OpenRaiser/Intern-Atlas huggingface dataset:
A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration
Venue: arXiv cs.RO 2026
Year: 2026

Human activity recognition (HAR) is fundamental in human-robot collaboration (HRC), enabling robots to respond to and dynamically adapt to human intentions. This paper introduces a HAR system combining a modular data glove equipped with Inertial Measurement Units and a vision-based tactile sensor to capture hand activities in contact with a robot. We tested our activity recognition approach under different conditions, including offline classification of segmented sequences, real-time classification under static conditions, and a realistic HRC scenario. The experimental results show a high accuracy for all the tasks, suggesting that multiple collaborative settings could benefit from this multi-modal approach.
ShapBPT: Image Feature Attributions Using Data-Aware Binary Partition Trees
Venue: arXiv cs.CV 2026
Year: 2026

Pixel-level feature attributions are an important tool in eXplainable AI for Computer Vision (XCV), providing visual insights into how image features influence model predictions. The Owen formula for hierarchical Shapley values has been widely used to interpret machine learning (ML) models and their learned representations. However, existing hierarchical Shapley approaches do not exploit the multiscale structure of image data, leading to slow convergence and weak alignment with the actual morphological features. Moreover, no prior Shapley method has leveraged data-aware hierarchies for Computer Vision tasks, leaving a gap in model interpretability of structured visual data. To address this, this paper introduces ShapBPT, a novel data-aware XCV method based on the hierarchical Shapley formula. ShapBPT assigns Shapley coefficients to a multiscale hierarchical structure tailored for images, the Binary Partition Tree (BPT). By using this data-aware hierarchical partitioning, ShapBPT ensures that feature attributions align with intrinsic image morphology, effectively prioritizing relevant regions while reducing computational overhead. This advancement connects hierarchical Shapley methods with image data, providing a more efficient and semantically meaningful approach to visual interpretability. Experimental results confirm ShapBPT's effectiveness, demonstrating superior alignment with image structures and improved efficiency over existing XCV methods, and a 20-subject user study confirming that ShapBPT explanations are preferred by humans.
FADE: Selective Forgetting via Sparse LoRA and Self-Distillation
Venue: arXiv cs.CV 2026
Year: 2026

Machine Unlearning aims to remove the influence of specific data or concepts from trained models while preserving overall performance, a capability increasingly required by data protection regulations and responsible AI practices. Despite recent progress, unlearning in text-to-image diffusion models remains challenging due to high computational costs and the difficulty of balancing effective forgetting with retention of unrelated concepts. We introduce FADE (Fast Adapter for Data Erasure), a two-stage unlearning method for image generation that combines parameter localization with self-distillation. FADE first identifies parameters most responsible for the forget set using gradient-based saliency and constrains updates through sparse LoRA adapters, ensuring lightweight, localized modifications. In a second stage, FADE applies a self-distillation objective that overwrites the forgotten concept with a user-defined surrogate while preserving behavior on retained data. The resulting adapters are memory-efficient, reversible, and can be merged or removed at runtime, enabling flexible deployment in production systems. We evaluated FADE on the UnlearnCanvas benchmark and conducted ablation studies on Imagenette, Labeled Faces in the Wild, AtharvaTaras Dog Breeds Dataset, and SUN Attributes datasets, demonstrating State-of-the-Art unlearning performance with fine-grained control over the forgetting-retention trade-off. Our results demonstrate that FADE achieves strong concept erasure and high retainability across various domains, making it a suitable solution for selective unlearning in diffusion-based image generation models.
Video-based Music Generation
Venue: arXiv cs.LG 2026
Year: 2026

As the volume of video content on the internet grows rapidly, finding a suitable soundtrack remains a significant challenge. This thesis presents EMSYNC (EMotion and SYNChronization), a fast, free, and automatic solution that generates music tailored to the input video, enabling content creators to enhance their productions without composing or licensing music. Our model creates music that is emotionally and rhythmically synchronized with the video. A core component of EMSYNC is a novel video emotion classifier. By leveraging pretrained deep neural networks for feature extraction and keeping them frozen while training only fusion layers, we reduce computational complexity while improving accuracy. We show the generalization abilities of our method by obtaining state-of-the-art results on Ekman-6 and MovieNet. Another key contribution is a large-scale, emotion-labeled MIDI dataset for affective music generation. We then present an emotion-based MIDI generator, the first to condition on continuous emotional values rather than discrete categories, enabling nuanced music generation aligned with complex emotional content. To enhance temporal synchronization, we introduce a novel temporal boundary conditioning method, called "boundary offset encodings," aligning musical chords with scene changes. Combining video emotion classification, emotion-based music generation, and temporal boundary conditioning, EMSYNC emerges as a fully automatic video-based music generator. User studies show that it consistently outperforms existing methods in terms of music richness, emotional alignment, temporal synchronization, and overall preference, setting a new state-of-the-art in video-based music generation.
CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs
Venue: arXiv cs.SE 2026
Year: 2026

Current paradigms for code verification rely heavily on external mechanisms-such as execution-based unit tests or auxiliary LLM judges-which are often labor-intensive or limited by the judging model's own capabilities. This raises a fundamental, yet unexplored question: Can an LLM's functional correctness be assessed purely from its internal computational structure? Our primary objective is to investigate whether the model's neural dynamics encode internally decodable signals that are predictive of logical validity during code generation. Inspired by mechanistic interpretability, we propose to treat code verification as a mechanistic diagnostic task, mapping the model's explicit algorithmic trajectory into line-level attribution graphs. By decomposing complex residual flows, we aim to identify the structural signatures that distinguish sound reasoning from logical failure within the model's internal circuits. Analysis across Python, C++, and Java confirms that intrinsic correctness signals are robust across diverse syntaxes. Topological features from these internal graphs predict correctness more reliably than surface heuristics and enable targeted causal interventions to fix erroneous logic. These findings establish internal introspection as a decodable property for verifying generated code. Our code is at https:// github.com/bruno686/CodeCircuit.
Stickers on Facebook: Multifunctionality and face-enhancing politeness in everyday social interaction
Venue: arXiv cs.MM 2026
Year: 2026

Stickers are multimodal resources widely used in everyday digital conversations. Despite their popularity, most studies have focused on emojis and emoticons. Therefore, this study analyzes, from a sociopragmatic perspective, the use of stickers in the comments from a corpus of Facebook posts containing acts of face-enhancing politeness, created during and after the COVID-19 pandemic. The main objective is to identify their communicative functions and determine the extent to which they act as strategies of face-enhancing politeness also considering the gender variable. The results show a predominance of naked stickers and those representing human emotions and gestures, and festive situations. Six main functions were identified: affective, illocutionary, interactional, gestural, aesthetic, and representative or substitutive. It was found that stickers can intensify polite messages and express face-enhancing politeness autonomously. Furthermore, gender differences were observed: women use more stickers, especially cute and affectionate ones, whereas men prefer masculine human figures. These findings highlight the key and multifunctional role of stickers in affective digital communication.
WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark
Venue: arXiv cs.CV 2026
Year: 2026

Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synthesis. However, these models often face challenges when dealing with implicit editing instructions, which describe the cause of a visual change without explicitly detailing the resulting outcome. These limitations arise because existing models rely on uniform editing strategies that are not equipped to handle the complex world knowledge and reasoning required for implicit instructions. To address this gap, we introduce \textbf{WorldEdit}, a dataset specifically designed to enable world-driven image editing. WorldEdit consists of high-quality editing samples, guided by paraphrased instructions that align with real-world causal logic. Furthermore, we provide \textbf{WorldEdit-Test} for evaluating the existing model's performance on causal editing scenarios. With WorldEdit, we use a two-stage training framework for fine-tuning models like Bagel, integrating with a causal verification reward. Our results show that the proposed dataset and methods significantly narrow the gap with GPT-4o and Nano-Banana, demonstrating competitive performance not only in instruction following but also in knowledge plausibility, where many open-source systems typically struggle.
TLC-Plan: A Two-Level Codebook Based Network for End-to-End Vector Floorplan Generation
Venue: arXiv cs.CV 2026
Year: 2026

Automated floorplan generation aims to improve design quality, architectural efficiency, and sustainability by jointly modeling global spatial organization and precise geometric detail. However, existing approaches operate in raster space and rely on post hoc vectorization, which introduces structural inconsistencies and hinders end-to-end learning. Motivated by compositional spatial reasoning, we propose TLC-Plan, a hierarchical generative model that directly synthesizes vector floorplans from input boundaries, aligning with human architectural workflows based on modular and reusable patterns. TLC-Plan employs a two-level VQ-VAE to encode global layouts as semantically labeled room bounding boxes and to refine local geometries using polygon-level codes. This hierarchy is unified in a CodeTree representation, while an autoregressive transformer samples codes conditioned on the boundary to generate diverse and topologically valid designs, without requiring explicit room topology or dimensional priors. Extensive experiments show state-of-the-art performance on RPLAN dataset (FID = 1.84, MSE = 2.06) and leading results on LIFULL dataset. The proposed framework advances constraint-aware and scalable vector floorplan generation for real-world architectural applications. Source code and trained models are released at https://github.com/rosolose/TLC-PLAN.
Extended to Reality: Prompt Injection in 3D Environments
Venue: arXiv cs.CV 2026
Year: 2026

Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering diverse applications such as robotics and situated conversational agents. When MLLMs reason over camera-captured views of the physical world, a new attack surface emerges: an attacker can place text-bearing physical objects in the environment to override MLLMs' intended task. While prior work has studied prompt injection in the text domain and through digitally edited 2D images, it remains unclear how these attacks function in 3D physical environments. To bridge the gap, we introduce PI3D, a prompt injection attack against MLLMs in 3D environments, realized through text-bearing physical object placement rather than digital image edits. We formulate and solve the problem of identifying an effective 3D object pose (position and orientation) with injected text, where the attacker's goal is to induce the MLLM to perform the injected task while ensuring that the object placement remains physically plausible. Experiments demonstrate that PI3D is an effective attack against multiple MLLMs under diverse camera trajectories. We further evaluate existing defenses and show that they are insufficient to defend against PI3D.
Reasoning-Augmented Representations for Multimodal Retrieval
Venue: arXiv cs.IR 2026
Year: 2026

Universal Multimodal Retrieval (UMR) seeks any-to-any search across text and vision, yet modern embedding models remain brittle when queries require latent reasoning (e.g., resolving underspecified references or matching compositional constraints). We argue this brittleness is often data-induced: when images carry "silent" evidence and queries leave key semantics implicit, a single embedding pass must both reason and compress, encouraging spurious feature matching. We propose a data-centric framework that decouples these roles by externalizing reasoning before retrieval. Using a strong Vision--Language Model, we make implicit semantics explicit by densely captioning visual evidence in corpus entries, resolving ambiguous multimodal references in queries, and rewriting verbose instructions into concise retrieval constraints. Inference-time enhancement alone is insufficient; the retriever must be trained on these semantically dense representations to avoid distribution shift and fully exploit the added signal. Across M-BEIR, our reasoning-augmented training method yields consistent gains over strong baselines, with ablations showing that corpus enhancement chiefly benefits knowledge-intensive queries while query enhancement is critical for compositional modification requests. We publicly release our code at https://github.com/AugmentedRetrieval/ReasoningAugmentedRetrieval.
Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
Venue: arXiv cs.LG 2026
Year: 2026

Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological features. We present Landscaper, an open-source Python package for arbitrary-dimensional loss landscape analysis. Landscaper combines Hessian-based subspace construction with topological data analysis to reveal geometric structures such as basin hierarchy and connectivity. A key component is the Saddle-Minimum Average Distance (SMAD) for quantifying landscape smoothness. We demonstrate Landscaper's effectiveness across various architectures and tasks, including those involving pre-trained language models, showing that SMAD captures training transitions, such as landscape simplification, that conventional metrics miss. We also illustrate Landscaper's performance in challenging chemical property prediction tasks, where SMAD can serve as a metric for out-of-distribution generalization, offering valuable insights for model diagnostics and architecture design in data-scarce scientific machine learning scenarios.
Exploring Teachers' Perspectives on Using Conversational AI Agents for Group Collaboration
Venue: arXiv cs.HC 2026
Year: 2026

Collaboration is a cornerstone of 21st-century learning, yet teachers continue to face challenges in supporting productive peer interaction. Emerging generative AI tools offer new possibilities for scaffolding collaboration, but their role in mediating in-person group work remains underexplored, especially from the perspective of educators. This paper presents findings from an exploratory qualitative study with 33 K12 teachers who interacted with Phoenix, a voice-based conversational agent designed to function as a near-peer in face-to-face group collaboration. Drawing on playtesting sessions, surveys, and focus groups, we examine how teachers perceived the agent's behavior, its influence on group dynamics, and its classroom potential. While many appreciated Phoenix's capacity to stimulate engagement, they also expressed concerns around autonomy, trust, anthropomorphism, and pedagogical alignment. We contribute empirical insights into teachers' mental models of AI, reveal core design tensions, and outline considerations for group-facing AI agents that support meaningful, collaborative learning.
Architectural Anti-Patterns in Student-Developed Microservice Architectures: An Exploratory Study
Venue: arXiv cs.SE 2026
Year: 2026

Teaching microservice architectures is challenging due to distributed complexity and the gap between academia and industry. Understanding the quality issues students introduce in MSAs is essential to improve education. This study analyzes student-developed microservices using an established anti-pattern taxonomy and derives lessons learned with actionable teaching recommendations. We conducted a longitudinal, project-based course (2023-2025) involving 216 Master's students (67 teams) who designed and deployed a realistic, containerized MSA for a gamified testing platform. The final systems revealed 23 out of 58 known MSA anti-patterns, spanning five categories. Security issues were most frequent, highlighting weaknesses in authentication, authorization, and data protection. Team Organization and Service Interaction problems followed, reflecting limited DevOps experience and difficulties in inter-service coordination. Fewer issues appeared in Intra-service Design and Inter-service Decomposition, suggesting students generally defined service boundaries well. Overall, students prioritized feature delivery over robustness and operational discipline. To address this, we recommend enforcing minimal standards (API contracts, gateways), providing labs on resilient communication, integrating security-by-design practices, and offering CI-CD templates. The paper contributes a realistic, full-scale educational experience and a replicable model for teaching industry-aligned microservice architecture.
A compliant ankle-actuated compass walker with triggering timing control
Venue: arXiv cs.RO 2026
Year: 2026

Passive dynamic walkers are widely adopted as a mathematical model to represent biped walking. The stable locomotion of these models is limited to tilted surfaces, requiring gravitational energy. Various techniques, such as actuation through the ankle and hip joints, have been proposed to extend the applicability of these models to level ground and rough terrain with improved locomotion efficiency. However, most of these techniques rely on impulsive energy injection schemes and torsional springs, which are quite challenging to implement in a physical platform. Here, a new model is proposed, named triggering controlled ankle actuated compass gait (TC-AACG), which allows non-instantaneous compliant ankle pushoff. The proposed technique can be implemented in physical platforms via series elastic actuators (SEAs). Our systematic examination shows that the proposed approach extends the locomotion capabilities of a biped model compared to impulsive ankle pushoff approach. We provide extensive simulation analysis investigating the locomotion speed, mechanical cost of transport, and basin of attraction of the proposed model.
Can LLMs Discern the Traits Influencing Your Preferences? Evaluating Personality-Driven Preference Alignment in LLMs
Venue: arXiv cs.CL 2026
Year: 2026

User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet how to reliably leverage preference signals for answer generation remains under-explored. In practice, preferences can be noisy, incomplete, or even misleading, which can degrade answer quality when applied naively. Motivated by the observation that stable personality traits shape everyday preferences, we study personality as a principled ''latent'' signal behind preference statements. Through extensive experiments, we find that conditioning on personality-aligned preferences substantially improves personalized question answering: selecting preferences consistent with a user's inferred personality increases answer-choice accuracy from 29.25% to 76%, compared to using randomly selected preferences. Based on these findings, we introduce PACIFIC (Preference Alignment Choices Inference for Five-factor Identity Characterization), a personality-labeled preference dataset containing 1200 preference statements spanning diverse domains (e.g., travel, movies, education), annotated with Big-Five (OCEAN) trait directions. Finally, we propose a framework that enables an LLM model to automatically retrieve personality-aligned preferences and incorporate them during answer generation.
Measuring Complexity at the Requirements Stage: Spectral Metrics as Development Effort Predictors
Venue: arXiv cs.SE 2026
Year: 2026

Complexity in engineered systems presents one of the most persistent challenges in modern development since it is driving cost overruns, schedule delays, and outright project failures. Yet while architectural complexity has been studied, the structural complexity embedded within requirements specifications remains poorly understood and inadequately quantified. This gap is consequential: requirements fundamentally drive system design, and complexity introduced at this stage propagates through architecture, implementation, and integration. To address this gap, we build on Natural Language Processing methods that extract structural networks from textual requirements. Using these extracted structures, we conducted a controlled experiment employing molecular integration tasks as structurally isomorphic proxies for requirements integration - leveraging the topological equivalence between molecular graphs and requirement networks while eliminating confounding factors such as domain expertise and semantic ambiguity. Our results demonstrate that spectral measures predict integration effort with correlations exceeding 0.95, while structural metrics achieve correlations above 0.89. Notably, density-based metrics show no significant predictive validity. These findings indicate that eigenvalue-derived measures capture cognitive and effort dimensions that simpler connectivity metrics cannot. As a result, this research bridges a critical methodological gap between architectural complexity analysis and requirements engineering practice, providing a validated foundation for applying these metrics to requirements engineering, where similar structural complexity patterns may predict integration effort.