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IJCAI 2026

Interactive Open-Set Semantic Mapping with a 3D Scene Graph Backend

Felix Igelbrink, Lennart Niecksch, Martin Günther, Marian Renz, Oscar Lima, Martin Atzmueller

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.

IJCAI 2026

LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation

Samy Haffoudhi, Nikola Dobričić, Fabian Suchanek, Nils Holzenberger

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.

IJCAI 2026

Interactive System for Reducing Error Propagation in Multi-Stage Ancient Egyptian Text Analysis

Maksim Golyadkin, Innokentiy Humonen, Ilya Makarov

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.

IJCAI 2026

Anti-Slavery Intelligence (ASI): An AI-Powered Tool for Modern Slavery Compliance Analysis and Remediation

Mahmoud Gad, Abdessalam Elhabbash, Steven Young

We present Anti-Slavery Intelligence (ASI), a deployed AI system that analyses corporate modern slavery statements to identify compliance gaps and generate prioritised remediation advice. ASI orchestrates a multi-model pipeline: Gemini-2.5-Flash for vision-enhanced PDF parsing and structured compliance scoring against 48 expert-defined criteria, and Gemini-2.5-Pro for synthesising company-specific, timeline-based recommendations. A benchmarking engine compares each statement against industry and FTSE index averages across 11 sectors. Evaluation on 95 expert-annotated statements yields an F1-score of 0.86 and recall of 0.94, reducing time to generate an initial compliance assessment from several hours to approximately five minutes per document. ASI is publicly available at https://www.antislaveryintelligence.co.uk/ with free access for academics and NGOs. A demonstration video is at https://youtu.be/DNhdEGrRwzg.

IJCAI 2026

Sparse ProtoPatient: Interactive Multi-Prototype Explanations for Clinical Diagnosis Prediction

Conor Fallon, Bogdan Kostić, Betty van Aken, Jens-Michalis Papaioannou, Alexei Figueroa, Keno Bressem, Alexander Löser

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.

IJCAI 2026

DeepL Voice: Real-Time Speech-to-Speech Translation

Johannes Ernesti, Peter Kaiser, Jonas Heinze, Elnaz Shafaei-Bajestan, Kristina Geißler, Weiyue Wang, Johannes Beck, Sascha Brinker, Thorben Finke

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.

IJCAI 2026

PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions

Greta Damo, Stéphane Petiot, Elena Cabrio, Serena Villata

The increasing volume of hate speech on online platforms poses significant societal challenges. While the Natural Language Processing community has developed effective methods to automatically detect the presence of hate speech, responses to it, called counter-speech, are still an open challenge. We present PEACE 2.0, a novel tool that, besides analysing and explaining why a message is considered hateful or not, also generates a response to it. More specifically, PEACE 2.0 has three main new functionalities: leveraging a Retrieval-Augmented Generation (RAG) pipeline i) to ground HS explanations into evidence and facts, ii) to automatically generate evidence-grounded counter-speech, and iii) exploring the characteristics of counter-speech replies. By integrating these capabilities, PEACE 2.0 enables in-depth analysis and response generation for both explicit and implicit hateful messages.

IJCAI 2026

RUVA: Personalized Transparent On-Device Graph Reasoning

Gabriele Conte, Alessio Mattiace, Gianni Carmosino, Potito Aghilar, Giovanni Servedio, Francesco Musicco, Vito Walter Anelli, Tommaso Di Noia, Francesco Maria Donini

The Personal AI landscape is currently dominated by "Black Box" Retrieval-Augmented Generation. While standard vector databases offer statistical matching, they suffer from a fundamental lack of accountability: when an AI hallucinates or retrieves sensitive data, the user cannot inspect the cause nor correct the error. Worse, "deleting" a concept from a vector space is mathematically imprecise, leaving behind probabilistic "ghosts" that violate true privacy. We propose Ruva, the first "Glass Box" architecture designed for Human-in-the-Loop Memory Curation. Ruva grounds Personal AI in a Personal Knowledge Graph, enabling users to inspect what the AI knows and to perform precise redaction of specific facts. By shifting the paradigm from Vector Matching to Graph Reasoning, Ruva ensures the "Right to be Forgotten." Users are the editors of their own lives; Ruva hands them the pen.

IJCAI 2026

SparseDR: Differentiable Rendering of Sparse Signed Distance Fields

Alexey Budak, Albert Garifullin, Vladimir Frolov

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.

IJCAI 2026

LLM-to-Map: Transparent Conversational Tool Orchestration for Real-Time Multi-Domain Simulation

Marouane Benbrahim, Kavya Gautam, Zonghan Zhang, Zhiqian Chen

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.

IJCAI 2026

[COMP25] The Automated Negotiating Agents Competition (ANAC) 2025 Challenges and Results

Reyhan Aydoğan, Tim Baarslag, Tamara C.P. Florijn, Katsuhide Fujita, Catholijn M. Jonker, Yasser Mohammad

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.

IJCAI 2026

eNNcode: Optimization-Based Analysis of Neural Networks

Muhammad Sabri Farag Atallah, Lukas Dankwart, Daniel Neider, Mustafa Yalçıner

The increasing use of neural networks (NNs) in high-stakes decision-making requires rigorous analysis to ensure safety, fairness, and explainability. Formal verification tools for neural networks typically focus on determining the satisfiability of properties such as safety or fairness. However, many fairness and explainability tasks go beyond satisfiability and instead rely on arbitrary optimization objectives. To address such problems, neural networks are often encoded as mixed-integer linear optimization (MILO) problems with linear objectives. In practice, these encodings are usually implemented in an ad-hoc manner, limiting comparability across works, reducing transparency, and increasing implementation effort. We address this gap by introducing eNNcode, a user-friendly PyPI library that converts any piecewise-linear neural network in Open Neural Network Exchange (ONNX) format into a MILO instance. The library supports arbitrary constraints on input and output nodes, as well as user-defined optimization objectives. Our experiments show that eNNcode achieves performance comparable to existing libraries, despite its simplicity and ease of use. Overall, eNNcode facilitates reproducible and standardized optimization-based analysis of neural networks.

IJCAI 2026

An Automated Maintenance Plant for Highways

N'zebo Richard Anvo, Alwyn Mathew, Lavindra de Silva, Damian Palin, Jie Xu, Samuel Schaefer, Abir Al-Tabbaa, Fumiya Iida, Ioannis Brilakis

The Digital Roads project at Cambridge University is leveraging digitalisation, automation, and low-carbon materials to build an Automated Maintenance Plant (AMP) for UK road networks, aimed at minimising repair times to reduce congestion, improving safety, and contributing to the UK’s net-zero goals through faster, more accurate, and efficient road maintenance.

IJCAI 2026

Neuro-Symbolic AI for Evidence-Based Renewable Energy Planning in Sub-Saharan Africa

Janice Anta Zebaze, Azanzi Jiomekong, Germaine Djuidje Kenmoé, Maria Esther Vidal

Renewable energy is a key alternative to fossil fuels and their environmental harms. However, sub-Saharan African countries remain behind in their implementation, due to poor investment in the appropriate resource, among other causes. This information is dispersed as unstructured knowledge across scientific papers, making the task manually demanding for decision and policy makers. In this work, we combine the strength of Large Language Models (LLMs) and structured knowledge (Knowledge Graphs) in a neuro-symbolic framework to tackle this issue. We implement this approach in the hydropower field, building a question-answering system that uses Retrieval Augmented Generation (RAG) on a knowledge graph. Using LLMs such as LLaMA 3.1 8B, LLaMA 3.3 70B, LLaMA 4 Scout 17B, and Kimi K2, we observe an improvement in exact match of 48 − 60% across all models. These results support the ability of this framework to participate in evidence-based energy planning in developing countries.

IJCAI 2026

Personalizing Long-Term Interactions with LLM-Based Agents

Rebecca Westhäußer

LLMs increasingly serve as the control unit of AI agents. However, current systems struggle to support personalized interactions, as they lack effective mechanisms to remember and reuse user-specific information across sessions. To address this, our research is centered around how memory augmentation and agentic decision-making can enable personalization in long-term interactions with LLM-based agents. As a first step, we proposed CAIM, a memory framework inspired by cognitive AI principles that aims to simulate human thought processes to create more adaptable systems. Building on this, we extended CAIM with an agentic workflow and user-specific memory modules to support personalized behavior beyond static retrieval-based approaches. We conducted a user study to evaluate perceived personalization, comparing our memory-based approach to a RAG baseline. Our ongoing research focuses on how personalization can be measured without user studies, which are costly and hard to scale. The long-term goal of our research is to establish foundations for designing and evaluating personalized LLM-based systems.

IJCAI 2026

Interpreting Neural Receivers: Mechanistic and Data Attribution Approaches

Marko Tuononen

This research studies interpretability methods for deep neural network--based radio receivers in future wireless communication systems. The work focuses on representation-level analysis and training data attribution techniques that reveal how models encode physical-layer channel conditions and how training data influence receiver behavior. These insights enable validation, adaptation, and more trustworthy deployment of data-driven communication systems under realistic channel conditions, while providing deeper understanding of model behavior.

IJCAI 2026

Novel Applications of Large Language Models in Computational Social Choice

Nguyen Thach

My research initiates the use of large language models (LLMs) to automate the design of effective algorithms for challenging problems in computational social choice, namely participatory budgeting (PB) and facility location mechanism design. To this end, I propose two LLM-based frameworks that respectively generate interpretable PB rules and mechanisms fulfilling competing objectives, i.e., optimality vs. fairness or strategyproofness. Future directions include extending these frameworks to achieve provable guarantees and addressing other relevant areas such as fair division.

IJCAI 2026

Towards Reliable Multimodal Clinical Decision Support: From Quality-Aware Fusion to Patient Digital Twins

Linpeng Sun

Multimodal deep learning for clinical decision support frequently fails in real-world deployments due to severe data missingness and sensor noise. Standard fusion and Mixture-of-Experts (MoE) architectures assume all modalities are uniformly informative, causing routing collapse and miscalibrated confidence when faced with corrupted inputs. My doctoral research addresses this fragility by shifting the paradigm from generative imputation to uncertainty-guided abstention. As a foundational step, I introduce QA-MoE (Quality-Aware MoE), which explicitly decouples reliability estimation from the routing process. By quantifying epistemic uncertainty, the model dynamically filters noisy modalities before fusion, ensuring sparse and stable inference. Building on this static robustness, my ongoing and future research extends to longitudinal and structured clinical data. I am developing PathFlow-EM, which integrates Expectation-Maximization to handle hidden variable distributions in unevenly sampled time-series, and Uncertainty-Guided Dynamic Topological Fusion (U-DTF), which uses topological data analysis to gate features based on evolving patient graphs. Ultimately, this research trajectory aims to deliver robust, calibration-aware architectures that form the computational basis for reliable Patient Digital Twins.

IJCAI 2026

Context-Adaptive World Models for Visual Out-of-Distribution Generalization

Shubham Subhnil

A vision-based robot can be faced with changing friction, gravity, or motor behavior, whose effects are observable only from pixels mid-episode. Visual model-based reinforcement learning struggles in such settings because most world models assume the latent factors driving these changes stay fixed during interaction, or fold them into a single context embedding. My thesis is developing a context-adaptive Mamba-based world model that infers K context vectors from short pixel-trajectory windows. Each vector steers a disjoint slice of the Mamba backbone through block-diagonal feature-wise linear modulation (FiLM), so distinct dynamics signals travel along parallel pathways rather than collapse into a single channel. The policy trains on imagined rollouts conditioned on the inferred context, and I show that its suboptimality is bounded by √K times the context-inference error along a Lipschitz chain through FiLM, the SSM, and the decoder. Evaluation spans within-episode shifts on DeepMind Control (Walker, Quadruped) and mode/difficulty switching on Atari, against an inferred-context baseline and a privileged-context oracle.

IJCAI 2026

Toward Trustworthy Foundation Models: Mitigating OOD Failure in Adaptation and Deployment

Ping Song

Foundation models’ versatile generalization has driven their broad adoption, yet their deployment is constrained by critical gaps in trustworthiness. First, we conduct a survey to present a comprehensive analysis of what constitute trustworthy foundation model and examined approaches for each requirement from a lifecycle perspective. We identify out-of-distribution (OOD) robustness as the primary technical bottleneck, where performance collapse under distribution shifts invalidates other pillars like fairness and accountability. The nature of this vulnerability is two-fold: first, a reliance on spurious background cues during inference, and second, dimensional collapse in the representation space during fine-tuning. This research aims to mitigate these failures by developing mechanistic insights and algorithmic solutions during foundation model adaptation and deployment phases of the lifecycle.

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