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- Vision-language navigation: a survey and taxonomy89
A comprehensive survey and an insightful taxonomy ofVision-language navigation tasks based on the different characteristics of language instructions is provided, including the incorporation of knowledge into VLN models and transferring them to the real physical world.
- Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model78
Multi-stage Token Dropping (MustDrop) is proposed to measure the importance of each token from the whole lifecycle, including the vision encoding stage, prefilling stage, and decoding stage, and can more precisely recognize the important and redundant tokens, thus achieving an optimal balance between performance and efficiency.
- Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning72
Improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects, and solve the non-convergence policies problems caused by sparse reward.
- FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity59
Experimental results show that FedHiSyn outperforms six baseline methods, e.g., FedAvg, SCAFFOLD, and FedAT, in terms of training accuracy and efficiency.
- Scheduling parallel jobs with tentative runs and consolidation in the cloud41
The algorithm employs tentative run and workload consolidation under such a two-tier virtual machines architecture to enhance the popular FCFS algorithm, and it can even produce comparable performance to the runtime-estimation-based EASY algorithm.
- Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach38
By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed and the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements.
- A Reinforcement Learning Behavior Tree Framework for Game AI27
Following this study, a QBot model is build based on the framework in the Raven platform, a popular test bed for game AI development, and the result proves that the framework is advanced.
- GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation26
GeoNav, a multi-modal agent for long-range aerial navigation with geospatial awareness, operates in three phases-landmark navigation, target search, and precise localization-mimicking human coarse-to-fine spatial reasoning patterns.
- Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution26
A novel idea of dynamic constraint based on frequent sub-trees mining, which can accelerate evolution by protecting preponderant behavior sub-Trees from undesired crossover, is proposed and introduced into the evolving BTs with hybrid constraints (EBT-HC).
- Dynamic Obstacle-Avoiding Path Planning for Robots Based on Modified Potential Field Method25
A new potential field method is proposed, whose new virtual force is deduced through introducing the restriction of collision angle with exponential form and both the information of angle and magnitude of relative velocity, to solve the problem of unnecessary obstacle avoidance in dynamic obstacle avoidance planning.
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- Regarding Goal Bounding and Jump Point Search20
The combination of faster preprocessing and stronger online reasoning leads to three new and highly performant algorithms: JPS+BB+ and Two-Oracle Pathfinding Search (TOPS) based on search, and Topping+ based on path extraction.
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- Integrating a Path Planner and an Adaptive Motion Controller for Navigation in Dynamic Environments18
Improvements make the IA3C controller converge faster and become more adaptive to incomplete, noisy information caused by partial observability and strengthen the robots’ temporal reasoning of the environments by a memory-based network.
- Towards Autonomous UAV Visual Object Search in City Space: Benchmark and Agentic Methodology17
This work introduces CityAVOS, the first benchmark dataset for autonomous search of static urban objects and proposes PRPSearcher (Perception-Reasoning-Planning Searcher), a novel agentic method powered by multi-modal large language models that enables a UAV agent to think and reason like humans on visual cues when searching for objects.
- DARA: Domain- and Relation-Aware Adapters Make Parameter-Efficient Tuning for Visual Grounding17
This paper proposes DARA, a novel PETL method comprising Domain-aware Adapters (DA Adapters) and Relation-aware Adapters (RA Adapters) for VG that achieves the best accuracy while saving numerous updated parameters compared to the full fine-tuning and other PETL methods.
- Question-aware memory network for multi-hop question answering in human–robot interaction16
This work proposes question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process and incorporates graph context information into knowledge graph embedding model to increase the ability to represent entities and relations.
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- A Semi-Markov Decision Model for Recognizing the Destination of a Maneuvering Agent in Real Time Strategy Games16
A novel semi-Markov decision model (SMDM) is proposed that outperforms another extension of the MDP in terms of precision, recall, and -measure, and Destinations are recognized efficiently by the SMDM no matter whether they are changed or not.
- Formation Control of Robotic Swarm Using Bounded Artificial Forces16
A potential field control algorithm, navigating a swarm of robots into a predefined 2D shape while avoiding intermember collisions, and defines the bounded artificial forces in the form of exponential functions, which proves the stability and convergence properties of the algorithm.
- Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration14
In state value decoding stage, the model explores the hierarchical semantic of ontology and enhances the interrelation between slots with masked hierarchical attention and solves the out-of-vocabulary problem by combining generation method and extraction method together.
- Shortest Path Network Interdiction With Goal Threshold14
A new version of the network interdiction problem that optimizes the interdictor’s resource consumption while limiting network capacities to a specified threshold is considered and two basic algorithms to solve it based on Benders decomposition and Lagrange duality are proposed.
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- SwimVG: Step-Wise Multimodal Fusion and Adaption for Visual Grounding12
Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually, and they fuse the cross-modal features from shallow to deep layers gradually.
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- Asymmetrically Decentralized Federated Learning11
Empirically, extensive experiments conducted on the MNIST, CIFAR10&100 datasets demonstrate the superior performance of the proposed DFedSGPSM algorithm compared to several existing SOTA optimizers in terms of generalization.
- Single Real Goal, Magnitude-Based Deceptive Path-Planning11
This work formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method that helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios.
- Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction.11
Experimental results indicate that the learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models and the overall framework and the DLNI problem domain are introduced.
- Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction11
A Markov Decision Process-based goal recognition model along with its dynamic Bayesian network representation and the applied goal inference method is proposed to identify the evader’s real goal within the DSPLNI context.
- CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments9
CityCube is introduced, a systematic benchmark designed to probe cross-view reasoning capabilities of current VLMs in urban settings and indicates the task correlations and fundamental cognitive disparity between VLMs and human-like reasoning.
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- DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning9
Novel DFL algorithms are proposed that exhibit superior performance in terms of both generalization and convergence speed compared to existing state-of-the-art (SOTA) optimizers in DFL.
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- Dynamic Detection of Topological Information from Grid-Based Generalized Voronoi Diagrams9
Beyond the construction and reconstruction of a GVD on grids, DTD further extracts connectivity among the GVD edges and vertices, making it work well in dynamic environments and the usefulness of the resulting topological information for high-level path planning tasks.
- Deceptive Path Planning via Count-Based Reinforcement Learning under Specific Time Constraint8
A DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints and a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) is proposed and verified its feasibility in discrete path-planning domains.
- DFedGFM: Pursuing global consistency for Decentralized Federated Learning via global flatness and global momentum8
This paper first rethink the local flatness and local momentum acceleration techniques in the two most popular DFL optimizers and proposes a novel DFL algorithm, called DFedGFM, to simultaneously explore the global flatness and global momentum to achieve global consistency.
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- Learning Ad Hoc Cooperation Policies from Limited Priors via Meta-Reinforcement Learning8
This paper proposes a method called a Few typE-based Ad hoc Teamwork via meta-reinforcement learning (FEAT), which effectively adapts to teammates using a small set of known types within a single episode, and enables agents to develop a highly adaptive policy through meta-reinforcement learning by employing limited priors about known types.
- A Semi-Markov Decision Model With Inverse Reinforcement Learning for Recognizing the Destination of a Maneuvering Agent in Real Time Strategy Games8
A semi-Markov decision model (SMDM), which explicitly models the duration of an action, is proposed in this paper and shows that the recognizer using IRL can recognize the destination effectively even if the intention changes during the midway, and it performs better in terms of several most frequently-used metrics.
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- Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder7
A hierarchical path planning framework called SG–RL (subgoal graphs–reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments, which demonstrates that the design of reward functions and the types of training environments are important factors for learning feasible policies.
- Online probabilistic goal recognition and its application in dynamic shortest-path local network interdiction7
The empirical test results show the effectiveness of the goal recognition method, and the practical implications of these methods in solving scalable multi-terminus network interdiction problem are verified.
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- A Decomposition Approach for Stochastic Shortest-Path Network Interdiction with Goal Threshold6
The results showed that the dual subgraph and better-response procedure can significantly improve the efficiency and scalability of the decomposition algorithm and the improved enhancement algorithms are less sensitive and robust to parameters.
- Who are the ‘silent spreaders’?: contact tracing in spatio-temporal memory models5
A neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) is presented to identify ACCs from contact tracing data and displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.
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- An extended area-partition-involved collaborative patrolling game in chemical clusters considering attackers’ bounded rationality and parameter uncertainty5
Results of the case study indicate that when considering two types of uncertainties mentioned above, the defender’s payoff will be slightly reduced, whereas the more robust patrolling routes are more easily convinced and adopted by the management department of chemical clusters and improve the process safety of chemical cluster.
- Improving the Scalability of the Magnitude-Based Deceptive Path-Planning Using Subgoal Graphs5
By leading the path deception information down into a general purpose but highly-efficient path-planning formulation, the paper substantially speeds up the task upon large scale terrains with an admissible loss of deception.
- Speed Optimization for Incremental Updating of Grid-Based Distance Maps5
A novel algorithm to speed up the construction of DMs on planar, eight-connected grids that only updates those cells which are affected by the changes and employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update.
- A Lookahead Behavior Model for Multi-Agent Hybrid Simulation5
A lookahead behavior model (LBM) is proposed to implement a PR-based hybrid simulation and shows that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations.
- A Logical Hierarchical Hidden Semi-Markov Model for Team Intention Recognition5
It is proved that the explicit duration modeling of the working mode is effective in team intention recognition, and the LHHSMM outperforms a modified logical hierarchical hidden Markov model in terms of precision, recall, and -measure.
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- Credit-based Differential Privacy Stochastic Model Aggregation Algorithm for Robust Federated Learning via Blockchain4
A Credit-based Differential Privacy stochastic model aggregation algorithm combined with SIGN operation (Cre-DPSIGN) is adopted in the authors' peer-to-peer network, which shows lower privacy loss consumption and better system robustness.
- MIRA: Model-Based Imagined Rollouts Augmentation for Non-Stationarity in Multi-Agent Systems4
This article combines model-based reinforcement learning (MBRL) and meta-learning and proposes a method called Model-based Imagined Rollouts Augmentation (MIRA), based on an environment dynamics model, which can independently perform multi-agent rollouts with opponent models during exploitation and learn to infer the environmental non-stationarity as a latent variable using the rollouts.
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- Application Analysis on PSO Algorithm in the Discrete Optimization Problems4
An investigation of PSO algorithm from the perspective of spatial search is given, a novel analysis of the key feature changes when PSOgorithm is applied to discrete optimization, and a classification method is proposed to summary existing discrete PSO algorithms.
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- Self-Organizing Memory Based on Adaptive Resonance Theory for Vision and Language Navigation3
A S elf-organizing Memory based on Adaptive Resonance Theory (SMART) module for incremental topological mapping and a framework for utilizing the SMART module to guide navigation, which provides a compact spatial representation and supports the discovery of novel shortcuts through inferences while being explainable in terms of cognitive science.
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- Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric3
The paper tries to answer what kinds of actions contain less information and more uncertainty about the agent’s real goal, and how to describe this uncertainty; what is the best way to control the process of goal identification; and how goal vagueness could be controlled.
- Improving the Combination of JPS and Geometric Containers3
This work considers an alternative approach where Dijkstra only from every node where a jump point is located, and compute and store geometric containers only for those outgoing edges which are consistent with the diagonal-first ordering in JPS.
- Multi-Agent Intention Recognition using Logical Hidden Semi-Markov Models3
LHSMMs are used in multi-agent intention recognition (MAIR), which identifies not only intentions of every agent but also working modes of the team considering cooperation and can even recognize destinations of the agent in advance by making use of the cooperation information.
- Incremental Construction of Generalized Voronoi Diagrams on Pointerless Quadtrees3
This paper proposes an improvement on the current algorithms, using pointerless quadtrees in place of metric matrices to compute and maintain GVDs, and provides a method to approximate roadmaps in multiple granularities from the quadtree based GVD.
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- PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events2
This work proposes a Psycho logy-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory and designs an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts to enhance interpretability.
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- M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension2
During fine-tuning, the pre-trained vision and language encoders are kept fixed and update M 3 ISAs on side networks to establish connections between them, thereby achieving parameter-and memory-efficient tuning for REC.
- Prompt-based Context- and Domain-aware Pretraining for Vision and Language Navigation2
A novel Prompt-bAsed coNtext- and inDoor-Aware (PANDA) pretraining framework to address problems of VLN adaptation and experimental results on both R2R and REVERIE show the superiority of PANDA compared to existing state-of-the-art methods.
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- A Scalable GVT Estimation Algorithm for PDES: Using Lower Bound of Event-Bulk-Time2
This study proposes a scalable GVT estimation algorithm based on Lower Bound of Event-Bulk-Time, which triggers the computation of the GVT value according to the number of processed events and shows higher speedup and accuracy of GVT computation than the fast asynchronous GVT algorithm.
- A Path Planning Algorithm Based on Parallel Particle Swarm Optimization2
The resulting simulation experiments show that when compared with traditional PSO, using PPSO could considerably reduce the searching time of path finding in multi-agent environment.
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- SplitGround: Long-Chain Reasoning Split via Modular Multi-Expert Collaboration for Training-Free Scene Knowledge-Guided Visual Grounding1
This work proposes SplitGround—a collaborative framework that strategically decomposes complex reasoning processes by fusing the input query and image with knowledge through two auxiliary modules, effectively splitting long reasoning chains into manageable subtasks with reduced complexity.
- OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement1
This paper enhances the consistency of DFL by developing an opposite lookahead enhancement technique (Ole), yielding OledFL to optimize the initialization of each client in each communication round, thus significantly improving both the generalization and convergence speed.
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- The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness1
The key strategy for enhancing model robustness entails the transfer of robustness from the pre-trained model to the fine-tuned model, coupled with adding a small amount of Gaussian noise to augment the representative capacity of the model.
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- Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning1
This work presents a novel framework that incorporates meta-learning into a multi-agent reinforcement learning algorithm that enables the extraction of meta-parameters from past experiences, facilitating rapid adaptation to new tasks with altered chronological orders and circumventing the time-intensive nature of reinforcement learning.
- An Approach for Deployment of Service-Oriented Simulation Run-Time Resources1
A learnable genetic algorithm for SRR deployment (LGASRD) where the population can learn from elites and adaptively select evolution operators performing well is proposed, verifying the effectiveness and excellence of LGASRD when deploying SRR.
- Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments1
This paper presents Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task.
- An incremental flow-based task scheduler for simulation tasks in cloud environments1
This paper focuses on the efficient scheduling of simulation tasks in the cloud, which has been recognized as an NP-hard combinational optimization problem, and designs an incremental flow-based task scheduler to deal with the dynamic changes of tasks and hosts.
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- Memory network based Knowledge Driven Model for Response Generation in Dialog System1
A novel architecture is proposed, namely Memory network based Knowledge Driven Model (MKDM), which can integrate knowledge base by memory manager, and generate knowledge grounded responses in dialogue system in an end-to-end way.
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- Learning real-time search on c-space GVDs1
The learning real-time search (LRTS) algorithm is extended to search on a set of c-space generalized Voronoi diagrams (c-space GVDs), helping the robots to incrementally plan a path, to efficiently avoid local minima, and to execute fast movement.
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- Coordinated Learning by Model Difference Identification in Multiagent Systems with Sparse Interactions1
This paper proposes a modified approach to learn where and how to coordinate agents’ behaviors in more general MASs with sparse interactions, which outperforms its competitors by improving the average agent reward per step and works well in some broader scenarios.
- An accurate interest matching algorithm based on prediction of the space–time intersection of regions for the distributed virtual environment1
The results prove that although an additional matching effort is required in the new algorithm, it outperforms the baselines in terms of event-capturing ability, redundant matching avoidance, runtime efficiency and scalability.
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- UTSE: A Game Engine-Based Simulation Environment for Agent1
An Unreal Tournament-Based Simulation Environment (UTSE) for agent simulation is proposed and a simple case is given to demonstrate the effectiveness of UTSE to rapidly produce game engine-based high fidelity simulations.
- Research of Combat Simulation Organization Modeling Based on Complex Networks1
The results show that this algorithm can better reflect the characteristics of organizational behaviors in combat simulation, and is better than analogous algorithms at complexity.
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- Distributed Dynamic Data Driven Simulations: A General Framework and Illustration Example–
The results show that the proposed framework can produce consistent hybrid state estimates while maintaining a low communication burden, and suggests robustness to microscopic sensor miss-counts, while also revealing a case-specific dependence on accurate spatial accumulation data.
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- DSPIGCN: Dual-stream Physics-informed Graph Convolutional Network for Reliable Pedestrian Trajectory Prediction–
DSPIGCN is introduced, a dual-stream physics-informed graph convolutional network that fuses domain physics with deep representation learning and generates physically consistent paths, especially for pedestrians exhibiting steady motion patterns.
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- TrapSuffix: Proactive Defense Against Adversarial Suffixes in Jailbreaking–
TrapSuffix is proposed, a lightweight fine-tuning approach that injects trap-aligned behaviors into the base model without changing the inference pipeline, providing both strong defense and reliable traceability in jailbreak mitigation.
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- Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution–
AutoCO is presented, an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning and highlights AutoCO as a principled and effective path toward proactive, verifiable LLM-driven optimization.
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- NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models–
A novel framework that integrates LLMs with SOAR to autonomously produce generative symbolic rules from natural language, and introduces a novel Execution-Grounded Generator-Critic mechanism that enables smaller-parameter models to achieve better performance than larger counterparts.
- Label Forensics: Interpreting Hard Labels in Black-Box Text Classifier–
This paper introduces label forensics, a black-box framework that reconstructs a label's semantic meaning and further validate the framework on an undocumented HuggingFace classifier, enabling fine-grained label interpretation and supporting responsible AI auditing.
- An Online Human-Aware Behavior Planning Method for Nondeterministic UAV System Under Probabilistic Model Checking–
Experiments and analysis show that the results of the online behavior planning are consistent with the task logic at the semantic level, indicating the correctness of the proposed method.
- Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events–
A Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation and an LLM-based role-playing agent to enhance interpretability.
- Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach–
This paper proposes a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0, and designs the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities.
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- Exploration and Practical Teaching Improvement of “Game Simulation & Modeling” Course–
The teaching group created a simulation competition platform independently for theoretical and practical teaching of “Game Simulation & Modeling” course, and the introduction of competition platform has played a good role in stimulating students’ learning motivation.
- Design of Simulation Competition Platform Based on Cognitive Behavior Modeling–
This paper elaborates the proposed platform from three aspects, including demand analysis, platform design, and content design, to cultivate student’s team collaboration and innovation capability, and improve their learning motivation.
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- Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad‐Trees–
EQ LRTS (encoded quad-tree based LRTS) possesses the ability of quickly escaping from heuristic depressions with less state revisitations, and is a much more memory-efficient method than other data structures expressing quad-trees.
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- Cooperative Behavior Modeling Technology in CGF–
According to requirements and the state of art of existing research, a CGF Agent cognitive architecture with cooperative behavior capability was brought forward, then a cooperative behavior modeling framework was designed, and detailed discussion about the correlations of each sub modules and their key implement technologies were given.
- Formation of Task-oriented CGF Entities Joint Intention–
Experimental results show that the proposed task-oriented joint intention formation method can effectively improve the formation efficiency of joint intention and the capability of collaboration, as well as the reality of behavior models.
- Disaster emergency response modeling and simulation–
The test to the prototype system which is implemented as the framework was carried out shows that it can efficiently meet the requirements of multi-agents emergency simulation.
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- Research and design of real time estimation systems–
The logical structure of the system is constructed with three levels of data resource, component and application, including sample collection, real time estimation, result display, replay control and rollback control.
- Design of HLA Simulation Database Based on Object-Relational Features–
This paper explores a method to design and develop an extensible HLA simulation database by using the object-relational database technique, and provicles the solution based on HLA object model to implement, manage and use the extensible object- Relational simulation database.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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