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Knowledge Graphs (KGs) evolve over time and it is critical to ensure that their integrity constraints are maintained after each update. We introduce GEV, the first tool to statically ensure that a KG update in Java preserves satisfaction of SHACL constraints. This allows verification of updates at design time, and eliminates the need for costly continuous revalidation. GEV is a command-line system that loads and verifies updates, applies them to a loaded KG, and keeps track of the validation status. Internally, it relies on SHACL graph updates, a theoretical framework with a method for static verification.
We present PERELMAN (PipEline foR sciEntific Literature Meta-ANalysis), an agentic framework designed to extract specific information from a large corpus of scientific articles to support large-scale literature reviews and meta-analyses. Our central goal is to reliably transform heterogeneous article content into a unified, machine-readable representation. PERELMAN first elicits domain knowledge-including target variables, inclusion criteria, units, and normalization rules-through a structured dialogue with a subject-matter expert. This domain knowledge is then reused across multiple stages of the pipeline and guides coordinated agents in extracting evidence from narrative text, tables, and figures, enabling consistent aggregation across studies. In order to assess reproducibility and validate our implementation, we evaluate the system on the task of reproducing the meta-analysis of layered Li-ion cathode properties LiNi0.8Mn0.1Co0.1O2 (NMC811). We describe our solution, which has the potential to reduce the time required to prepare meta-analyses from months to minutes.
Machine learning models for steel property prediction routinely report high-quality metrics with R² > 0.85, yet these results rely on random splits that allow similar grades in both train and test sets. We present SteelAgent, an interactive system that exposes a critical generalization gap: the same models drop from R² > 0.85 to R² = 0.11 on unseen steel families, revealing more than 7 times higher quality degradation. Similarly, conformal prediction coverage degrades from 91% to 38% under distribution shift induced by holding out substantial data sources. SteelAgent combines physics-informed features grounded in classical metallurgy and interpretable models with conformal uncertainty quantification, and an LLM orchestrator that coordinates six domain-specific tools. The system supports property prediction with specification compliance checking, competitive steel comparison, and cost-aware inverse alloy design over 3,741 heat treatment records spanning 1,234 grades. All predictions are traceable through explicit tool calls, ensuring that all physical quantities are computed, not generated. We made the code and data freely accessible to the community.
Entity linking is a key component of many downstream NLP systems, yet existing approaches are often tied to the specific target knowledge bases and domains, limiting their real world application. In this paper, we extend LELA, a modular and domain-agnostic LLM-based entity disambiguation method, into a practical Python library that integrates zero-shot Named Entity Recognition (NER) -- thereby providing a complete end-to-end pipeline for entity-linking in real-world usage. We provide experimental results validating LELA's performance and robustness across diverse entity linking settings. In our demo, users can play with the system on their own input texts. All code is publicly available at https://github.com/dig-team/LELA, and a video is at https://www.youtube.com/watch?v=WdupiRjLbR4.
While Open-Set Semantic Mapping and 3D Semantic Scene Graphs (3DSSGs) have become established paradigms in robotic perception in recent years, most existing works are limited to small environments or sacrifice geometric detail and instance granularity for scalability. Deploying these systems at scale for large multi-room environments remains a major challenge due to the computational overhead of high-dimensional feature integration and the maintenance of the 3DSSG structure. In this paper, we demonstrate a modular mapping architecture that establishes 3D Semantic Scene Graphs (3DSSGs) as its foundational backend. Unlike approaches that generate scene graphs as a post-processing step, our system maintains the graph as the primary, incrementally updated knowledge representation. Our architecture is optimized for GPU-accelerated operations, enabling the dense representation of extensive environments containing thousands of unique object instances, supporting open-vocabulary queries via CLIP features without requiring any additional post-processing steps. In this live demonstration, we showcase our pipeline processing large-scale data from the Habitat Matterport 3D (HM3D) dataset as well as live data collected from a handheld device. Attendees will interact with the generated maps by performing real-time, open-set queries (e.g., “find the vintage wooden chair”) across complex, multi-story environments, highlights the system's capability to represent dynamic, human-aligned environmental understanding suitable for downstream robotic tasks.
As Large Language Model (LLM) agents scale toward real-world deployment, they generate large volumes of fragmented, non-standardized execution traces. Many existing observability platforms treat these traces primarily as passive logging artifacts, lacking the unified infrastructure to operationalize them for active governance and agent adaptation across heterogeneous single-agent and multi-agent workflows. To address this gap, we introduce TraceBrain, an open-source infrastructure for autonomous agent trace management. TraceBrain adopts a framework-agnostic architecture built on a delta-based OpenTelemetry (OTLP) schema, which mitigates context explosion and supports on-demand reconstruction of long-horizon execution trajectories. For runtime governance, TraceBrain implements uncertainty-driven supervision, where an internal Trace Evaluator prioritizes ambiguous trajectories for human review, thereby reducing manual annotation workload. Moving beyond passive observation, the platform incorporates a hybrid semantic-lexical retrieval engine that combines dense vector similarity and exact keyword matching for operational memory retrieval. Furthermore, an automated curriculum mechanism continuously synthesizes failure patterns into structured training artifacts. Empirical evaluations demonstrate a ~100x reduction in storage overhead together with high precision in uncertainty-guided trace supervision. Ultimately, TraceBrain transforms the execution history into a reusable operational memory substrate, bridging runtime observability with retrieval-driven agent adaptation. The system is publicly available at https://github.com/ToolBrain/TraceBrain.
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular paradigm and semantics, DeepLog serves as a universal backend that can emulate many systems in the neurosymbolic alphabet soup. By treating diverse neurosymbolic languages as high-level specifications, the DeepLog software automatically compiles them into optimized arithmetic circuits. This design lowers the barrier for machine learning practitioners by treating logic as composable modules, while providing neurosymbolic developers with a shared, high-performance basis for prototyping new integration strategies. The code is available here: https://github.com/ML-KULeuven/deeplog
Forest imagery analysis often involves multiple tightly coupled vision tasks, which must be performed under substantial variation in geographic regions, sensors, and acquisition conditions. However, practitioners often lack a unified tool that is geospatial-native, cloud-optimized, and ML-integrated for end-to-end workflows spanning annotation, prediction, visualization, and downstream analysis at scale. We present AwakeForest, an interactive end-to-end platform designed for large-scale forest imagery that integrates model-assisted inference, automatic annotation, and human-in-the-loop refinement within a single workflow. Our platform supports plug-and-play integration of pretrained models and enables scalable interaction with forest imagery ranging from standard aerial scenes to large orthomosaics that can span several gigabytes to hundreds of gigabytes. AwakeForest produces analysis-ready outputs that can be directly used for downstream analysis and to support iterative model and annotation updates on new scenes. We demonstrate the system on the PALMS dataset and illustrate how AwakeForest supports an end-to-end workflow for practical forest management and analysis.
We demonstrate ADP-MA (Autonomous Data Processing using Meta-Agents), a system that autonomously solves a complex and diverse set of data processing tasks. Three domain-agnostic meta-agents coordinate task-specific ground agents through a multi-stage pipeline: data understanding, planning, critique, expansion, execution, and finalization. Errors are caught early via progressive sampling on small data subsets before running on full data. The system supports three execution strategies, twelve domain knowledge packs, and confidence-based early stopping. An interactive web interface lets users watch pipelines being built in real time, replay completed runs at any stage, and compare results across cases. On four benchmarks, ADP-MA reaches 90.6% on DSEval, 44.8% on KramaBench, 50.0% on DA-Code, and 70.0% on AgentBench, outperforming published single-agent baselines.
Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases remains a critical bottleneck for evidence-based medicine. While commercial black-box tools lack transparency, standard open-source RAG implementations frequently suffer from ``reasoning drift'' when handling complex, long-tail queries. We present DeepMed Search, a fully open-source, agentic platform designed for transparent medical deep research. Built on a high-performance Next.js architecture, DeepMed Search features a source-adaptive router that autonomously dispatches sub-queries to PubMed, web search, or local graph-based knowledge bases based on information density. Crucially, the platform integrates an introspective verification module, powered by a causal-consistent multi-agent debate framework, to validate retrieved evidence against diagnostic logic before synthesis. To demonstrate its robustness, we showcase DeepMed Search's ability to autonomously decompose high-difficulty rare disease queries, filter out confounding noise, and generate structured, citation-backed research reports in minutes. By open-sourcing this software, we provide the community with a robust infrastructure to democratize access to trustworthy, glass-box medical reasoning at a commercial-grade performance level, which is publicly available at: https://www.deepmedsearch.cloud and the demonstration video is available at: https://youtu.be/4U4aok8yLpk.