between agents and omits practical needs such as staged information gathering, explicit authorization boundaries, and systematic feedback integration. We propose GAIA, a governance-first framework for LLM-human agency in B2B negotiation and screening. GAIA defines three essential roles - Principal (human), Delegate (LLM agent), and Counterparty - with an optional Critic to enhance performance, and organizes interactions through three mechanisms: information-gated progression that separates screening from negotiation; dual feedback integration that combines AI critique with lightweight human corrections; and authorization boundaries with explicit escalation paths. Our contributions are fourfold: (1) a formal governance framework with three coordinated mechanisms and four safety invariants for delegation with bounded authorization; (2) information-gated progression via task-completeness tracking (TCI) and explicit state transitions that separate screening from commitment; (3) dual feedback integration that blends Critic suggestions with human oversight through parallel learning channels; and (4) a hybrid validation blueprint that combines automated protocol metrics with human judgment of outcomes and safety. By bridging theory and practice, GAIA offers a reproducible specification for safe, efficient, and accountable AI delegation that can be instantiated across procurement, real estate, and staffing workflows.
Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents
Venue: arXiv cs.AI 2025
Year: 2025

Financial documents like earning reports or balance sheets often involve long tables and multi-page reports. Large language models have become a new tool to help numerical reasoning and understanding these documents. However, prompt quality can have a major effect on how well LLMs perform these financial reasoning tasks. Most current methods tune prompts on fixed datasets of financial text or tabular data, which limits their ability to adapt to new question types or document structures, or they involve costly and manually labeled/curated dataset to help build the prompts. We introduce a self-improving prompt framework driven by data-augmented optimization. In this closed-loop process, we generate synthetic financial tables and document excerpts, verify their correctness and robustness, and then update the prompt based on the results. Specifically, our framework combines a synthetic data generator with verifiers and a prompt optimizer, where the generator produces new examples that exposes weaknesses in the current prompt, the verifiers check the validity and robustness of the produced examples, and the optimizer incrementally refines the prompt in response. By iterating these steps in a feedback cycle, our method steadily improves prompt accuracy on financial reasoning tasks without needing external labels. Evaluation on DocMath-Eval benchmark demonstrates that our system achieves higher performance in both accuracy and robustness than standard prompt methods, underscoring the value of incorporating synthetic data generation into prompt learning for financial applications.
MT-HuBERT: Self-Supervised Mix-Training for Few-Shot Keyword Spotting in Mixed Speech
Venue: arXiv cs.SD 2025
Year: 2025

Few-shot keyword spotting aims to detect previously unseen keywords with very limited labeled samples. A pre-training and adaptation paradigm is typically adopted for this task. While effective in clean conditions, most existing approaches struggle with mixed keyword spotting--detecting multiple overlapping keywords within a single utterance--a capability essential for real-world applications. We have previously proposed a pre-training approach based on Mix-Training (MT) to tackle the mixed keyword detection problem and demonstrated its efficiency. However, this approach is fully supervised, unable to utilize vast unlabeled data. To this end, we propose Mix-Training HuBERT (MT-HuBERT), a self-supervised learning (SSL) pre-training framework that implements the MT criterion during pre-training. MT-HuBERT predicts, in a self-supervised manner, the clean acoustic units of each constituent signal from contextual cues, in contrast to predicting compositional patterns of mixed speech. Experiments conducted on the Google Speech Commands (GSC v2) corpus demonstrate that our proposed MT-HuBERT consistently outperforms several state-of-the-art baselines in few-shot KWS tasks under both mixed and clean conditions.
Decomate: Leveraging Generative Models for Co-Creative SVG Animation
Venue: arXiv cs.HC 2025
Year: 2025

Designers often encounter friction when animating static SVG graphics, especially when the visual structure does not match the desired level of motion detail. Existing tools typically depend on predefined groupings or require technical expertise, which limits designers' ability to experiment and iterate independently. We present Decomate, a system that enables intuitive SVG animation through natural language. Decomate leverages a multimodal large language model to restructure raw SVGs into semantically meaningful, animation-ready components. Designers can then specify motions for each component via text prompts, after which the system generates corresponding HTML/CSS/JS animations. By supporting iterative refinement through natural language interaction, Decomate integrates generative AI into creative workflows, allowing animation outcomes to be directly shaped by user intent.
External Photoreflective Tactile Sensing Based on Surface Deformation Measurement
Venue: arXiv cs.RO 2025
Year: 2025

We present a tactile sensing method enabled by the mechanical compliance of soft robots; an externally attachable photoreflective module reads surface deformation of silicone skin to estimate contact force without embedding tactile transducers. Locating the sensor off the contact interface reduces damage risk, preserves softness, and simplifies fabrication and maintenance. We first characterize the optical sensing element and the compliant skin, thendetermine the design of a prototype tactile sensor. Compression experiments validate the approach, exhibiting a monotonic force output relationship consistent with theory, low hysteresis, high repeatability over repeated cycles, and small response indentation speeds. We further demonstrate integration on a soft robotic gripper, where the module reliably detects grasp events. Compared with liquid filled or wireembedded tactile skins, the proposed modular add on architecture enhances durability, reduces wiring complexity, and supports straightforward deployment across diverse robot geometries. Because the sensing principle reads skin strain patterns, it also suggests extensions to other somatosensory cues such as joint angle or actuator state estimation from surface deformation. Overall, leveraging surface compliance with an external optical module provides a practical and robust route to equip soft robots with force perception while preserving structural flexibility and manufacturability, paving the way for robotic applications and safe human robot collaboration.
Precision-Scalable Microscaling Datapaths with Optimized Reduction Tree for Efficient NPU Integration
Venue: arXiv cs.AR 2025
Year: 2025

Emerging continual learning applications necessitate next-generation neural processing unit (NPU) platforms to support both training and inference operations. The promising Microscaling (MX) standard enables narrow bit-widths for inference and large dynamic ranges for training. However, existing MX multiply-accumulate (MAC) designs face a critical trade-off: integer accumulation requires expensive conversions from narrow floating-point products, while FP32 accumulation suffers from quantization losses and costly normalization. To address these limitations, we propose a hybrid precision-scalable reduction tree for MX MACs that combines the benefits of both approaches, enabling efficient mixed-precision accumulation with controlled accuracy relaxation. Moreover, we integrate an 8x8 array of these MACs into the state-of-the-art (SotA) NPU integration platform, SNAX, to provide efficient control and data transfer to our optimized precision-scalable MX datapath. We evaluate our design both on MAC and system level and compare it to the SotA. Our integrated system achieves an energy efficiency of 657, 1438-1675, and 4065 GOPS/W, respectively, for MXINT8, MXFP8/6, and MXFP4, with a throughput of 64, 256, and 512 GOPS.
Enhancing Deep Learning-Based Rotational-XOR Attacks on Lightweight Block Ciphers Simon32/64 and Simeck32/64
Venue: arXiv cs.CR 2025
Year: 2025

At CRYPTO 2019, Gohr pioneered neural cryptanalysis by introducing differential-based neural distinguishers to attack Speck32/64, establishing a novel paradigm combining deep learning with differential cryptanalysis.Since then, constructing neural distinguishers has become a significant approach to achieving the deep learning-based cryptanalysis for block ciphers.This paper advances rotational-XOR (RX) attacks through neural networks, focusing on optimizing distinguishers and presenting key-recovery attacks for the lightweight block ciphers Simon32/64 and Simeck32/64.In particular, we first construct the fundamental data formats specially designed for training RX-neural distinguishers by refining the existing data formats for differential-neural distinguishers. Based on these data formats, we systematically identify optimal RX-differences with Hamming weights 1 and 2 that develop high-accuracy RX-neural distinguishers. Then, through innovative application of the bit sensitivity test, we achieve significant compression of data format without sacrificing the distinguisher accuracy. This optimization enables us to add more multi-ciphertext pairs into the data formats, further strengthening the performance of RX-neural distinguishers. As an application, we obtain 14- and 17-round RX-neural distinguishers for Simon32/64 and Simeck32/64, which improves the previous ones by 3 and 2 rounds, respectively.In addition, we propose two novel techniques, key bit sensitivity test and the joint wrong key response, to tackle the challenge of applying Bayesian's key-recovery strategy to the target cipher that adopts nonlinear key schedule in the related-key setting without considering of weak-key space. By this, we can straightforwardly mount a 17-round key-recovery attack on Simeck32/64 based on the improved 16-round RX-nerual distinguisher. To the best of our knowledge, the presented RX-neural......
Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization
Venue: arXiv cs.LG 2025
Year: 2025

The optimization of urban traffic is threatened by the complexity of achieving a balance between transport efficiency and the maintenance of privacy, as well as the equitable distribution of traffic based on socioeconomically diverse neighborhoods. Current centralized traffic management schemes invade user location privacy and further entrench traffic disparity by offering disadvantaged route suggestions, whereas current federated learning frameworks do not consider fairness constraints in multi-objective traffic settings. This study presents a privacy-preserving federated learning framework, termed FedFair-Traffic, that jointly and simultaneously optimizes travel efficiency, traffic fairness, and differential privacy protection. This is the first attempt to integrate three conflicting objectives to improve urban transportation systems. The proposed methodology enables collaborative learning between related vehicles with data locality by integrating Graph Neural Networks with differential privacy mechanisms ($ε$-privacy guarantees) and Gini coefficient-based fair constraints using multi-objective optimization. The framework uses federated aggregation methods of gradient clipping and noise injection to provide differential privacy and optimize Pareto-efficient solutions for the efficiency-fairness tradeoff. Real-world comprehensive experiments on the METR-LA traffic dataset showed that FedFair-Traffic can reduce the average travel time by 7\% (14.2 minutes) compared with their centralized baselines, promote traffic fairness by 73\% (Gini coefficient, 0.78), and offer high privacy protection (privacy score, 0.8) with an 89\% reduction in communication overhead. These outcomes demonstrate that FedFair-Traffic is a scalable privacy-aware smart city infrastructure with possible use-cases in metropolitan traffic flow control and federated transportation networks.
ArtReg: Visuo-Tactile based Pose Tracking and Manipulation of Unseen Articulated Objects
Venue: arXiv cs.RO 2025
Year: 2025

Robots operating in real-world environments frequently encounter unknown objects with complex structures and articulated components, such as doors, drawers, cabinets, and tools. The ability to perceive, track, and manipulate these objects without prior knowledge of their geometry or kinematic properties remains a fundamental challenge in robotics. In this work, we present a novel method for visuo-tactile-based tracking of unseen objects (single, multiple, or articulated) during robotic interaction without assuming any prior knowledge regarding object shape or dynamics. Our novel pose tracking approach termed ArtReg (stands for Articulated Registration) integrates visuo-tactile point clouds in an unscented Kalman Filter formulation in the SE(3) Lie Group for point cloud registration. ArtReg is used to detect possible articulated joints in objects using purposeful manipulation maneuvers such as pushing or hold-pulling with a two-robot team. Furthermore, we leverage ArtReg to develop a closed-loop controller for goal-driven manipulation of articulated objects to move the object into the desired pose configuration. We have extensively evaluated our approach on various types of unknown objects through real robot experiments. We also demonstrate the robustness of our method by evaluating objects with varying center of mass, low-light conditions, and with challenging visual backgrounds. Furthermore, we benchmarked our approach on a standard dataset of articulated objects and demonstrated improved performance in terms of pose accuracy compared to state-of-the-art methods. Our experiments indicate that robust and accurate pose tracking leveraging visuo-tactile information enables robots to perceive and interact with unseen complex articulated objects (with revolute or prismatic joints).
Whole-Body Control With Terrain Estimation of A 6-DoF Wheeled Bipedal Robot
Venue: arXiv cs.RO 2025
Year: 2025

Wheeled bipedal robots have garnered increasing attention in exploration and inspection. However, most research simplifies calculations by ignoring leg dynamics, thereby restricting the robot's full motion potential. Additionally, robots face challenges when traversing uneven terrain. To address the aforementioned issue, we develop a complete dynamics model and design a whole-body control framework with terrain estimation for a novel 6 degrees of freedom wheeled bipedal robot. This model incorporates the closed-loop dynamics of the robot and a ground contact model based on the estimated ground normal vector. We use a LiDAR inertial odometry framework and improved Principal Component Analysis for terrain estimation. Task controllers, including PD control law and LQR, are employed for pose control and centroidal dynamics-based balance control, respectively. Furthermore, a hierarchical optimization approach is used to solve the whole-body control problem. We validate the performance of the terrain estimation algorithm and demonstrate the algorithm's robustness and ability to traverse uneven terrain through both simulation and real-world experiments.
Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
Venue: arXiv cs.SE 2025
Year: 2025

Background: Large Language Models emerged with the potential of provoking a revolution in software development (e.g., automating processes, workforce transformation). Although studies have started to investigate the perceived impact of LLMs for software development, there is a need for empirical studies to comprehend how to balance forward and backward effects of using LLMs. Objective: We investigated how LLMs impact software development and how to manage the impact from a software developer's perspective. Method: We conducted 22 interviews with software practitioners across 3 rounds of data collection and analysis, between October (2024) and September (2025). We employed socio-technical grounded theory (STGT) for data analysis to rigorously analyse interview participants' responses. Results: We identified the benefits (e.g., maintain software development flow, improve developers' mental model, and foster entrepreneurship) and disadvantages (e.g., negative impact on developers' personality and damage to developers' reputation) of using LLMs at individual, team, organisation, and society levels; as well as best practices on how to adopt LLMs. Conclusion: Critically, we present the trade-offs that software practitioners, teams, and organisations face in working with LLMs. Our findings are particularly useful for software team leaders and IT managers to assess the viability of LLMs within their specific context.
SR-KI: Scalable and Real-Time Knowledge Integration into LLMs via Supervised Attention
Venue: arXiv cs.CL 2025
Year: 2025

This paper proposes SR-KI, a novel approach for integrating real-time and large-scale structured knowledge bases (KBs) into large language models (LLMs). SR-KI begins by encoding KBs into key-value pairs using a pretrained encoder, and injects them into LLMs' KV cache. Building on this representation, we employ a two-stage training paradigm: first locating a dedicated retrieval layer within the LLM, and then applying an attention-based loss at this layer to explicitly supervise attention toward relevant KB entries. Unlike traditional retrieval-augmented generation methods that rely heavily on the performance of external retrievers and multi-stage pipelines, SR-KI supports end-to-end inference by performing retrieval entirely within the models latent space. This design enables efficient compression of injected knowledge and facilitates dynamic knowledge updates. Comprehensive experiments demonstrate that SR-KI enables the integration of up to 40K KBs into a 7B LLM on a single A100 40GB GPU, and achieves strong retrieval performance, maintaining over 98% Recall@10 on the best-performing task and exceeding 88% on average across all tasks. Task performance on question answering and KB ID generation also demonstrates that SR-KI maintains strong performance while achieving up to 99.75% compression of the injected KBs.
MULTIBENCH++: A Unified and Comprehensive Multimodal Fusion Benchmarking Across Specialized Domains
Venue: arXiv cs.LG 2025
Year: 2025

Although multimodal fusion has made significant progress, its advancement is severely hindered by the lack of adequate evaluation benchmarks. Current fusion methods are typically evaluated on a small selection of public datasets, a limited scope that inadequately represents the complexity and diversity of real-world scenarios, potentially leading to biased evaluations. This issue presents a twofold challenge. On one hand, models may overfit to the biases of specific datasets, hindering their generalization to broader practical applications. On the other hand, the absence of a unified evaluation standard makes fair and objective comparisons between different fusion methods difficult. Consequently, a truly universal and high-performance fusion model has yet to emerge. To address these challenges, we have developed a large-scale, domain-adaptive benchmark for multimodal evaluation. This benchmark integrates over 30 datasets, encompassing 15 modalities and 20 predictive tasks across key application domains. To complement this, we have also developed an open-source, unified, and automated evaluation pipeline that includes standardized implementations of state-of-the-art models and diverse fusion paradigms. Leveraging this platform, we have conducted large-scale experiments, successfully establishing new performance baselines across multiple tasks. This work provides the academic community with a crucial platform for rigorous and reproducible assessment of multimodal models, aiming to propel the field of multimodal artificial intelligence to new heights.
A Multi-Agent System for Semantic Mapping of Relational Data to Knowledge Graphs
Venue: arXiv cs.DB 2025
Year: 2025

Enterprises often maintain multiple databases for storing critical business data in siloed systems, resulting in inefficiencies and challenges with data interoperability. A key to overcoming these challenges lies in integrating disparate data sources, enabling businesses to unlock the full potential of their data. Our work presents a novel approach for integrating multiple databases using knowledge graphs, focusing on the application of large language models as semantic agents for mapping and connecting structured data across systems by leveraging existing vocabularies. The proposed methodology introduces a semantic layer above tables in relational databases, utilizing a system comprising multiple LLM agents that map tables and columns to Schema.org terms. Our approach achieves a mapping accuracy of over 90% in multiple domains.