This paper presents the primary research challenges and key findings from the 15th International Automated Negotiating Agents Competition (ANAC 2025), one of the official competitions of IJCAI 2025. We focus on two critical domains: multideal negotiations and the development of agents capable of concurrent negotiation within complex supply chain management environments. Furthermore, this work analyzes the results of the competition and outlines strategic directions for future iterations.
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This demo paper describes the development of the AI Teaching & Learning Assistant, a modular Moodle plugin that leverages Retrieval-Augmented Generation (RAG) to deliver high-quality, highly selective answers strictly grounded in teachers’ materials. The system employs a dual-centric design, providing students with interactive, Socratic-based tutoring options and educators with a “human-in-the-loop” workspace for supervised content generation, e.g., quizzes, tasks, or short summaries. By grounding Large Language Model (LLM) responses in teacher-provided materials, the assistant addresses the risks of misinformation while encouraging deep conceptual mastery. Evaluation via the LLM-as-a-Judge framework and a preliminary user study confirms its effectiveness, achieving faithfulness scores up to 0.97 and a 4.00/5.00 recommendation rate. Demo Video: https://tinyurl.com/4zz4bcjn
As AI‑based decision‑support systems become increasingly widespread, methods aimed at improving the performance of human-AI teams are gaining attention. In recent years, explainable artificial intelligence (XAI) has received growing interest, as it provides methods to make the behavior of machine learning models more transparent and can help to identify errors and flaws, which is particularly important in safety critical domains such as medicine and law. However, explanations themselves can be misleading, inconsistent, or incorrect, which makes it essential to raise awareness of the possibilities and limitations of these methods. We introduce Lucyde, a web‑based demonstrator designed to help users explore, compare, and better understand XAI methods across different datasets, models, and configurations. Lucyde provides a curated collection of explanation techniques, enables side‑by‑side comparison of methods, and offers easy‑to‑understand supplementary information for different user groups. It also illustrates interactive machine learning workflows by allowing users to correct model outputs or explanations. Lucyde thereby fosters informed and reflective engagement with AI systems and their explanations.
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.
DeepL Voice is a real-time speech-to-speech translation system for global business communication, following a pragmatic incremental approach: developing a production-grade cascaded speech-to-speech-translation (S2ST) system, while exploring end-to-end solutions in parallel. The production system (launched November 2024) achieves competitive transcription quality through proprietary real-time ASR models and eliminates translation "flickering" via stable text streaming while maintaining low latency. Supporting 18 input languages and 30+ target languages, it offers DeepL Voice for Meetings (Microsoft Teams/Zoom integration), DeepL Voice for Conversations (mobile apps), as well as the DeepL API for Voice. Key features include customizable formality and glossary support for business-appropriate communication, with voice cloning TTS under development.
We present the Sparse ProtoPatient Demo, a publicly available interactive system for interpretable ICD-10 diagnosis prediction from clinical admission notes. The system is designed for clinicians in training, researchers, and educators exploring prototype-based diagnostic reasoning. The demo links predictions to learned prototypical patient representations and token-level evidence, allowing users to input custom text or select preset cases, inspect predicted ICD-10 codes, visualize label-wise saliency, retrieve supporting prototype notes, and compare alternative prototype cohorts. The demo provides a reproducible platform for interactive inspection of prototype-based clinical reasoning, enabling complementary opinion exploration, model auditing, and teaching of interpretable diagnosis prediction. The deployed model is trained on the publicly released CodiEsp corpus (1000 clinical notes, 955 ICD-10 labels) using a sparse multi-prototype architecture with five prototypes per label. We use the official machine-translated English CodiEsp-MT release to support English-language interaction. It achieves a macro-AUROC of 0.92 on a held-out test set and supports real-time interaction (300ms per query). The system is fully containerized for public research and educational use.
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.
High-resolution spatial fields are critical for local decision-making, yet many operational and scientific workflows produce coarse outputs due to computational limits. We present CrossRefine, a deployable microservice for cross-domain spatial super-resolution that enhances multi-channel spatial tiles without modifying upstream models. It is built around a unified, topography-conditioned adversarial UNet trained across geographically diverse regions to ensure robustness to heterogeneous terrains and domain shifts. Unlike region-specific enhancement models, the system generalizes across domains within a single architecture, balancing numerical fidelity and structural realism through a hybrid regression–adversarial objective. The service provides REST API endpoints for batch and streaming inference, supports mixed-precision, and offers per-tile diagnostics and confidence maps to promote safe deployment. In our demo, we show interactive refinement of coarse spatial inputs, side-by-side comparison with interpolation and non-adversarial baselines, and real-time profiling of latency and throughput on commodity hardware. CrossRefine illustrates how spatial super-resolution can be delivered as a practical AI microservice, enabling scalable refinement of existing computational workflows without requiring higher-resolution upstream simulations. Demonstration video: https://shorturl.at/lz2un
We introduce a novel neural ray tracing method designed for compact scene representations and real-time rendering. To compress the scene with minimal fidelity losses, we address the issue of limiting the search space for intersections. For each scene we first construct two lightweight proxy shells that tightly bound the original surface from inside and outside. While executing ray queries, we intersect the rays with the shells and extract the regions that potentially contain the intersection with the original surface. Extracted regions are passed to the small neural network to retrieve the exact intersection location. We implement our method as part of a GPU-accelerated hybrid path tracing pipeline. We demonstrate it running real-time rendering on a variety of scenes, achieving up to 300x memory reduction and surpassing existing compressed ray tracing techniques in memory-quality trade-off.
We introduce a web tool that puts the full image-to-text pipeline for Ancient Egyptian hieroglyphs inside a single annotation workspace. Instead of only producing a final transcription, our system exposes editable intermediate results so users can validate and correct the pipeline step by step. User edits are stored as image-aligned annotations, which supports both text analysis and dataset creation. Quantitative results indicate improved efficiency and output quality relative to a manual baseline. A demonstration video is available at Google Drive.
The integration of Large Language Model (LLM)-based code assistants into IDEs has boosted developer productivity, but cloud‑based solutions pose severe privacy risks for enterprises handling proprietary code. Existing on‑premise alternatives lack semantic code retrieval, multi‑GPU optimization, and rigorous productivity validation. We present an enterprise‑oriented, customizable IDE plugin that uses internally hosted LLMs, combining client‑side tiered retrieval (Personal CodeRAG with SiamBERT/CodeSage) and vLLM‑optimized inference on dedicated hardware. No code leaves the organization. Our benchmark of four inference frameworks shows vLLM and LMDeploy achieve $\approx$20 ms time‑to‑first‑token on LLaMA‑3‑8B, suitable for real‑time interaction. In a three‑month study with 100 developers, the plugin reduced mean cycle time by 15.4\%, with perceived productivity gains of 50-70\% for tasks like code explanation and unit test generation. This work provides the first systematic evaluation of on‑premise LLM‑assisted coding that quantifies both objective and subjective productivity impacts.
We present ElderMTL, a multi-task affect monitoring system designed for elderly care settings. The system simultaneously estimates Facial Action Units (FAUs), Valence-Arousal (VA) signals, and categorical emotions (FER) from video, capturing multiple layers of affective information. To improve sensitivity to subtle affective cues common in older adults, our approach incorporates age-conditioned physiological modeling, including baseline muscle adjustments and a dynamic AU co-activation graph. This enables the system to adapt to age-related changes in facial expression patterns, providing more reliable and interpretable emotion assessments. In a live demonstration, we showcase ElderMTL processing video streams, visualizing AU activations, affective state predictions, and interpretable insights that highlight age-specific affective dynamics. This work demonstrates that physiologically grounded, multi-task affective monitoring can provide meaningful, real-world support for elderly care.
Cities deploying autonomous vehicles face an urgent policy question: would the adoption of autonomous vehicles (AVs) improve the congestion rate or worsen it? What would be the optimal adoption rate to minimize the congestion rate? How would cautious AVs (Waymo-style) and aggressive AVs (Tesla "Mad Max'"-style) interact with human drivers and delivery robots on shared roads? We present UrbanMix, an interactive simulation platform that embeds cognitively diverse agents (human drivers, cautious AVs, aggressive AVs, and delivery robots) with distinct behavioral profiles inside a Simulation of Urban MObility (SUMO) framework of real urban road networks. Our LLM planner operates as an urban policy coordinator, setting traffic rules through a bounded action interface, while a regulation shield enforces infrastructure constraints. Our experiments reveal three key phenomena: (i) roads throughput may substantially drop with the increase in AVs adoption; (ii) an aggressive cascade, where runtime behavior switching modeling Tesla user-selectable "Mad Max" mode triggers up to 60 times increase in emergency braking events on a real Austin Downtown network; and (iii) a delivery bottleneck, showing 24-36% throughput reduction from slow robots. Results validated on synthetic and real data from Austin, TX, demonstrate that real network topology amplifies cascade effects by more than four times.
Coupled power-traffic simulations are valuable for studying EV charging stress and outage propagation, but existing tools typically require scripts and opaque configurations. We present LLM-to-Map, a demo system that exposes a real-time multi-domain simulator through structured LLM tool orchestration. Natural-language requests are mapped to typed tool calls that coordinate SUMO traffic simulation, PyPSA power-flow analysis, V2G actions, and map operations. The agent operates over a fixed tool schema and emits auditable execution logs in the UI so users can inspect every action and parameter. Tool execution is deterministic: each request resolves to explicit API calls, and unsafe or destructive actions can require confirmation. The tool layer provides JSON schemas and parameter validation, preventing arbitrary code execution and enabling reproducible runs. The agent receives live system state (loads, EV statistics, V2G status, time, temperature) to support context-aware multi-step commands; when an LLM is unavailable, a deterministic parser preserves the same tool interface. The backend synchronizes cross-domain events (EV charging demand and substation failures) and streams state updates to a 3D map interface. We describe the architecture, coupling and synchronization, and a demo workflow that showcases multi-step scenario control and reporting for non-experts without writing scripts. Demo video: https://youtu.be/CpEgCZPl_2g; code: https://github.com/MarouaneBenbrahim/Map-LLM.
Rare diseases are characterized by low prevalence and intricate pathogenesis, leading to highly heterogeneous clinical trajectories. The care of rare disease presents formidable challenges due to the requirement for highly specialized expertise and experiences. Existing methods are typically tailored for isolated rare disease scenarios (e.g., diagnostic tasks, medication recommendations), which lacks a comprehensive perspective of the entire care process. Inspired by recent studies of agent skills, we propose RareDASH, a multi-agent system (MAS) featuring dynamic workflow orchestration designed to provide a comprehensive solution for the full life-cycle of rare disease care. Our framework is inherently patient-centric, enhancing rare disease discovery capabilities through proactive inquiry and information elicitation directly from the patients. Furthermore, we implement diverse agent memory to optimize both the accuracy and efficiency of the multi-agent collaboration. Finally, an online auditing module is integrated into the system to monitor and mitigate the hallucinations, ensuring the reliability of clinical outputs. The work sheds light on the feasibility of leveraging MAS in holistic rare disease care.
Automated identification of killer whale call types from continuous acoustic recordings is essential for scalable population monitoring, yet existing general-purpose frameworks such as ANIMAL-SPOT suffer from suboptimal spectrogram resolution and lack strategies tailored to the spectral-temporal characteristics of killer whale vocalizations. We identify two key limitations of the ANIMAL-SPOT framework: (1)~no possibility to choose different architecture of CNN backbone, and (2)~the absence of regularization and class-balancing techniques limits generalization across call types with varying abundance. To address these issues, we propose a framework that combines optimized STFT parameters (FFT size 1024, hop length 172) with label smoothing and targeted oversampling, evaluated across three CNN backbones and five segment lengths. On a dataset of 12 killer whale vocalization classes from Avacha Gulf, Russia, our best configuration (ResNet-18 with 1200\,ms segments) achieves \textbf{97.1\%} segment-level accuracy, compared to \textbf{96.2\%} for the ANIMAL-SPOT baseline --- a relative error reduction of 23.7\%. We further present an interactive demonstration system for uploading recordings and obtaining time-resolved call-type predictions, enabling rapid analysis of passive acoustic monitoring data. Demo video link: https://drive.google.com/file/d/1ITA_52WdnAcyg7zRjIIshutjl6Lp_um1/view?usp=drive_link Presentation slides: https://drive.google.com/file/d/1sd-JH1R5YPa9iRtsbRO-OZxY8-fyglsO/view?usp=drive_link
Securing research funding remains fragmented and time-consuming: researchers must navigate separate databases across dozens of agencies while simultaneously drafting competitive proposals. We present GRAIL, a web-based platform that unifies grant discovery and proposal writing through conversational AI. Users describe their research interests in natural language to explore opportunities from a unified index of 11.8K U.S. federal and nonprofit grant opportunities; within the document editor, integrated AI assistance supports real-time proposal revision and refinement. The system runs in any modern browser without installation. Conference attendees are invited to interact with the live system at the demo booth, explore the grant discovery and writing assistance workflows, and provide feedback on the user experience.
We present SparseDR, a novel differentiable rendering algorithm designed for sparse representations based on Signed Distance Fields (SDF). We leverage the Sparse Brick Set representation and propose an adaptation of redistancing for sparse SDFs. This enables SparseDR to surpass existing works in accuracy of surface reconstruction by increasing the effective resolution of the SDF representation. SparseDR is efficiently implemented in C++ and Vulkan, achieving several times shorter reconstruction time than other methods.
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.
Determining when to leave for the airport is a complex problem shaped by flight delay risk, traffic, weather, airport congestion, and passenger-specific constraints. Existing services rely on isolated delay estimates or simple travel time calculations, failing to capture real-time context. We propose ORBIT, a decision-support system that generates personalized leave-by recommendations by integrating predictive models with real-time operational signals. The system combines user input normalization, real-time data acquisition, Transformer-based delay prediction, and LLM-based reasoning to jointly account for statistical forecasts and dynamic factors like congestion, previous-leg propagation, weather, traffic, and airport processing times. Instead of a standalone delay estimate, ORBIT produces an actionable and interpretable departure plan. We implement ORBIT as an interactive system and demonstrate its applicability. A video demo is available at https://youtu.be/fZX42jceIM4.