The knowing--doing gap, the mismatch between ideas articulated during model reasoning and the realized creative artifact, remains a fundamental challenge in creative AI and persists in LLM-based artistic creation. This paper responds to this gap by introducing a systematic and interpretable framework for examining how user intent is articulated during model reasoning and selectively realized, or lost, during action in LLMs, using symbolic music composition as an analytical lens. We present a realizational process theory that formalizes creative generation and localizes the knowing--doing gap at the realization stage. We instantiate this theory with Music Atelier (Mutelier), an LLM-as-a-Judge framework that operationalizes idea-level realization analysis and makes the gap observable and analyzable in practice. Across diverse evaluation settings, we show that Mutelier reveals reliable, previously invisible failure modes in which intent-aligned ideas are articulated during reasoning but fail to materialize in the final artifact. By reframing artistic creation as a realizational process, this work provides a principled process-level foundation for understanding how knowing--doing gaps emerge in machine creativity.
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Proactive scaling improves cloud resource efficiency by forecasting system-relevant indicators and dynamically provisioning resources to maximize utilization while satisfying quality requirements. Existing approaches forecast service indicators in isolation, ignore forecasting uncertainty, and scale resource types independently, violating bundled resource constraints and degrading service quality. Therefore, we propose JointScaler, a learning-based framework for multi-indicator distribution forecasting and uncertainty-aware scaling. It captures inter-indicator dependencies via hierarchical attention, models dynamic uncertainties with normalizing flows, and leverages full predictive distributions to optimize bundled resource allocations under quality requirements. Evaluated on 4 real-world datasets, JointScaler improves point and distribution forecasting accuracy by 5.87% and 14.74%, outperforming 12 advanced baselines. In a week-long A/B test on a payment application’s cloud platform, it reduced GPU and CPU usage by 2,400+ and 37,000+ hours with stable service quality, delivering significant economic benefits.
Edge addition is commonly considered risky in Graph Anomaly Detection (GAD), as random edge addition may induce anomaly–normal connectivity. Consequently, most existing augmentation strategies focus on feature perturbation, edge removal, or subgraph sampling, leaving edge addition largely unexplored. In contrast, we empirically find that moderate and targeted structural completion among normal nodes consistently improves GAD performance, revealing guided edge addition as an overlooked yet effective augmentation dimension. Motivated by this observation, we introduce two adjacency-centered edge generation strategies with complementary mechanisms. One performs a training-free structural completion scheme via spectrum-aware Langevin dynamics, enriching graph connectivity while preserving node features. The other models the joint evolution of node features and graph structure through a stochastic differential equation–based diffusion process, producing structurally coherent and anomaly-aware complementary graphs. Extensive experiments on 12 benchmark datasets with 7 state-of-the-art GAD models demonstrate consistent and substantial improvements in both AUROC and AUPRC. Code and appendices are available at https://github.com/GaoHaokai222/LangGen-JointSDE
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable reasoning capabilities across various downstream multimodal tasks, they are proven to be vulnerable to carefully designed adversarial examples. Existing LVLM attackers show that exploring external components of adversarial guidance (e.g., forcing adversarial alignment, resembling harmful features) can help improve adversarial effects. However, leveraging the intrinsic patterns of LVLMs to induce adversarial perturbation generation by exploring how LVLMs perceive images has not been deeply studied. Inspired by the cognitive science, in this paper, we make the first attempt to investigate the interference of adversarial perturbation from the perspectives of image phase, and find that LVLMs are sensitive to the phase-aware image structure. Motivated by this, we propose a novel LVLM attack method called BadPhase with further backdoor designs, to implant adversarial phase as triggers into any image inputs via data poisoning so as to control the LVLMs’ predictions. A textual trigger and a backdoor perturbation switcher are also introduced to activate the malicious behavior only when both triggers are present. The whole backdoor optimization is implemented at the test-time to reduce the resource reliance. Experiments on four popular LVLMs and three benchmarks demonstrate the effectiveness of our proposed method.
We present S³-TIR (Coupled Spatial-Spectral Splatting), a physics-inspired framework for high-fidelity thermal infrared (TIR) novel view synthesis. Existing 3D Gaussian splatting methods struggle to capture the unique duality of TIR imagery: the coexistence of diffusive smoothness from heat conduction and structural sharpness from material discontinuities. To address this issue, we leverage the intrinsic frequency properties of thermal scenes, where conduction creates smooth fields while discontinuities cause sharp signal jumps. Accordingly, S³-TIR couples adaptive spatial support with internal spectral modulation via a unified generalized-exponential Gabor primitive. This design utilizes spatial support as a physical truncation controller, confining high-frequency textures within sharp boundaries while maintaining smooth diffusion elsewhere. Furthermore, we propose a frequency-aware deferred splatting scheme that enforces multi-view frequency consistency, anchoring frequency distributions to the 3D structure rather than view-dependent appearance. Experiments on diverse benchmarks demonstrate that S³-TIR achieves state-of-the-art performance, notably reducing LPIPS by 42.5% and improving PSNR by 3.2% on the RGBT-Scenes dataset, while yielding structurally stable thermal sequences free from jittering artifacts.
Contrastive learning has become a dominant paradigm for learning time series representations from large-scale unlabeled data. However, current methods are often adapted from computer vision and rely on random time-domain augmentations (e.g., jittering and cropping). Such augmentations can unpredictably disrupt the natural frequency structure of signals, leading to representations failing to capture crucial patterns in the data. To address this, we propose a framework of Frequency-Aware Augmentation and Alignment for Time Series Contrastive Learning (FACL), which comprises two key innovations. First, FACL employs a novel frequency-structured augmentation mechanism based on wavelet transforms. The mechanism constructs controlled and interpretable contrastive views by the structured attenuation and recombination of specific wavelet components. Second, FACL introduces a multi-level contrastive objective that incorporates a subspace alignment strategy. This objective explicitly aligns representations within their corresponding frequency subspaces. Experiments across six forecasting and four classification benchmarks show that FACL achieves superior performance compared to recent baselines. Ablation studies and model analysis highlight the contribution of each component in FACL. Furthermore, low-sample semi-supervised learning experiments confirm the robustness and generalization of FACL.
Elevating AI on the Edge: A Demonstration of MIMaaS (Machine Intelligence with Microcontroller-as-a-Service)
PDF ↗Deploying AI on microcontrollers (MCUs) is challenging. We introduce MIMaaS, a Microcontroller-as-a-Service platform that enables users to upload a model, select a target device, and receive a detailed performance report remotely. A key innovation is our measurement of real-world power consumption, alongside latency and memory usage, directly from the physical hardware. MIMaaS empowers researchers and developers to easily create and validate hardware-aware AI models without needing physical hardware access.
Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-intensive and highly context-specific. We present Principles2Plan, an interactive research prototype demonstrating how a human and a Large Language Model (LLM) can collaborate to produce context-sensitive ethical rules and guide automated planning. A domain expert provides the planning domain, problem details, and relevant high-level principles such as beneficence and privacy. The system generates operationalisable ethical rules consistent with these principles, which the user can review, prioritise, and supply to a planner to produce ethically-informed plans. To our knowledge, no prior system supports users in generating principle-grounded rules for classical planning contexts. Principles2Plan showcases the potential of human-LLM collaboration for making ethical automated planning more practical and feasible.
Recent research has increasingly focused on the reasoning capabilities of Large Language Models (LLMs) in multi-turn interactions, as these scenarios more closely mirror real-world problem-solving. However, analyzing the intricate reasoning processes within these interactions presents a significant challenge due to complex contextual dependencies and a lack of specialized visualization tools, leading to a high cognitive load for researchers. To address this gap, we present VISTA, an web-based Visual Interactive System for Textual Analytics in multi-turn reasoning tasks. VISTA allows users to visualize the influence of context on model decisions and interactively modify conversation histories to conduct "what-if" analyses across different models. Furthermore, the platform can automatically parse a session and generate a reasoning dependency tree, offering a transparent view of the model's step-by-step logical path. By providing a unified and interactive framework, VISTA significantly reduces the complexity of analyzing reasoning chains, thereby facilitating a deeper understanding of the capabilities and limitations of current LLMs. The platform is open-source and supports easy integration of custom benchmarks and local models.
In this work, we propose a diffusion-based method for 3D-aware image composition. Previous approaches have focused on 2D-view image composition, which limits their handling of complex 3D spatial relationships. Consequently, they are not well-suited for applications requiring precise 3D object control and iterative refinement, including interior design visualization, visual effects prototyping, and virtual reality scene construction. In contrast, our method extracts 3D bounding boxes for all objects in the scene image. Users can then specify a new 3D bounding box based on existing spatial context and provide an image of the target object. Leveraging a fine-tuned diffusion model, our approach enables high-fidelity image composition while preserving the underlying 3D structure of the scene.
We present Magnol.AI Copilot, an extension of the Magnol.AI digital biomarker platform that integrates multimodal large language models (LLMs) to transform digital health technology (DHT) trial dashboards into conversational systems. Copilot augments the platform with a multi-agent orchestration layer and vision-enabled LLMs that interpret visualizations, tabular summaries, and textual metadata. The system enables natural language queries and automatic generation of contextual insights, allowing researchers to interact with wearable data through dialogue rather than static inspection. A case study with an actigraphy device demonstrates Copilot’s ability to identify nightly compliance gaps and provide contextual explanations, reducing cognitive load compared to manual dashboard review. This work presents a novel integration of IoMT infrastructure with multimodal LLMs, advancing digital biomarker research toward conversational and accessible DHT trial platforms.
The evaluation of Large Language Models (LLMs) increasingly relies on other LLMs acting as judges. However, current evaluation paradigms typically yield a single score or ranking, answering which model is better but not why. While essential for benchmarking, these top-level scores obscure the specific, actionable reasons behind a model's performance. To bridge this gap, we introduce CLEAR, an interactive, open-source package for LLM-based error analysis. CLEAR first generates per-instance textual feedback, then it creates a set of system-level error issues, and quantifies the prevalence of each identified issue. Our package also provides users with an interactive dashboard that allows for a comprehensive error analysis through aggregate visualizations, applies interactive filters to isolate specific issues or score ranges, and drills down to the individual instances that exemplify a particular behavioral pattern. We demonstrate CLEAR analysis for RAG and Math benchmarks, and showcase its utility through a user case study.
Causal agents have emerged as promising tools for automating causal analysis based on user queries. However, existing causal agent systems are often limited to a single causal task, limiting their ability to handle complex queries. In addition, they accept only numerical data as input, preventing the integration of domain knowledge expressed in natural language. To overcome these limitations, we propose the OrcheCause agent, a causal agent leveraging textual knowledge for end-to-end causal inference. Specifically, OrcheCause is designed to orchestrate a sequence of interrelated causal tasks in response to user queries. Furthermore, OrcheCause supports diverse data types—numerical as well as textual data—by extracting cause-effect pairs from the relevant sources and incorporating them into causal discovery (CD), thereby improving the performance of CD. OrcheCause also introduces a metric-based hyperparameter optimization framework for CD when ground-truth graphs are not available.
PortfolioPilot: An Agentic Platform for Financial Portfolio Management Algorithm Development and Evaluation
PDF ↗Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.
Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this paper, we introduce VitalDiagnosis, an LLM-driven ecosystem designed to shift chronic disease management from passive monitoring to proactive, interactive engagement. By integrating continuous data from wearable devices with the reasoning capabilities of LLMs, the system addresses both acute health anomalies and routine adherence. It analyzes triggers through context-aware inquiries, produces provisional insights within a collaborative patient–clinician workflow, and offers personalized guidance. This approach aims to promote a more proactive and cooperative care paradigm, with the potential to enhance patient self-management and reduce avoidable clinical workload.
Despite the rapid advancement of generative agents, their deployment in real world industry scenarios often encounters challenges due to a lack of domain-specific knowledge. To address this gap, we present KnowPilot: a Domain-Specific Knowledge Augmented Agent System. KnowPilot is an open-source framework that integrates task-specific priors, explicit knowledge, and experiential knowledge to enhance agent performance in specialized applications. It combines knowledge retrieval from structured repositories with a memory system capable of capturing expert experience through human–AI interaction.
AgentGraph: Trace-to-Graph Platform for Interactive Analysis and Robustness Testing in Agentic AI Systems
PDF ↗Modern Agentic AI systems plan, reason, and act across multiple steps, creating execution patterns that are difficult to interpret. Existing observability platforms track prompt I/O and operational metrics but require manual inspection of traces to reconstruct structure and reasoning. We present AgentGraph, which converts execution logs into interactive knowledge graphs and actionable insights. Nodes represent agents, tasks, tools, data inputs/outputs, and humans, while typed edges capture relations such as inputs consumed, tasks delegated or sequenced, tools required or used, outputs produced and delivered, and interventions from agents or humans. Each graph element links to its exact trace span, ensuring verifiability. Building on this representation, AgentGraph enables two analyses: qualitative trace-grounded failure detection and optimisation recommendations, and quantitative robustness evaluation via perturbation testing and causal attribution.
We present AgentSeer, an interactive observability framework for agentic AI systems. Unlike conventional tracing tools that expose raw spans or model-centric metrics, AgentSeer introduces a dual graph decomposition constructed through a deterministic rule-based parser: a temporal action graph, where each prompt or tool invocation is represented as a distinct action, and a component graph capturing architectural relations among agents, tools, and memory modules. Beyond visualization, AgentSeer enables action-level red teaming, where jailbreak payloads are systematically attached to every action node (including agent messages, tool calls, and memory retrievals) to uncover vulnerabilities invisible to model-level testing. Our demonstration features a six-agent hierarchical testbed with interactive visualization and deployment-oriented safety evaluation applied directly on the same prompts and contexts, systematically revealing high-risk interactions, context-dependent vulnerabilities, and emergent behaviors. By combining structured decomposition, automated red teaming, and rule-based reliability, AgentSeer establishes a safety-first methodology for observability in multi-agent AI.
We present AutoTuneX, a system architecture design and implementation for users to interactively fine-tune large language models (LLMs) based on automated hyperparameter optimization particularly built around Bandit Limited Discrepancy Search. Next to a classical Graphical User Interface (GUI) our system features an agentic runtime to facilitate automated fine-tuning via chat.
We present SafeLens, a lightweight segment-level video moderation system that fuses speech, text, and visual frames to produce hateful content detection for each segment. For every segment, SafeLens returns a structured prediction: label, prediction confidence, reasons for flag, harm categories. The structured predictions are optimized for triage, appeals, and downstream enforcement. The system is modular (pluggable speech, text, and visual processing modules back-ends and a mid-size policy Language Language Model (LLM) agent with parameter-efficient tuning). In the live demo, attendees can upload or select clips, scrub the timeline to flag hateful segments, inspect rationales, and vary the policy LLM agent to benchmark the hateful content moderation performance.