The Dynamic Flexible Job Shop Scheduling Problem (DFJSP) necessitates a trade-off between instant reaction to stochastic disturbances and global optimization of production goals. Conventional priority rules are insufficiently flexible to handle complex disruptions, whereas learning-based approaches often compromise interpretability or fail to generalize across problem scales. Although Large Language Models (LLMs) offer advanced reasoning capabilities to bridge this gap, their substantial inference latency is incompatible with the millisecond-level decision cycles of industrial control systems. To resolve this conflict, we introduce RACE-Sched, an asynchronous agent-based framework that decouples policy execution from logical reasoning via a dual-stream architecture. The Reactive Stream executes low-latency symbolic heuristics to enable real-time dispatching, while the parallel Deliberative Stream leverages an LLM to synthesize, validate, and evolve these rules. Candidate rules undergo rigorous testing in a sandbox and are deployed via atomic updates, ensuring safety without blocking the control loop. Additionally, a semantic rule repository indexes validated heuristics for retrieval-based initialization which enhances transferability across problem scales. Extensive evaluations on GEN-Bench, MK-Bench, and JMS-Bench demonstrate that RACE-Sched outperforms leading Deep Reinforcement Learning and other LLM-based baselines. This approach harmonizes real-time constraints with long-horizon reasoning to achieve superior solution quality and robust adaptation to dynamic events.
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Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic information in remote sensing. Motivated by this observation, we propose Uni-RS, the first unified multimodal model tailored for remote sensing, to explicitly address the spatial asymmetry between understanding and generation. Specifically, we first introduce explicit Spatial-Layout Planning to transform textual instructions into spatial layout plans, decoupling geometric planning from visual synthesis. We then impose Spatial-Aware Query Supervision to bias learnable queries toward spatial relations explicitly specified in the instruction. Finally, we develop Image–Caption Spatial Layout Variation to expose the model to systematic geometry-consistent spatial transformations. Extensive experiments across multiple benchmarks show that our approach substantially improves spatial faithfulness in text-to-image generation, while maintaining strong performance on multimodal understanding tasks like image captioning, visual grounding, and VQA tasks.
Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling, most continuous (PO)MDP solvers do not exploit gradient-based action optimization. We propose Action-Gradient MCTS (AGMCTS), a framework that combines global tree search with local gradient-based action refinement, while maintaining consistent value estimates. We provide three key theoretical contributions: (1) an action score gradient theorem for particle belief states; (2) the Multiple Importance Sampling (MIS) Tree that supports frequent action-branch updates by reusing prior samples without introducing estimator drift; and (3) tractable action score gradients for smooth generative models using the Area Formula. Empirical results demonstrate that AGMCTS outperforms state-of-the-art sample-based solvers in multiple challenging continuous MDP and POMDP benchmarks.
Hybrid tables, also referred to as `smart' in the literature, represent a valuable modeling technique within Constraint Programming (CP). These tables allow us to handle disjunctive cases (constraining expressions) in a compact and structured way, by authorizing tuples (table entries) to contain simple unary and binary arithmetic restrictions (similar to internal constraints). In this paper, we show the practical interest of using hybrid tables for planning-like combinatorial puzzles, when the transition from one state to the next can be encoded by a single hybrid table. Experimental results show that these hybrid models exhibit greater compactness and efficiency in solving compared to their conventional counterparts.
Many multiagent systems depend on collective decisions made by self-interested agents, which raises deep questions about coalition formation and stability. We study social choice with endogenous, outcome-contingent transfers, where agents voluntarily form contracts that redistribute utility depending on the collective decision, allowing for fully strategic coalition formation. We show that under consensus rules, individually rational strong Nash equilibria (IR–SNE) always exist, implementing welfare-maximizing outcomes with feasible transfers, and provide a simple, efficient algorithm to construct them. For more general anonymous, monotonic, and resolute rules, we identify necessary conditions for viable deviations, significantly limiting the possibility of destabilizing coalitions. By bridging cooperative and noncooperative perspectives, our approach shows that transferable utility can achieve core-like stability, restoring efficiency and budget balance even where classical impossibility results generally apply. Overall, this framework offers a practical and robust way to coordinate large-scale strategic multiagent systems.
We study a robust scheduling problem on identical machines in which machines may fail after the initial assignment. The goal is to compute an initial schedule together with a recovery strategy that minimizes the post-failure makespan, under the constraint that jobs on available machines cannot be moved. We consider two failure models: a strong adversary, which selects the failed machines, and a weak adversary, which selects only the number of failures. For up to k failures, we give algorithms with robustness ratios 1.618 for the strong adversary and 1.5 for the weak adversary. For the single-failure case, k = 1, we obtain best possible ratios 1.387 and 1.281, respectively, matching our lower bounds.
We introduce and study the multi-agent stochastic shortest path (MSSP) problem, in which k agents strive to reach a target state, aiming to minimize the expected time to reach the target by any agent. We analyze the computational and strategy-complexity of the problem in both autonomous and coordinated settings, and we design efficient strategy-synthesis algorithms. The algorithms are experimentally evaluated on instances of increasing size against natural baselines.
Reachability games are two-player games played on a graph, where the objective of REACH player is to reach the target set whereas the objective of SAFE player is to stay away from the target set. Reachability games have important applications in artificial intelligence and reactive synthesis, and many of these applications give rise to infinite-state reachability games. In this paper, we study turn-based reachability games on infinite-state graphs defined over valuations of a finite set of real variables. We consider the problem of determining the existence of and computing a winning strategy for REACH player. Our contributions are twofold. First, we propose ranking certificates for reachability games, a sound and complete proof rule for proving that REACH player has a winning strategy from the specified initial state. Second, we consider polynomial reachability games, where transitions and objectives are described by polynomial constraints over real variables, and propose a fully automated algorithm for computing a winning strategy for REACH player together with a formal correctness witness in the form of a ranking certificate. The algorithm is sound, semi-complete, and runs in sub-exponential time. Our experiments demonstrate the ability of our method to solve challenging examples from the literature that were out of the reach of existing methods. Specifically, for the classical Cinderella-Stepmother game, we are able to compute an optimal winning strategy for an arbitrary precision parameter for the first time.
In human-aware vision-and-language navigation (HA-VLN), agents need to follow instructions while navigating safely among people in crowded environments. Existing methods often struggle to effectively transform forward-looking risk prediction into decision-making basis, resulting in intelligent agents being prone to collisions in crowded scenarios and taking overly cautious actions, thereby reducing navigation efficiency. Moreover, it is difficult for high-level decisions based on social norms to be stably implemented in dynamic crowd scenarios, which can easily lead to behavioral jitter and planning deviation. In this work, we propose the AHead-VLN, a navigation framework that incorporates forward-looking risk information in the reasoning process. Specifically, we designed the path risk association (PRA) module, which compiles future risks into structured risk events aligned with each candidate path, making risk information decision-ready for the large language model (LLM) reasoning. To balance executability and decision stability, we have further designed the hierarchical LLM planner (HLP) with two specialized components. The ``Brain'' LLM makes route choices based on instructions and risks, while the ``Cerebellum'' LLM prepares executable action scripts for candidate routes and retrieves the appropriate script for execution. Experiments on the HA-VLN benchmark show that AHead-VLN improves success while enhancing safety compared to strong baselines.
Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses. However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages. This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that captures key semantic features of trajectories in an efficient way, enabling more meaningful comparisons between them. Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.
Accurate prediction of prerequisite relations among concepts is important for course planning and intelligent tutoring systems. Existing text-based methods are frequently contaminated by noise such as redundant phrasing, ambiguous sentences, and domain-specific colloquialisms. Moreover, previous methods based on graph structures may have failed to capture the complex interactions between concepts and documents, an information gap exists between the two approaches. Therefore, we propose an LLM Enhancement and Concept-Document Interactive Modeling for Prerequisite Relation Prediction (LECDPR) model. Concretely, LECDPR elicits an LLM via two-stage prompting to distill concept descriptions and contextual evidence from documents, resulting in semantic space embeddings of concepts. Then, the complex interactive relations between concepts and documents are modeled through three propagation mechanisms to produce relational space embeddings, aligns the two embeddings through random masking and contrastive learning, and adaptively fuses the resulting embeddings. Finally, the fused concept representations are used for more accurate prerequisite relation prediction. Extensive experiments on the public UCD, Lecture Bank, and MOOC datasets show that LECDPR outperforms ten baselines on ACC, AUC, and F1. Our code is available at https://github.com/lecdpr/LECDPR.
Reliable forecasting of cross-regional terrorism fatalities is essential for early warning and security planning. However, real-world terrorism data are characterized by extreme sparsity, abrupt volatility, and weak, rapidly shifting non-Euclidean spatial relationships, posing substantial challenges for existing spatiotemporal forecasting models in learning stable multi-scale dependencies. To address these challenges, we propose a robust spatiotemporal forecasting framework, termed WBMCF. In the temporal dimension, a single-level Discrete Wavelet Transform (DWT) is introduced to stabilize temporal structures, and a Bidirectional Gated Mamba module is employed to capture stage-dependent long-range temporal dependencies; additionally, in the spatial dimension, a topology-agnostic Convolutional Feed-Forwar+d Network (ConvFFN) is incorporated to model cross-regional dependencies via implicit feature coupling, thereby eliminating reliance on fixed spatial topologies. Extensive experiments on five regional subsets of the Global Terrorism Database (GTD) demonstrate that WBMCF achieves stable and competitive forecasting performance under sparse and noisy conditions. The source code is available at https://github.com/weibaozhong/WBMCF.
Low-Rank Adaptation (LoRA) is a popular approach that enables large language models (LLMs) to quickly adapt to domain-specific tasks by adding lightweight trainable adapters. Existing multi-LoRA serving systems typically exploit parameter sharing to serve hundreds of LoRA models with a single base model. However, most systems rely on first-come-first-serve (FCFS) scheduling, which can incur severe queuing delay and lead to excessive adapter memory usage, squeezing KV cache space and reducing concurrency and throughput. To address these challenges, we propose M-LoRA, a memory-aware multi-LoRA serving system that reduces queuing delay and improves throughput through efficient request scheduling guided by fine-grained memory modeling. M-LoRA consists of three components: (1) Multi-LoRA Length Predictor, which estimates the output length and KV-cache demand of each request; (2) Memory-aware Speculative Scheduler, which dispatches requests based on predicted KV-cache and adapter memory requirements to maximize throughput under a fixed GPU memory budget; and (3) Recovery Mechanism, which corrects mispredictions while preserving generation quality. Compared to state-of-the-art LoRA serving systems, M-LoRA reduces average per-token latency by 87% and improves throughput by up to 1.7x.
In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations. Existing multithreaded strategies struggle to match thread resources before or during execution, causing resource contention, scheduling overhead, and reduced throughput. Through empirical analysis, we identify the ratio of task execution time to scheduling time as the key factor determining the optimal thread count. Building on this insight, we propose AutoThread, a hybrid adaptive thread-tuning method for mitigating simulation bottlenecks in RL inference. AutoThread employs a Physics-Informed Neural Operator (PINO) as a thread-count predictor and incorporates a finite-source M/M/1 queueing model to constrain and guide prediction, enabling fast and accurate estimation under dynamic workloads. It further performs load-aware online fine-tuning to compensate for prediction errors and refine resource allocation. Experiments show that AutoThread improves average speedup by 18.4% over static strategies, achieves average throughput of 1.7× and 1.8× that of XGBoost and Reinforcer, respectively, and reduces execution time by up to 83.8% compared with state-of-the-art methods. Our code and dataset are publicly available at https://github.com/suchenjm/AutoThread.
Stochastic games have become a prevalent framework for studying long-term multi-agent interactions, especially in the context of multi-agent reinforcement learning. In this work, we comprehensively investigate the concept of constant-memory strategies in stochastic games. We first establish some results on best responses and Nash equilibria for behavioral constant-memory strategies, followed by a discussion on the computational hardness of best responding to mixed constant-memory strategies. Those theoretic insights are later verified on several sequential decision-making testbeds, including the Iterated Prisoner's Dilemma, the Iterated Traveler's Dilemma, and the Pursuit domain. This work aims to enhance the understanding of theoretical issues in single-agent planning under multi-agent systems, and uncover the connection between decision models in single-agent and multi-agent contexts. The codebase and the full version of this paper is available at github.com/Fernadoo/Const-Mem.
Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-intensive and highly context-specific. We present Principles2Plan, an interactive research prototype demonstrating how a human and a Large Language Model (LLM) can collaborate to produce context-sensitive ethical rules and guide automated planning. A domain expert provides the planning domain, problem details, and relevant high-level principles such as beneficence and privacy. The system generates operationalisable ethical rules consistent with these principles, which the user can review, prioritise, and supply to a planner to produce ethically-informed plans. To our knowledge, no prior system supports users in generating principle-grounded rules for classical planning contexts. Principles2Plan showcases the potential of human-LLM collaboration for making ethical automated planning more practical and feasible.
Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this paper, we introduce VitalDiagnosis, an LLM-driven ecosystem designed to shift chronic disease management from passive monitoring to proactive, interactive engagement. By integrating continuous data from wearable devices with the reasoning capabilities of LLMs, the system addresses both acute health anomalies and routine adherence. It analyzes triggers through context-aware inquiries, produces provisional insights within a collaborative patient–clinician workflow, and offers personalized guidance. This approach aims to promote a more proactive and cooperative care paradigm, with the potential to enhance patient self-management and reduce avoidable clinical workload.
AgentGraph: Trace-to-Graph Platform for Interactive Analysis and Robustness Testing in Agentic AI Systems
PDF ↗Modern Agentic AI systems plan, reason, and act across multiple steps, creating execution patterns that are difficult to interpret. Existing observability platforms track prompt I/O and operational metrics but require manual inspection of traces to reconstruct structure and reasoning. We present AgentGraph, which converts execution logs into interactive knowledge graphs and actionable insights. Nodes represent agents, tasks, tools, data inputs/outputs, and humans, while typed edges capture relations such as inputs consumed, tasks delegated or sequenced, tools required or used, outputs produced and delivered, and interventions from agents or humans. Each graph element links to its exact trace span, ensuring verifiability. Building on this representation, AgentGraph enables two analyses: qualitative trace-grounded failure detection and optimisation recommendations, and quantitative robustness evaluation via perturbation testing and causal attribution.
Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants.
RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents
PDF ↗Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform that jointly simulates natural hazards and human response. RESPOND couples high-fidelity flood AI forecasting an agent-based model of human behavior. LLM modules improve each agent decision-making, enabling context-aware reasoning over alerts, road closures, social signals, and changing water levels. The system simulates evacuation flows, resource seeking, and communication patterns producing actionable outputs for emergency management, urban planning, and policy. In the live demo one can run what-if or predicted scenarios, adjust assumptions, and observe emergent population behavior and risk hot spots in real time. By tightly coupling dynamic hazards with LLM-driven multi-agent behavior, RESPOND moves beyond fragmented tools and offers a practical, integrated platform for disaster preparedness and response.