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IJCAI 2026

Real-Time Multi-Robot Motion Planning with Safe-Interval Search and Learning-Guided Repair

Rajat Kumar, Kristin Predeck, Ken Meszaros, Trevor Dardik

Motion planning among multiple robots in a shared space is a fundamental yet computationally challenging problem in robotics, with applications ranging from warehouse automation to autonomous fleets. In this work, we introduce a fast, scalable motion planner that achieves real-time, collision-free trajectory planning via a two-staged algorithm combining deterministic search-based planning with machine learning-driven conflict resolution. We present a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions. We added a second layer of ML-guided Large Neighborhood Search (LNS) procedure to our SIPP-PP algorithm for improving success rates in highly congested environments via intelligent selection of conflict resolution actions. The result is a planning system that generates collision-free paths for multiple robots in complex environments within tens of milliseconds. For example, compared to recent advanced learning-based methods such as diffusion planners, our planner is two-to-three orders of magnitude faster. Our work demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.

IJCAI 2026

FILD-Nav:Vision-and-Language Navigation with Instruction Landmark Features in Continuous Environments

Chuangye Hu, Lulu Liu, Huaiwei Si, Yawen Zhao, Nan Ding

Vision-and-language navigation (VLN) requires agents to follow natural language instructions to navigate autonomously in continuous environments. However, existing approaches often lack high-level semantic guidance in waypoint prediction and explicit language–landmark alignment in cross-modal planning. To address these limitations, we propose FILD-Nav, a vision-and-language navigation framework that integrates instruction landmark features. FILD-Nav extracts task-relevant landmarks from instructions and incorporates landmark semantics into both waypoint prediction and topological planning. Specifically, landmark-guided waypoint prediction improves waypoint relevance, while landmark-enhanced cross-modal planning enables more effective long-horizon navigation. Extensive experiments on the VLN-CE benchmark demonstrate that FILD-Nav consistently outperforms prior methods, achieving improvements of 2% in Success Rate (SR), 3% in Success weighted by Path Length (SPL), and 7% in Oracle Success Rate (OSR), particularly in unseen environments.

IJCAI 2026

PECHC: Robust Tactile Grasping Stabilization in Vision-Denied Peripersonal Space

Changlin Chen, Sisheng Chen, Hang Zhang, Xianglai Zhou, Zhen Tian, Weitao Liu, Feng-Qi Cui, Erbao Dong, Wenjing Chen

In the final "Last-Centimeter" phase of manipulation, where visual occlusion or calibration errors render vision unreliable, robots often suffer high failure rates due to local pose uncertainty and simulation dynamics deviations. To address these issues, this paper proposes the PECHC (Physics-Evolving Cascade Constraint and Human-Correction) algorithm. To rigorously isolate the contribution of tactile feedback in multi-finger coordination, we adopt a decoupled control strategy that focuses on grasp stabilization within the hand's workspace, acting as a fail-safe reflex. The core of our approach is Hybrid Correction Imitation Learning (HCIL), which establishes a "failure-triggered" human-machine mechanism to efficiently resolve the "model gap" via sparse expert corrections. To ensure sample efficiency and baseline performance, we introduce two supporting modules: Cascaded Constraint Scheduling (CCS) addresses the "geometric gap" by enforcing physically plausible behavioral constraints (geometric approach, force closure, and dynamic stability), while Temporal Heterogeneous Distillation (THED) resolves the "physical gap" by enabling implicit system identification from tactile history. Experiments demonstrate that PECHC achieves a 97.3% real-robot success rate on 150 objects from the Visual Dexterity Dataset under fully autonomous testing, where one object is used for one-time HCIL calibration and the remaining 149 objects are evaluated without further intervention. Compared to a standard Sim-to-Real reinforcement learning baseline (Vanilla PPO with Domain Randomization), PECHC delivers a significant performance improvement (+42.8%) and exhibits human-like force modulation capabilities for fragile objects.

IJCAI 2026

Operationalising Normative Rules in Autonomous Robotic Systems Through Context-Oriented Programming

Roberto Casadei, Martina De Sanctis, Gianluca Filippone, Sara Pettinari, Gian Luca Scoccia, Nicolas Troquard

The increasing sensitivity to human aspects in autonomous systems engineering calls for principled approaches to embed ethical and normative concerns into their behaviour. Indeed, recent research has focused on expressing and validating sets of social, legal, ethical, empathetic, and cultural (SLEEC) concerns as rules. However, existing work is limited to rule specification or verification, leaving the problem of semantic-preserving operationalisation of ethical rules in autonomous systems largely unaddressed. For this purpose, we provide an operational solution for ethical-aware autonomous systems, applied to the realm of multi-service robots. Specifically, we devise a principled approach, named CO-SLEEC (Context-Oriented SLEEC), connecting the normative setting of SLEEC rules to context-oriented programming (COP). CO-SLEEC enables runtime adaptation while preserving the semantics of SLEEC rules during robot task execution. It features two reusable Python libraries for (i) parsing SLEEC rules into contextual elements for operationalising them, and (ii) connecting the operational model to the Robot Operating System (ROS), respectively. We evaluate our implementation for correctness, efficiency, and maintainability over multi-service assistive robot scenarios.

IJCAI 2026

TeNet: Text-to-Network for Compact Policy Synthesis

Ariyan Bighashdel, Kevin Sebastian Luck

Robots that follow natural-language instructions often either plan at a high level using hand-designed interfaces or rely on large end-to-end models that are difficult to deploy for real-time control. We propose TeNet (Text-to-Network), a framework for instantiating compact, task-specific robot policies directly from natural language descriptions. TeNet conditions a hypernetwork on text embeddings produced by a pretrained large language model (LLM) to generate a fully executable policy, which then operates solely on low-dimensional state inputs at high control frequencies. By using the language only once at the policy instantiation time, TeNet inherits the general knowledge and paraphrasing robustness of pretrained LLMs while remaining lightweight and efficient at execution time. To improve generalization, we optionally ground language in behavior during training by aligning text embeddings with demonstrated actions, while requiring no demonstrations at inference time. Experiments on MuJoCo and Meta-World benchmarks show that TeNet produces policies that are orders of magnitude smaller than sequence-based baselines, while achieving strong performance in both multi-task and meta-learning settings and supporting high-frequency control. These results show that text-conditioned hypernetworks offer a practical way to build compact, language-driven controllers for ressource-constrained robot control tasks with real-time requirements.

IJCAI 2026

DART: Navigating Last-Mile Heterogeneity in Instant Delivery via Distribution-Adaptive Splines

Hao Xiong, Yang Gao, Haiyong Luo, Fang Zhao, Dan Luo

On-demand delivery platforms rely on Travel Time Estimation (TTE) to balance courier earnings and overdue risks. In collaboration with one of China's largest platforms, we address a critical "Fairness Gap" in TTE: current systems fail to capture complex delivery patterns in GNSS-denied environments, subjecting couriers handling high concurrent order volumes to disproportionate pressure due to overdue deliveries. Analyzing 1.27 million real-world trajectories, we attribute this bias to unique challenges in GNSS-denied scenarios: distributional heterogeneity, structural heterogeneity, and contextual uncertainty. To bridge this gap, we propose DART (Distribution-Adaptive Robust Timing). DART incorporates a Learnable Adaptive Spline (LAS) encoder with a gradient-driven knot migration mechanism to enhance non-linear expressiveness for outliers, significantly improving long-tail accuracy. Furthermore, a Spatio-Temporal Transition Graph (STTG) reconstructs the latent topology by integrating sequence semantics, such as Wi-Fi-sensed arrival merchant timestamps. At the same time, a Distribution Gating Mechanism characterizes delivery time distributions under distinct contexts. Through extensive experiments and large-scale online A/B testing, DART not only reduces MAE by 14.0% in complex environments but also decreases the Order Overdue Rate by 1.7% (saving $24,000 daily), demonstrating how AI effectively reconciles operational efficiency with labor fairness.

IJCAI 2026

Rule-Bottleneck RL: Learning to Decide and Explain for Sequential Resource Allocation via LLM Agents in Public Health

Guojun Xiong, Mauricio Tec, Haichuan Wang, Francesca Dominici, Joseph Ngonzi, Adeline Boatin, Milind Tambe

Reducing preventable maternal mortality remains a global health priority. Under Sustainable Development Goal (SDG) target 3.1, the WHO emphasizes timely and equitable allocation of limited maternal health resources. Motivated by Department of Obstetrics and Gynecology at several important hospitals in Uganda and Ghana, we study the problem of sequential allocation of wearable vital sign monitoring devices among maternal mothers. While deep reinforcement learning (RL) has shown promise for sequential resource allocation, its limited interpretability hinders adoption in such high-stakes settings. In contrast, large language model (LLM) agents provide human-readable reasoning but often struggle with effective long-term decision making. To bridge this gap, we introduce Rule-Bottleneck RL (RBRL), the first LLM agent framework for resource allocation problems that jointly optimizes language-based decision policy and explainability. At each step within RBRL, an LLM first generates candidate rules---language statements capturing decision priorities tailored to the current state. RL then optimizes rule selection to maximize environmental rewards and explainability, with the LLM acting as a judge. Finally, an LLM chooses the action (optimal allocation) based on the rule. We provide conditions for RBRL performance guarantees as well as the finite-horizon evaluation gap of the learned RBRL policy. Experiments in maternal health show that RBRL outperforms baseline LLM agents and approaches the performance of deep RL, while producing clearer, policy-relevant explanations. Human evaluations further confirm improved trust and usability, demonstrating RBRL as a practical AI approach aligned with SDG target 3.1.

IJCAI 2026

UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

Yue Wang, Djellel Difallah, Areg Karapetyan, Samer Madanat

Spatio-temporal traffic forecasting is a core component of intelligent transportation systems, supporting various downstream tasks such as signal control and network-level traffic management. In real-world deployments, forecasting models must operate under structural and observational uncertainties, conditions that are rarely considered in model design. Recent approaches achieve strong short-term predictive performance by tightly coupling spatial and temporal modeling, often at the cost of increased complexity and limited modularity. In contrast, efficient time-series models capture long-range temporal dependencies without relying on explicit network structure. We propose UniST-Pred, a unified spatio-temporal forecasting framework that first decouples temporal modeling from spatial representation learning, then integrates both through adaptive representation-level fusion. To assess robustness of the proposed approach, we construct a dataset based on an agent-based, microscopic traffic simulator (MATSim) and evaluate UniST-Pred under severe network disconnection scenarios. Additionally, we benchmark UniST-Pred on standard traffic prediction datasets, demonstrating its competitive performance against existing well-established models despite a lightweight design. The results illustrate that UniST-Pred maintains strong predictive performance across both real-world and simulated datasets, while also yielding interpretable spatio-temporal representations under infrastructure disruptions. The source code and the generated dataset are available at https://anonymous.4open.science/r/UniST-Pred-EF27.

IJCAI 2026

Beyond Vision: A Multimodal Dataset and Framework for Pest Recognition via Plant Electrophysiological Signals

Lu Wang, Jiaming Lin, Yuting Ye, Tao Wei, Chuchu Qin

Precise pest identification is essential for sustainable agriculture. Current visual recognition systems are brittle in the wild, where performance degrades due to occlusion and variable illumination. In contrast, plant electrophysiological signals serve as a robust, all-weather physiological modality, capable of detecting cryptic feeding behaviors that escape optical sensors. However, this field remains constrained by the scarcity of data and the absence of specialized algorithms. To bridge this gap, we introduce the Herbivory-Induced Plant Bio-signal Multimodal (HIPB-MM) dataset, the first fine-grained dataset comprising 4,023 synchronized plant electrophysiological signal-video pairs recording the feeding processes of three typical pest species. To address the weak and non-stationary nature of these signals, we propose the Herbivory-Induced Physiological Sensing (HIPS) framework. It integrates a Morphological Semantic Decoupling strategy to recover robust slow-wave semantics, and a Generation-State Encoder to model latent physiological states. Complementing this, an auxiliary dual-stream visual branch calibrates signal representations using explicit behavioral and morphological cues. Experiments demonstrate that HIPS establishes a solid benchmark (69.81% accuracy), comprehensively outperforming state-of-the-art baselines. Crucially, this work validates plant electrophysiology as a low-cost, all-weather modality for sustainable crop protection, effectively reducing pesticide dependency and safeguarding ecosystem health.

IJCAI 2026

White-Hat Testing for the Ballot Box: A Framework for Election AI Auditing

Chendi Wang, Jieying Chen

Recent research shows that conversational AI can shift voter preferences, with effects persisting for weeks. Yet frontier models exhibit a documented "persuasion-reliability tradeoff", producing hallucinated or systematically distorted election information. Despite these risks, election officials lack standardized tools to systematically evaluate AI systems before deployment. We propose CivicAudit-Bench, a stakeholder-guided auditing framework to stress-test large language models for civic hallucinations, false confidence, jurisdiction-dependent failure, and asymmetric refusals/accuracy. This framework introduces a modular, counterfactual, and severity-aware auditing methodology that integrates roll-call–based alignment modeling, entity-swap probing, and jurisdiction-conditional correctness criteria. Informed by engagement with the U.S. Election Assistance Commission, the toolkit consists of three modules: (1) PoliBias-US, a multi-indicator alignment screen combining Congressional roll-call ideology scaling with party-cue counterfactual sensitivity, persona robustness, and narrative-framing alignment; (2) HalluBias-Election, an evidence-linked benchmark that measures hallucinations, severity-weighted critical errors, and asymmetries via Entity-Swap Counterfactual Probing and a jurisdiction-safe completion criterion; and (3) Disclosure-Test, pre-registered experiments assessing whether transparency and calibrated-uncertainty disclosures reduce overreliance and attenuate persuasion without blocking legitimate civic information. CivicAudit-Bench outputs versioned audit scorecards and a coordinated white-hat disclosure workflow, advancing UN SDG~16 by strengthening democratic information integrity.

IJCAI 2026

Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering

Chendi Wang, Liam Cunningham, Tom Yishay, Jieying Chen

Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings where factual errors and ideological distortions are high-stakes. We present a reproducible measurement framework that treats hallucinations, unsupported statements in document-grounded QA, as diagnostic signals of ideological drift. Using 21,727 expert-labeled U.S. political news articles from QBias spanning left, center, and right sources, we (i) generate an article-specific question, (ii) elicit document-grounded answers from three open-weight LLMs and one proprietary model, (iii) detect sentence-level hallucinations via reference-based comparison, (iv) classify the ideological valence of hallucinated sentences with a fine-tuned stance classifier, and (v) probe output logits to relate token-level uncertainty to hallucination and drift. Hallucination rates vary substantially across models and concentrate in contentious topics, while source-ideology differences in hallucination frequency are modest. In contrast, hallucination content exhibits robust leftward drift: a majority of hallucinated sentences are classified as left-leaning, including among hallucinations generated from right-leaning sources. Logit-level analysis shows hallucinations arise in high-entropy generation contexts, and in some models uncertainty also predicts leftward drift, consistent with an "uncertainty → guessing" mechanism. We discuss implications for auditing AI-mediated political information and for designing safeguards in election-relevant deployments.

IJCAI 2026

PhysTrans: A Physics-Aware Transferable Framework for Global Cold-Start Photovoltaic Forecasting

Meng Wan, Kaipeng Gao, Jue Wang, Siyan Fang, Xue Miao, Pufen Zhang, Sijie Chang, Peng Shi, Yangang Wang, Zhenbing Zhao

With the rapid expansion of photovoltaic (PV) power generation worldwide, PV systems have become key to global energy construction. Accurate PV forecasting is essential for safe grid operation and renewable energy integration. However, most existing models rely heavily on site-specific historical data and perform poorly when deployed in cold-start scenarios of newly built power plants. We propose PhysTrans, a physics-aware transferable framework for cold-start PV forecasting. Firstly, we design a physics-constrained residual network that utilizes a clear-sky module for better physical consistency. Furthermore, we propose a dynamic cloud cropping method to obtain the cloud information of shaded PV stations by fitting the angle of the sun offsets. To fuse the asymmetric data, a query-based asymmetric fusion mechanism is introduced to achieve high-precision alignment of multi-modal data. We conduct experiments on global datasets, and the results show that the PhysTrans outperforms state-of-the-art models with a 13.2% decrease in MAE in the single-site task, and also outperforms existing migration models with an average decrease in MAE of 12.7% in the cross-sites task. Our work advances reliable and transferable PV forecasting for early-stage grid integration and contributes to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), in line with the Leave No One Behind principle.

IJCAI 2026

NeuroDALEC: A Differentiable and Interpretable Mass-Conserving Framework for Terrestrial Ecosystem Carbon Cycle Dynamics

Meng Wan, Tiantian Liu, Zhixin Xia, Ningming Nie, Jue Wang, Rongqiang Cao, Honglin He, Xiaoli Ren, Peng Shi, Yangang Wang

Accurate simulation of terrestrial ecological carbon cycles is crucial for global climate change and ecosystem management. Process-based carbon models have high interpretability, but suffer from insufficient accuracy and slow computation due to fixed parameters. In contrast, deep-learning carbon models achieve high accuracy, but disregard physical principles, which prevents ecologists from explaining ecosystem dynamics. We propose NeuroDALEC, an interpretable framework that embeds the DALEC carbon-cycle model within a neural network, enabling differentiable computation of ecological processes. Key parameters and ensemble learning strategies are designed, and mass-conserving carbon pool state transition equations are introduced to ensure physical consistency. Experiments show NeuroDALEC outperforms existing models in both accuracy and efficiency. Moreover, it provides sufficient interpretability by predicting all components of the carbon cycle. Deployed in a real-time carbon assimilation system, NeuroDALEC supports daily carbon forecasting and decision-making. This work contributes to the United Nations' Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land). The source code is available at: https://github.com/codesiena/NeuroDALEC.

IJCAI 2026

Democratizing Ski Safety: Real-Time Turn Segmentation with Smartphone IMU and Causal LSTM Networks

Michał Szymocha, Piotr Kacprzak, Jakub Robak, Wojciech Turek

Anterior cruciate ligament (ACL) injury is one of the most common and serious injuries in sports, particularly among recreational skiers. Research shows that structured technique awareness and continuous feedback can significantly reduce the risk of such injuries, yet access to professional instructors is limited to wealthy athletes who can afford continuous private coaching, creating a harmful inequity in injury prevention. This gap can be mitigated by automating the real-time analysis of skiing techniques available to the wider recreational skiing community. The approach relies exclusively on inertial sensors embedded in standard smartphones, eliminating the need for specialized equipment and enabling broad social scalability. To support immediate feedback, the system operates causally, producing predictions based solely on past observations. The work is conducted in cooperation with professional ski instructors, ensuring that problem formulation, data annotation, and result evaluation reflect real-world coaching practices and injury prevention needs. The model is evaluated using Leave-One-Subject-Out validation on a public, in-the-wild dataset, demonstrating robust generalization across skiers, achieving an average directional accuracy of 89.8%, while maintaining extremely low inference latency suitable for on-device mobile deployment. This work outlines a practical pathway to democratizing injury prevention in recreational sports.

IJCAI 2026

Domain-Informed Graph Neural Networks for Climate Factor Forecasting to Support Sustainable Crop Management

Ziyue Sun, Zixin Jiang, Chenkai Xu, Xinggao Liu

Forecasting climate factors is critical for anticipating agro-climatic risks and enabling sustainable crop management. However, accurate prediction remains challenging due to complex spatiotemporal variability, heterogeneous seasonal patterns, and intricate interdependencies among climate variables. Inspired by agronomic knowledge, We propose DoIGNN, a Domain-Informed Graph Neural Network that injects a domain-structured graph constraint built from Agro-Climatic Homogeneous Zones (ACHZs). Specifically, we partition stations into agro-climatic zones using long-term climatic statistics and location attributes, and construct a hierarchical ACHZ-guided adjacency. To better capture shared climate dynamics, we introduce a spatiotemporal decomposition module with temporal regularization that factorizes the climate tensor into low-rank global temporal bases and station loadings, yielding a compact station-level global component as auxiliary information for target forecasting. Finally, DoIGNN performs forecasting on both the ACHZ-guided and static-dynamic graphs to learn cross-region dependencies. Experiments on real-world climate datasets demonstrate that DoIGNN consistently improves forecasting accuracy over strong baselines while yielding more interpretable spatial dependency patterns that support climate-informed crop management decisions. Cooperating with Ningbo Natural Resources and Planning Big Data Center, the proposed model has been trained and deployed for local data analysis.

IJCAI 2026

A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning

William Solow, Paola Pesantez-Cabrera, Markus Keller, Lav Khot, Sandhya Saisubramanian, Alan Fern

Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a hybrid modeling approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the parameters of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60% for phenology and 40% for cold hardiness compared to deployed biophysical models. Project site: https://tinyurl.com/DMC-MTL-Site.

IJCAI 2026

FlowID: Enhancing Forensic Identification with Latent Flow-Matching Models

Jules Ripoll, David Bertoin, Alasdair Newson, Charles Dossal, Jose Pablo Baraybar

Every day, many people die under violent circumstances, whether from crimes, war, migration, or climate disasters. Medico-legal and law enforcement institutions document many portraits of the deceased for evidence, but cannot immediately carry out identification on them. While traditional image editing tools can process these photos for public release, the workflow is lengthy and produces suboptimal results. In this work, we leverage advances in image generation models, which can now produce photorealistic human portraits, to introduce FlowID, an identity-preserving facial reconstruction method. Our approach combines single-image fine-tuning, which adapts the generative model to out-of-distribution injured faces, with attention-based masking that localizes edits to damaged regions while preserving identity-critical features. Together, these components enable the removal of artifacts from violent death while retaining sufficient identity information to support identification. To evaluate our method, we introduce InjuredFaces, a novel benchmark for identity-preserving facial reconstruction under severe facial damage. Beyond serving as an evaluation tool for this work, InjuredFaces provides a standardized resource for the community to study and compare methods addressing facial reconstruction in extreme conditions. Experimental results show that FlowID outperforms state-of-the-art open-source methods while maintaining low memory requirements, making it suitable for local deployment without compromising data privacy.

IJCAI 2026

Brazilian Indigenous Languages Revitalization at Scale: Reducing Cost and Development Time for Low-Resource Language Courses

Gustavo Polleti, Fabricio Gerardi, Carolina Aragon, Fernando Bororo, Mariel Kujiboekureu, Wayali Salomã, Fabio Cozman

Brazil is home to about 180 Indigenous languages, which span a wide range of sociolinguistic circumstances, from critically endangered to relatively stable. Across this continuum, Indigenous communities often lack pedagogical resources and are underserved by existing language-learning technologies, which are typically designed for high-resource languages and assume solid connectivity and large datasets. This research project proposes AI-assisted tools and workflows for the collection and annotation of textual and speech data that substantially reduce the time and cost required to produce engaging language-learning game apps. Our goal is to implement a language-learning game app that Indigenous students can use to practice their reading, writing and speaking skills at home. We propose novel speech processing models for low-resource Indigenous languages and offlline support in low-connectivity environments. Our project adopts a co-creation model that actively foster collaboration between Indigenous educators, linguists, and youth, is adapted to the their context, and complies with ethical guidelines. We outline an implementation plan with Bororo and Enawene Nawe communities to test our methods and, potentially, produce an AI-driven platform for Indigenous language education that is applicable across diverse sociolinguistic contexts in Brazil and beyond.

IJCAI 2026

A Gloss-driven Indian Sign Language Production System Using Learned Pose Representations

Suvajit Patra, Arkadip Maitra, Swami Punyeshwarananda, Soumitra Samanta

Sign Language Production (SLP) system translates spoken or written language into sign language, enabling accessible communication between the deaf/hard-of-hearing and the hearing population. Being one of the most widely used sign languages globally, Indian Sign Language (ISL) is a very low-resource language and lacks such SLP systems. This paper presents a scalable and modular SLP framework based on Sign-Pose-VQ-VAE model, designed for low-resource settings. The model learns discrete pose representations (codes) by disentangling body, left-hand, and right-hand keypoints, enabling efficient pose modeling and co-articulated sign generation. The proposed system is evaluated using a Hindi movie subtitle corpus coupled with an off-the-shelf back-translation model and achieves a gloss BLEU-4 score of 47.20. The system-generated signs are evaluated by certified ISL interpreters with an average rating of 4.33/5, and a BERT precision of 0.7683 on glosses. In addition, the proposed system achieves state-of-the-art performance among keypoint-based methods on the PHOENIX14T benchmark, attaining a BLEU-4 score of 10.03 and surpassing the previous best method by 0.67 points. The system with code is available at https://cs.rkmvu.ac.in/~isl/sl_gen_vqvae.

IJCAI 2026

STAMP: Multi-Pattern Attention-Aware Multiple Instance Learning for STAS Diagnosis in Multi-Center Histopathology Images

Liangrui Pan, Xiaoyu Li, Chenchen Nie, Yaning Yang, Shaoliang Peng

Spread through air spaces (STAS) constitutes a novel invasive pattern in lung adenocarcinoma (LUAD), associated with tumor recurrence and diminished survival rates. However, large-scale STAS diagnosis in LUAD remains a labor-intensive endeavor, compounded by the propensity for oversight and misdiagnosis due to its distinctive pathological characteristics and morphological features. Consequently, there is a pressing clinical imperative to leverage deep learning models for STAS diagnosis. This study initially assembled histopathological images from STAS patients at the Second Xiangya Hospital and the Third Xiangya Hospital of Central South University, alongside the TCGA-LUAD cohort. Three senior pathologists conducted cross-verification annotations to construct the STAS-SXY, STAS-TXY, and STAS-TCGA datasets. We then propose a multi‑pattern attention-aware multiple instance learning framework, named STAMP, to analyze and diagnose the presence of STAS across multi‑center histopathology images. Specifically, the dual‑branch architecture guides the model to learn STAS‑associated pathological features from distinct semantic spaces. Transformer-based instance encoding and a multi‑pattern attention aggregation modules dynamically selects regions closely associated with STAS pathology, suppressing irrelevant noise and enhancing the discriminative power of global representations. Moreover, a similarity regularization constraint prevents feature redundancy across branches, thereby improving overall diagnostic accuracy. Extensive experiments demonstrated that STAMP achieved competitive diagnostic results on STAS-SXY, STAS-TXY and STAS-TCGA, with AUCs of 0.8058, 0.8017, and 0.7928, respectively, surpassing the clinical level. The 10 open baseline results establish a benchmark for STAS diagnostic research and facilitate the future generalizability and clinical integration of computational pathology technologies. Dataset features and code are accessible at https://github.com/panliangrui/IJCAI2026.

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