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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

IJCAI 2026

Neuro-Symbolic Logical Reasoning with Textual Entailment

Zacchary Sadeddine, Fabian Suchanek

Large Language Models can use logical deduction to answer natural language questions, but they remain black-boxes with potentially erroneous chains-of-thought. In this paper, we adapt VANESSA, a neuro-symbolic method for chain-of-thought verification, to reasoning-based question answering. VANESSA combines a logical reasoner with a neural textual entailment model to handle phrasing variations. Building on VANESSA, we develop a transparent, logic-based approach to answer natural language questions even with phrase variations. Our experiments across various datasets show our method is competitive with the state of the art, while also delivering proof trees for its answers. A demo interface allows users to interact with the system.

IJCAI 2026

ElderMTL: Multi-Task Affect Monitoring for Elderly Care

Maria Razzhivina, Shahane Tigranyan, Aram Avetisyan, Ilya Makarov, Andrey Savchenko

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.

IJCAI 2026

Visualizing Deep Agents in Long-Horizon Tasks: Towards Explainable and Trustworthy Agentic AI

Amirkia Rafiei Oskooei, Mehmet S. Aktas

The transition from prompt-based Large Language Models (LLMs) to autonomous Deep Agents has enabled the automation of long-horizon tasks. However, as these agents adopt hierarchical architectures with nested tool usage, they suffer from significant opacity. Existing linear tracing tools fail to capture the multi-dimensional complexity of parallel sub-agent execution, hindering both debugging and user trust. We propose a general-purpose observability framework that decomposes agent execution into four distinct visualization dimensions: Temporal, Cognitive, Hierarchical, and Spatial. We validate this framework through RepoLearn, an open-source workbench for automated codebase comprehension. Our user study demonstrates that this multi-dimensional approach reduces the Time-to-Insight (TTI) for complex behavioral analysis by 56% and significantly lowers cognitive load (NASA-TLX) compared to state-of-the-art linear traces. The source code is available at https://github.com/amirkiarafiei/repo-learn and the demo at https://www.youtube.com/watch?v=s3U6E9o94gk.

IJCAI 2026

AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

Suraj Prasai, Kangning Cui, Rongkun Zhu, Sarra Alqahtani, Ying Zhang, Victor Paúl Pauca, Miles R. Silman, Fan Yang

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.

IJCAI 2026

A Privacy-Preserving Intelligent Assistant for Clinical Psychology Practice

Aaron Pico, Joaquin Taverner, Emilio Vivancos, Ana Garcia-Fornes, Vicent Botti

This paper describes a fully local, privacy-preserving intelligent system designed to assist in clinical psychology practice. The system automatically transcribes therapy sessions performing speaker attribution. Beyond transcription, the tool enhances clinical reasoning by detecting cognitive distortions and emotional patterns utilizing specialized deep learning classifiers and Large Language Models (LLMs). By guiding an LLM locally through a multi-step analysis process, the assistant synthesizes the enriched data and generates a series of analysis reports and clinical documentation of the session. As a result, the assistant reduces the administrative burden on professionals while preserving privacy with an edge computing approach in which the data never leaves the therapist's device. Finally, the assistant uses human-in-the-loop validation so that the professional always remains in control, ensuring clinical accuracy and trust.

IJCAI 2026

From Surface Learning to Deep Understanding: A Grounded AI Tutoring System for Moodle

Anna Ostrowska, Michał Kukla, Gabriela Majstrak, Jan Opala, Sebastian Pergała, Jan Skwarek, Anna Wróblewska

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

IJCAI 2026

Double Bounded Neural Ray Queries

Alexander Nikolaev, Nikolay Mozokhin, Roman Rodionov, Vladimir Frolov

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.

IJCAI 2026

Optimizing Spectrogram Resolution and Training Strategies for Real-Time Killer Whale Call Type Classification

Vladislav Naumov, Iaroslav Sheipak, Yuriy Ivanov, Ilya Makarov

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 97.1% segment-level accuracy, compared to 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.

IJCAI 2026

Wavelength.AI: Extending the Collaborative Game Wavelength as a Testbed for Studying Shared Understanding in Human–Agent Collaboration

Katelyn Morrison, Gabriel Enrique Gonzalez, Zahra Ashktorab, Matt Riemer, Andrew Anderson, Djallel Bouneffouf, Justin D. Weisz

AI's increasing role as a personal agent assisting knowledge workers in everyday tasks underscores the need to investigate how to help human–agent teams build a shared understanding. We extend the collaborative "mind-reading" game Wavelength to include an AI teammate, presenting the first demonstration of an LLM capable of playing this game. Based on our agent–agent play experiments, we developed Wavelength.AI, which implements two strategies to support shared understanding: an initial team grounding conversation and post-game reflective explanations. We interpret higher team scores as evidence for better shared understanding in a preliminary user study with 24 human–AI teams. Our findings reveal that Wavelength.AI can help researchers evaluate and design different strategies to shape human-agent teams' shared understanding. Human players can see if they are on the same wavelength with AI and view our demo video today at https://play-wavelength-ai.com.

IJCAI 2026

DeepLog: A Software Framework for Modular Neurosymbolic AI

Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen, Rik Adriaensen, Lucas Van Praet, Luc De Raedt, Giuseppe Marra

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

IJCAI 2026

Making Weak Supervision Interactive: Exploring Transfer from Sound Libraries to Passive Acoustic Monitoring Data

Novruz Mammadli, Rida Saghir, Kanwar Ammar Ali, Prathmesh Doddanawar, Thiago S. Gouvêa, Daniel Sonntag

Passive Acoustic Monitoring (PAM), an increasingly popular method for wildlife monitoring, generates large volumes of data whose analysis depends on instance-level annotations that are costly to obtain. Archival sound collections provide weak labels that lack temporal localisation. In prior work, we demonstrated that Multiple Instance Learning (MIL) can extract approximate event locations from weakly labelled PAM data, suggesting it may be applied to sound collection data. This demo operationalizes that approach within an interactive workflow that connects weakly annotated sound collections to downstream PAM deployment. The system supports configurable MIL-based localisation, lightweight interactive refinement, and transfer to an independent PAM dataset. We carried out a preliminary evaluation with an actual sound library from a museum collection and a benchmark PAM dataset. Results confirm that weakly annotated sound collections can serve as a viable training signal for downstream PAM detection and illustrate differences between alternative MIL instantiations under real transfer conditions. (Video available at https://cst.dfki.de/projects-weak-supervision-demo)

IJCAI 2026

DeepMed Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification

Maolin Liu, Fanyu Xu, Ruoqing Xu, Jiahang Zhang, Hao Wang, Rui Wang

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.

IJCAI 2026

A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework

Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag

Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative, sequence-to-sequence framework that performs all event extraction subtasks jointly and supports both end-to-end and pipeline configurations. We fine-tune pre-trained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large, evolving label spaces. Cross-domain experiments show strong, robust performance across datasets, demonstrating a scalable solution for real-world event extraction. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports inspection of different configurations and facilitates cross-domain comparisons.

IJCAI 2026

Intent Hub: A Self-Healing Semantic Agent Routing System for Resolving Overlap in Agentic Systems

Chenrui Liang, Peng Xu, Xinyuan Liu

Semantic overlap challenges accurate agent routing in large-scale agentic systems. We present Intent Hub, a self-healing semantic agent routing system that combines offline asynchronous HITL repair with online Dual Filtering. LLM-generated positive and adversarial negative utterances help construct explicit decision boundaries, enabling interpretable millisecond-level routing. Intent Hub further supports interactive semantic debugging, allowing developers to diagnose conflicts, repair rules, and observe online routing changes.

IJCAI 2026

TraceBrain: An Open-Source Framework for Agentic Trace Management

Quy Minh Le, Oscar Cao, Hoang Quoc Viet Pham, Hoang Thanh Lam, Hoang D. Nguyen

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.

IJCAI 2026

Low-Latency Real-Time Audio Game Commentary System via LLM-Based Parallel Text Generation

Ryota Kawamatsu, Anum Afzal, Yuki Saito, Shinnosuke Takamichi, Graham Neubig, Katsuhito Sudoh, Hiroya Takamura, Tatsuya Ishigaki

We present a low-latency real-time audio game commentary system that generates spoken commentary directly from live gameplay video. In this end-to-end setting, a key bottleneck is accumulated waiting time; conventional pipelines capture frames, generate text, and synthesize speech sequentially for each utterance, and do not request the next generation until speech playback has completed. This strict sequentiality causes long and unnatural silence between utterances. To address this latency bottleneck, our system runs text generation in parallel with speech playback and buffers multiple candidate utterances ahead of time, enabling immediate synthesis at playback boundaries. Experiments on fast-paced game videos show that our parallel design reduces the mean inter-utterance silence from 9.6 seconds to 0.3 seconds compared to sequential baselines. It also improves similarity to professional speaking--silence timing patterns by over 40 %, and a user study with 120 experienced game players confirms significantly improved perceived speaking rhythm. Our demo video is available at: https://youtu.be/pmrRUlvav8M.

IJCAI 2026

ORBIT: Optimal Recommendation Framework for Boarding with Interpretable Timelines

Joonseong Kang, Seung Ha Hwang, Jaehun Bang, Jiyoung Ko, Subeen Park, Jeffrey Gennari

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.

IJCAI 2026

GEV: Statically Correct and Programmable Knowledge Graph Updates

Eduard Kamburjan, Shqiponja Ahmetaj, Chinmayi Prabhu Prasad Baramashetru, Paolo Pareti

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.

IJCAI 2026

DataCube: A Video Retrieval Platform via Natural Language Semantic Profiling

Yiming Ju, Hanyu Zhao, Quanyue Ma, Donglin Hao, Chenwei Wu, Ming Li, Songjing Wang, Tengfei Pan

Large-scale video repositories are increasingly available for modern video understanding and generation tasks. However, transforming raw videos into high-quality, task-specific datasets remains costly and inefficient. We present DataCube, an intelligent platform for automatic video processing, multi-dimensional profiling, and query-driven retrieval. DataCube constructs structured semantic representations of video clips and supports hybrid retrieval with neural re-ranking and deep semantic matching. Through an interactive web interface, users can efficiently construct customized video subsets from massive repositories for training, analysis, and evaluation, and build searchable systems over their own private video collections. The system is publicly accessible at https://datacube.baai.ac.cn/. Demo Video: https://youtu.be/L7bKfPBm2tU

IJCAI 2026

Lucyde: A Demonstrator for Explainable Artificial Intelligence and Interactive Machine Learning

Eda Ismail-Tsaous, Ute Schmid

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.

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