GenAI4ELab/papercli-papers huggingface dataset:
RobustHAR: Multi-scale Spatial-temporal Masked Self-supervised Pre-training for Robust Human Activity Recognition
Authors: Xiao Liu; Guan Yuan; Yanmei Zhang; Shang Liu; Qiuyan Yan
Venue: IJCAI 2025 (main)

Abstract: Human activity recognition (HAR) is prone to performance degradation in real-world applications due to data missing between intra-sensor and inter-sensor channels. Masked modeling, as one mainstream paradigm of self-supervised pre-training, can learn robust representations across sensors in the data missing scenario by reconstructing the masked content based on the unmasked part. However, the existing methods predominantly emphasize the temporal dynamics of human activities, which limits their ability to effectively capture the spatial interdependencies among multiple sensors. Besides, different human activities often span across various spatial-temporal scales, which results in activity recognizer failing to capture intricate spatial-temporal semantic information. To address these issues, we propose RobustHAR, a new HAR model with multi-scale spatial-temporal masked self-supervised pre-training designed to improve model performance on the data missing context. RobustHAR involves three main steps: (1) RobustHAR constructs location-inspired spatial-temporal 3D-variation modeling to capture spatial-temporal correlated information in human activity data. (2) RobustHAR then designs multi-scale spatial-temporal masked self-supervised pre-training with semantic-consistent multi-scale feature co-learning for learning robust features at different scales. (3) Finally, RobustHAR fine-tunes the pretraining model with adaptive multi-scale feature fusion for human activity recognition. Extensive experiments on three public multi-sensor datasets demonstrate that RobustHAR outperforms existing state-of-the-art methods.
Proactive Data-driven Scheduling of Business Processes
Authors: Francesca Meneghello; Arik Senderovich; Massimiliano Ronzani; Chiara Di Francescomarino; Chiara Ghidini
Venue: IJCAI 2025 (main)

Abstract: Proactive scheduling creates robust offline schedules that optimize resource utilization and minimize job flow times. This work addresses scheduling challenges in business processes, often encountered in service systems, which differ from traditional applications like manufacturing due to inherent uncertainties in activity durations, and human resource availability. We model the business process scheduling problem (BPSP) as a variation of stochastic resource-constrained multi-project scheduling (RCMPSP), and apply process mining to infer unknown parameter values from historical event data. To overcome the randomness in activity durations, we transform the problem into its deterministic counterpart, and prove that the latter provides a lower bound on the Makespan of the stochastic problem. Our approach integrates data-driven Monte Carlo simulation with constraint programming to generate proactive schedules that guarantee, with high probability, that the Makespan remains below a predefined threshold. We evaluate our approach using synthetic datasets with varying levels of uncertainty and size. In addition, we apply the approach to a real-world dataset from an outpatient cancer hospital, demonstrating its effectiveness in optimizing the process Makespan by an average of 5% to 14%.
EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems
Authors: Dian Meng; Zhiguang Cao; Yaoxin Wu; Yaqing Hou; Hongwei Ge; Qiang Zhang
Venue: IJCAI 2025 (main)

Abstract: Recent neural heuristics for the Vehicle Routing Problem (VRP) primarily rely on node coordinates as input, which may be less effective in practical scenarios where real cost metrics—such as edge-based distances—are more relevant. To address this limitation, we introduce EFormer, an Edge-based Transformer model that uses edge as the sole input for VRPs. Our approach employs a precoder module with a mixed-score attention mechanism to convert edge information into temporary node embeddings. We also present a parallel encoding strategy characterized by a graph encoder and a node encoder, each responsible for processing graph and node embeddings in distinct feature spaces, respectively. This design yields a more comprehensive representation of the global relationships among edges. In the decoding phase, parallel context embedding and multi-query integration are used to compute separate attention mechanisms over the two encoded embeddings, facilitating efficient path construction. We train EFormer using reinforcement learning in an autoregressive manner. Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) reveal that EFormer outperforms established baselines on synthetic datasets, including large-scale and diverse distributions. Moreover, EFormer demonstrates strong generalization on real-world instances from TSPLib and CVRPLib. These findings confirm the effectiveness of EFormer’s core design in solving VRPs.
Preference-based Deep Reinforcement Learning for Historical Route Estimation
Authors: Boshen Pan; Yaoxin Wu; Zhiguang Cao; Yaqing Hou; Guangyu Zou; Qiang Zhang
Venue: IJCAI 2025 (main)

Abstract: Recent Deep Reinforcement Learning (DRL) techniques have advanced solutions to Vehicle Routing Problems (VRPs). However, many of these methods focus exclusively on optimizing distance-oriented objectives (i.e., minimizing route length), often overlooking the implicit drivers' preferences for routes. These preferences, which are crucial in practice, are challenging to model using traditional DRL approaches. To address this gap, we propose a preference-based DRL method characterized by its reward design and optimization objective, which is specialized to learn historical route preferences. Our experiments demonstrate that the method aligns generated solutions more closely with human preferences. Moreover, it exhibits strong generalization performance across a variety of instances, offering a robust solution for different VRP scenarios.
An Approach to Quantify Plans Robustness in Real-world Applications
Authors: Francesco Percassi; Sandra Castellanos-Paez; Romain Rombourg; Mauro Vallati
Venue: IJCAI 2025 (main)

Abstract: Automated planning systems are increasingly deployed in real-world applications, often characterised by uncertainty and noise stemming from sensors, actuators, and environmental conditions. Under such circumstances, improving the deployability of generated plans requires assessing their robustness to varying conditions, thereby reducing the need for costly replanning. Replanning can be computationally intensive and may hinder the practical applicability of planning systems. In many domains, such as urban traffic control or underwater exploration, it is often sufficient for plans to reach an acceptable region rather than the exact goal. A key distinction in this context lies between valid plans (which achieve the intended goal under ideal conditions) and executable plans (which remain feasible under uncertainty or perturbation). This paper formalises the notion of execution-invariant planning tasks, in which plans are robust to noise and uncertainty. To foster the adoption of automated planning in real-world settings, we propose a statistical framework for evaluating plan robustness, offering a quantifiable measure of a plan’s ability to reach a goal within a specified tolerance under diverse perturbations or uncertainty. We validate our approach in two real-world domains, demonstrating its effectiveness.
Abstraction Heuristics for Classical Planning Tasks with Conditional Effects
Authors: Martín Pozo; Jendrik Seipp
Venue: IJCAI 2025 (main)

Abstract: In planning tasks, conditional effects model action outcomes that depend on the current state of the world. Conditional effects are a crucial modeling feature since compiling them away can cause an exponential growth in task size. However, only a few admissible heuristics support them. To add abstraction heuristics to this set, we show how to compute projections, Cartesian abstractions and merge-and-shrink abstractions for tasks with conditional effects. Our experiments show that these heuristics are competitive with, and often surpass, the state-of-the-art for conditional-effect tasks.
A Finite-State Controller Based Offline Solver for Deterministic POMDPs
Authors: Alex Schutz; Yang You; Matías Mattamala; Ipek Caliskanelli; Bruno Lacerda; Nick Hawes
Venue: IJCAI 2025 (main)

Abstract: Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.
GRAML: Goal Recognition As Metric Learning
Authors: Matan Shamir; Reuth Mirsky
Venue: IJCAI 2025 (main)

Abstract: Goal Recognition (GR) is the problem of recognizing an agent's objectives based on observed actions. Recent data-driven approaches for GR alleviate the need for costly, manually crafted domain models. However, these approaches can only reason about a pre-defined set of goals, and time-consuming training is needed for new emerging goals. To keep this model-learning automated while enabling quick adaptation to new goals, this paper introduces GRAML: Goal Recognition As Metric Learning. GRAML frames GR as a deep metric learning problem, using a Siamese network composed of recurrent units to learn an embedding space where traces leading to the same goal are close, and those leading to different goals are distant. This metric is particularly effective for adapting to new goals, even when only a single example trace is available per goal. Evaluated on a versatile set of environments, GRAML shows speed, flexibility, and runtime improvements over the state-of-the-art GR while maintaining accurate recognition.
GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
Authors: Ya Shen; Gang Chen; Hui Ma; Mengjie Zhang
Venue: IJCAI 2025 (main)

Abstract: Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arriving workflow tasks, represented as Directed Acyclic Graphs (DAG), to suitable virtual machines (VMs). Deep reinforcement learning (DRL) has been widely employed for automated scheduling policy design. However, the performance of DRL is heavily influenced by the design of the problem-tailored policy network and is highly sensitive to hyperparameters and the design of reward feedback. Considering the above-mentioned issues, this study proposes a novel DRL method combining Graph Attention Networks-based policy network and Evolution Strategy, referred to as GATES. The contributions of GATES are summarized as follows: (1) GATES can capture the impact of current task scheduling on subsequent tasks by learning the topological relationships between tasks in a DAG. (2) GATES can assess the importance of each VM to the ready task, enabling it to adapt to dynamically changing VM resources. (3) Utilizing Evolution Strategy's robustness, exploratory nature, and tolerance for delayed rewards, GATES achieves stable policy learning in CADWS. Extensive experimental results demonstrate the superiority of the proposed GATES in CADWS, outperforming several state-of-the-art algorithms. The source code is available at: https://github.com/YaShen998/GATES.
Solving QNP and FOND+  with Generating, Testing and Forbidding
Authors: Zheyuan Shi; Hao Dong; Yongmei Liu
Venue: IJCAI 2025 (main)

Abstract: Qualitative Numerical Planning (QNP) extends classical planning with numerical variables that can be changed by arbitrary amounts. FOND+ extends Fully Observable Non-Deterministic (FOND) planning by introducing explicit fairness assumptions, resulting in a more expressive model that can also capture QNP as a special case. However, existing QNP and FOND+ solvers still face significant scalability challenges. To address this, we propose a novel framework for solving QNP and FOND+ by generating strong cyclic solutions of the associated FOND problem, testing their validity, and forbidding non-solutions in conducting further searches. For this, we propose a procedure called SIEVE*, which generalizes the QNP termination testing algorithm SIEVE to determine whether a strong cyclic solution is a FOND+ solution. Additionally, we propose several optimization techniques to further improve the performance of our basic framework. We implemented our approach based on the advanced FOND solver PRP; experimental results show that our solver shows superior scalability over the existing QNP and FOND+ solvers.
What Makes You Special? Contrastive Heuristics Based on Qualified Dominance
Authors: Rasmus G. Tollund; Kim G. Larsen; Alvaro Torralba
Venue: IJCAI 2025 (main)

Abstract: In cost-optimal planning, dominance pruning methods discard states during the search that are dominated by others. However, the binary nature of pruning fails to exploit information when we cannot prove that a state is fully dominated. To this end, we introduce qualified dominance, an automatic method that given a pair of states s,t synthetizes a finite state automaton that represents a language of plans from s that are dominated by t. This not only explains why s cannot be pruned, but also can be used to improve the heuristic function to guide the search. This results in a new type of heuristic, which we call contrastive heuristics, that are dependent on the search performed so far. We provide the theoretical foundation for showing that contrastive heuristics can be used to find optimal plans even when their more informative estimates are not admissible.
Towards a Unified View of Social Laws with Instantaneous Actions
Authors: Alexander Tuisov; Evgeny Mishlyakov; Alexander  Shleyfman; Erez Karpas
Venue: IJCAI 2025 (main)

Abstract: Multiple agents operating in a shared environment can interfere with each other’s ability to reach their goals. One of the approaches to address this issue is enacting a social law – a set of rules that restricts some possible behaviors of the agents. A social law is considered robust if it guarantees that each agent can achieve its goal independently of the actions of other agents. Recent work has shown how to verify that a given social law, encoded in an MA-STRIPS formalism, is robust by compilation to classical planning. Follow-up work presented an extended compilation which can handle numeric multi-agent planning. In this paper, we present a new compilation, which can handle both classical and numeric multi-agent planning formalisms, as well as any other multi-agent planning formalism with instantaneous actions, in which action preconditions can be negated using first-order logic with equality. This opens the door to using social laws in even richer planning formalisms. Our empirical evaluation shows that the added expressivity of the new compilation does not hurt its performance, and it achieves comparable performance to the previous state-of-the-art compilations.
DGL: Dynamic Global-Local Information Aggregation for Scalable VRP Generalization with Self-Improvement Learning
Authors: Yubin Xiao; Yuesong Wu; Rui Cao; Di Wang; Zhiguang Cao; Xuan Wu; Peng Zhao; Yuanshu Li; You Zhou; Yuan Jiang
Venue: IJCAI 2025 (main)

Abstract: The Vehicle Routing Problem (VRP) is a critical combinatorial optimization problem with wide-reaching real-world applications, particularly in logistics, transportation. While neural network-based VRP solvers have shown impressive results on test instances similar to training data, their performance often degrades when faced with varying scales and unseen distributions, limiting their practical applicability. To overcome these limitations, we introduce DGL (Dynamic Global-Local Information Aggregation), a novel model that combines global and local information to effectively solve VRPs. DGL dynamically adjusts local node selections within a localized range, capturing local invariance across problems of different scales and distributions, thereby enhancing generalization. At the same time, DGL integrates global context into the decision-making process, providing richer information for more informed decisions. Additionally, we propose a replacement-based self-improvement learning framework that leverages data augmentation and random replacement techniques, further enhancing DGL's robustness. Extensive experiments on synthetic datasets, benchmark datasets, and real-world country map instances demonstrate that DGL achieves state-of-the-art performance, particularly in generalizing to large-scale VRPs and real-world scenarios. These results showcase DGL's effectiveness in solving complex, realistic optimization challenges and highlight its potential for practical applications.
SRA-MCTS: Self-driven Reasoning Augmentation with Monte Carlo Tree Search for Code Generation
Authors: Bin Xu; Yiguan Lin; Yinghao Li; Yang Gao
Venue: IJCAI 2025 (main)

Abstract: Large language models exhibit remarkable performance in simple code generation tasks. However, they encounter significant challenges when addressing complex problems that require reasoning and question decomposition. To tackle this, we propose a self-driven reasoning augmentation process, SRA-MCTS, which incorporates Monte Carlo Tree Search (MCTS) for reasoning data generation. SRA-MCTS enables LLMs to self-generate intermediate reasoning steps and perform iterative self-evaluation, facilitating self-improvement. Specifically, it utilizes MCTS to produce diverse intermediate reasoning steps. During each iteration, MCTS generates a step and employs self-evaluation to guide the selection of subsequent branches, ultimately forming a sufficiently diverse reasoning path referred to as “thinking”. This thinking guides the model in generating corresponding code, and both are combined as training data for supervised fine-tuning. Experimental results demonstrate that SRA-MCTS achieves consistent performance improvements across three model scales without additional supervisory assistance. Applied to the Meta-Llama-3.1-8B-Instruct model, it delivers an 11-point improvement on the MBPP-Complex dataset, underscoring the significant potential for model self-improvement. The code and data are available at https://github.com/DIRECT-BIT/SRA-MCTS.
NS4S: Neighborhood Search for Scheduling Problems Via Large Language Models
Authors: Junjie Zhang; Canhui Luo; Zhouxing Su; Qingyun Zhang; Zhipeng Lü; Junwen Ding; Yan Jin
Venue: IJCAI 2025 (main)

Abstract: Large Language Models (LLMs) have emerged as a promising technology for solving combinatorial optimization problems. However, their direct application to scheduling problems remains limited due to the inherent complexity of these problems. This paper proposes an LLMs-based neighborhood search method that leverages LLMs to tackle the job shop scheduling problem (JSP) and its variants. The main contributions of this work are threefold. First, we introduce a novel LLMs-guided neighborhood evaluation strategy that guides local search by dynamically adjusting operation weights. Second, we develop a verification evolution (VeEvo) framework to mitigate the hallucination effects of LLMs, enabling the generation of high-quality heuristics for weight updates. Third, we integrate this framework with the weighted neighborhood evaluation strategy to effectively guide the search towards promising regions. Extensive experiments are conducted on 349 benchmark instances across three classical scheduling problems. The results demonstrate that our algorithm significantly outperforms existing state-of-the-art methods. For JSP, our algorithm reduces the average optimality gap from 10.46% to 1.35% on Taillard's instances compared to reinforced adaptive staircase curriculum learning. For flexible JSP (FJSP), it reduces the gap from 13.24% to 0.05% on Brandimarte's instances compared to deep reinforcement learning methods. Furthermore, for FJSP with sequence dependent setup time, our algorithm updates 9 upper bounds for benchmark instances.
Computational Complexity of Planning for Recursive Primitive Task Networks: Selective Action Nullification with State Preservation
Authors: Yifan Zhang; Pascal Bercher
Venue: IJCAI 2025 (main)

Abstract: This paper investigates fundamental aspects of Hierarchical Task Network (HTN) planning by systematically exploring recursive arrangements of primitive task networks. Working within a general framework that aligns with recently identified ACKERMANN-complete HTN problems, we map the computational complexity across various recursive configurations, revealing a rich complexity landscape. Through a novel proof technique that we call selective action nullification with state preservation, we demonstrate that even a highly restricted class of regular HTN problems remains PSPACE-complete, establishing a profound connection to classical planning. We hope these findings contribute to a deeper and broader understanding of the theoretical foundations of HTN planning.
Tree-of-AdEditor:  Heuristic Tree Reasoning for Automated Video Advertisement Editing with Large Language Model
Authors: Yuqi Zhang; Bin Guo; Nuo Li; Ying Zhang; Shijie Wang; Zhiwen Yu; Qing Li
Venue: IJCAI 2025 (main)

Abstract: Video advertising has become a popular marketing strategy on e-commerce platforms, requiring high-level semantic reasoning like selling point discovery, narrative organization. Previous rule-based methods struggle with these complex tasks, and learning-based approaches demand large datasets and high training costs. Recently, Large Language Models have opened incredible opportunities for advancing intelligent video advertisement editing. However, Input-output (IO) prompting and Chain-of-Thought (CoT) struggle to adapt to the nonlinear thinking hierarchy of video editing, where editors iteratively select shots or revert them to explore potential editing solutions. While Tree-of-Thought (ToT) offers a conceptual structure that mirrors this hierarchy, it falls short in aligning with effective video advertising strategies and lacks robust fact-checking mechanisms. To address these, we propose a novel framework, Tree-of-AdEditor (ToAE), which constructs a reasoning tree to mimic human editors, and incorporates domain-specific theories and heuristic fact-checking to identify optimal editing solutions. Specifically, motivated by effective advertisement principles, we develop a "local-global" mechanism to guide LLM in both the shot level and sequence level decision-making. We introduce a visual incoherence pruning module to provide external heuristic fact-checking, ensuring visual attractiveness and reducing computation costs. Quantitative experiments and expert evaluation demonstrate the superiority of our method compared to baselines.