Giuseppe Coviello of NEC Laboratories America presents “Völ: A Domain-Specific Language and Runtime for AI-Native Workflows” at HiPES 2026, held alongside Euro-Par in Pisa, Italy on August 25th. Völ lets developers describe AI workflows declaratively, leaving execution to the runtime.
Satellite imagery is vast, high-resolution, and rich with information, but finding specific objects within it using natural language has remained a stubborn challenge. Open-SAT, developed by researchers at NEC Laboratories America and North South University, tackles this problem without retraining any models.
In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses significant challenges for retrieving relevant image tiles, as the retrieval system must generalize to a wide range of unseen objects and concepts. While vision-language models (VLMs) such as CLIP are widely used for text-image retrieval, even fine-tuned variants often struggle to accurately align such queries with satellite imagery. To address this, we propose Open-SAT, a training-free query embedding refinement algorithm that operates at inference time to improve alignment between user queries and satellite image content. Open-SAT uses VLMs to compute embeddings for image tiles, which are stored in a vector database for efficient retrieval. At query time, it leverages Large Language Models (LLMs) to refine the text embeddings by incorporating contextual information about objects of interest and their surroundings. A threshold-free retrieval mechanism further enhances accuracy and efficiency. Experimental results in three public benchmarks demonstrate that Open-SAT improves the F1 score by up to 16.04%, while retrieving a comparable number of image tiles. These results demonstrate the effectiveness of Open-SAT in open-vocabulary satellite image retrieval, leveraging LLM guidance without the need for additional training or supervision.
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multiagent plan execution platform that interprets natural-language plans while enforcing stepwise execution through constraints and rubrics. RunAgent bridges the expressiveness of natural language and the determinism of programming languages via an agentic language with explicit control constructs (e.g., IF, GOTO, FORALL). It autonomously derives and verifies constraints at each step; dynamically selects among LLM reasoning, tool use, and Python execution; and integrates error correction to ensure correctness. Evaluations on Natural-plan and SciBench Datasets demonstrate that RunAgent outperforms baseline LLMs and state-of-the-art PlanGEN methods.
Deploying microservices across the computing continuum (edgecloud) requires placement decisions that adapt to workload variation and heterogeneous infrastructure, yet existing solutions often rely on static policies or opaque heuristics. We present Bellona a system for reliable and auditable Large Language Model (LLM)-driven workflow execution that combines a declarative specification language with a runtime that orchestrates tool calls, conditional control flow, and structured LLM reasoning. Using Bellona, we implement an agentic placement workflow that automatically recommends edge or cloud execution. The workflow uses structured prompts and verifiable tool interactions to (i) parse placement and latency-report instructions, (ii) update the latency log, and (iii) select placements based on measured latency improvement thresholds. We evaluate the resulting agent on two representative microservices-based video analytics applications (human-attributes detection and face recognition) over two days of varying workload. Across 1,440 placement decisions per service, the agent achieves accuracies of 94.66%/84.94% (human-attributes detection, Day1/Day2) and 80.91%/96.53% (face recognition, Day1/Day2) with GPT-4o; with GPT-5, accuracy increases to 98.82%/99.45% (human-attributes detection) and 99.31%/99.8% (face recognition). These results demonstrate that Bellona can support practical, self-improving agentic control for placement of microservices on the computing continuum.
Radiology Report Generation (RRG) is a critical step toward automating healthcare workflows, facilitating accurate patient assessments, and reducing the workload of medical professionals. Despite recent progress in Large Medical Vision-Language Models (Med-VLMs), generating radiology reports that are both visually grounded and clinically accurate remains a significant challenge. Existing approaches often rely on large labeled corpora for pre-training, costly task-specific preference data, or retrieval-based methods. However, these strategies do not adequately mitigate hallucinations arising from poor cross-modal alignment between visual and linguistic representations. To address these limitations, we propose VALOR:Visual Alignment of Medical Vision-Language Models for GrOunded Radiology Report Generation. Our method introduces a reinforcement learning-based post-alignment framework utilizing Group-Relative Proximal Optimization (GRPO). The training proceeds in two stages: (1) improving the Med-VLM with textual rewards to encourage clinically precise terminology, and (2) aligning the vision projection module of the textually grounded model with disease findings, thereby guiding attention toward image re gions most relevant to the diagnostic task. Extensive experiments on multiple benchmarks demonstrate that VALOR substantially improves factual accuracy and visual grounding, achieving significant performance gains over state-of-the-art report generation methods.
Large language models (LLMs) are becoming the centerpiece in the design and deployment of Agentic artificial intelligence (AI) systems. AI agents typically have (a) reasoning ability to analyze and think through the given task, (b) context/memory to remember things in the short-term and long-term, and (c) tools at their disposal to interact with the outsideworld. While solving the given task, it must decide whether tool use is required; if so, it must then select the appropriate tool and invoke it with the correct parameters. Although LLMs have advanced considerably in recent years, their tool-use capabilities remain limited. Even OpenAIs most capable model to date, GPT-5, continues to struggle with reliable tool usage. In this paper, we propose TacTool, which empowers AI agents with improved tool selection and tool call formulation using different LLMs. We conduct experiments using Nestful and Berkeley Function Calling Leaderboard version 3 (BFCLv3) benchmarks and show that TacTool achieves ?27% and ?3% improvement over GPT- 4o on Nestful and BFCL v3 dataset, respectively.
Creating effective slide presentations requires adapting both content and structure to match the communication context e.g. whether the presentation is for summarizing to executives, or reporting progress to research supervisors. In research and enterprise environments, this need for context-sensitive presentations often leads to repeated, manual reformatting of the same material to suit different audiences. Existing generative systems support slide creation but typically rely on structured inputs, assume a fixed format, and offer limited ability to iteratively refine outputs through natural language feedback. Moreover, they rarely accommodate organizational constraints such as formatting guidelines, domain-specific terminology, or branding requirements. We present SlideCraft, a context-aware generative agent that autonomously creates and edits slide presentations based on natural language instructions. SlideCraft infers the intended presentation context, such as an executive-facing or a project review summary for technical oversight, and selects the appropriate slide template. It then synthesizes content from input documents, enriches it with external knowledge and internal assets, assembles it into a structured intermediate representation, and generates a validated slide deck. SlideCraft supports both first-time slide creation and iterative updates, operating through familiar natural language interfaces like email or messaging tools. Our experiments demonstrate that SlideCraft consistently produces high-quality, context-aware presentations tailored to diverse communication settings, with minimal human input and reliable adherence to enterprise constraints.
Identifying subject-matter experts within organizations remains a challenging task due to the scale, heterogeneity, and unstructured nature of enterprise knowledge assets. We present TalentScout, an AI-driven expert identification system that constructs a unified, skill-centric knowledge graph by ingesting and analyzing diverse media, including research papers, reports, presentations, transcripts, and supervisor recommendations. TalentScout’s modular architecture integrates document parsing, audio/video transcription, metadata extraction, large language model-based skill extraction, multi-factor author disambiguation, and evidence-weighted skill attribution. At query time, TalentScout decomposes natural language queries into canonical skill requirements, traverses the constructed knowledge graph, and ranks experts based on aggregated skill weights, document quality, and endorsement signals, providing document-level justifications for each recommendation. We evaluate TalentScout on multiple public and internal enterprise datasets, including DBLP, TREC Enterprise, Tilburg, and ManConCorpus. Using standard information retrieval metrics such as Precision@ 5, Recall@5, nDCG@5, and Mean Reciprocal Rank (MRR), TalentScout consistently outperforms leading baselines, achieving up to 24% higher Precision@ 5 in early expert retrieval. The results highlight TalentScouts scalability, transparency, and accuracy, establishing it as a practical solution for evidence-based expert discovery and organizational talent management.
Murugan Sankaradas (presenting virtually) will present “TalentScout: Multimodal AI-Driven Expert Finding in Organizations” at the IEEE International Conference on Pervasive Intelligence and Computing (PICom2025) on Tuesday, October 21 (10:30am–12pm JST) | Monday, October 20 (9:30–11pm ET) in Hokkaido, Japan.
