GenAI4ELab/papercli-papers huggingface dataset:
EAVIT: Efficient and Accurate Human Value Identification From Text Data via LLMs
Authors: Wenhao Zhu; Yuhang Xie; Guojie Song; Xin Zhang
Venue: IJCAI 2025 (main)

Abstract: The rapid evolution of large language models (LLMs) has revolutionized various fields, including the identification and discovery of human values within text data. While traditional NLP models, such as BERT, have been employed for this task, their ability to represent textual data is significantly outperformed by emerging LLMs like GPTs. However, the performance of online LLMs often degrades when handling long contexts required for value identification, which also incurs substantial computational costs. To address these challenges, we propose EAVIT, an efficient and accurate framework for human value identification that combines the strengths of both locally fine-tunable and online black-box LLMs. Our framework employs a value detector—a small, local language model—to generate initial value estimations. These estimations are then used to construct concise input prompts for online LLMs, enabling accurate final value identification. To train the value detector, we introduce explanation-based training and data generation techniques specifically tailored for value identification, alongside sampling strategies to optimize the brevity of LLM input prompts. Our approach effectively reduces the number of input tokens by up to 1/6 compared to directly querying online LLMs, while consistently outperforming traditional NLP methods and other LLM-based strategies.
Learning Causally Disentangled Representations for Fair Personality Detection
Authors: Yangfu Zhu; Meiling Li; Yuting Wei; Di Liu; Yuqing  Li; Bin Wu
Venue: IJCAI 2025 (main)

Abstract: Personality detection aims to identify the personality traits implied in social posts. Existing methods mainly focus on learning the mapping between user-generated posts and personality trait labels but inevitably suffer from potential harm caused by individual bias, as these posts are written by authors from different backgrounds. Learning such spurious associations between posts and traits may lead to the formation of stereotypes, ultimately restricting the detection of personality in different kind of individual. To tackle the issue, we first investigate individual bias in personality detection from the causality perspective. We propose an Interventional Personality Detection Network (IPDN) to learn implicit confounders in user-generated posts and exploit the true causal effect to train the detection model. Specifically, our IPDN disentangled the causal and biased features behind user-generated posts, and then the biased features are accumulatively clustered as confounder prototypes as the training iterations increase. In parallel, the reconstruction network is reused to approximate backdoor adjustment on raw posts, ensuring that traits see each confounder equally before detection. Extensive experiments conducted on three real-world datasets demonstrate that our IPDN outperforms state-of-the-art methods in personality detection.
Multi-Label Text Classification with Label Attention Aware  and Correlation Aware Contrastive Learning
Authors: Zhengzhong Zhu; Pei Zhou; Zeting Li; Kejiang Chen; Jiangping Zhu
Venue: IJCAI 2025 (main)

Abstract: Multi-label text classification (MLTC) is a challenging task where each document can be associated with multiple interdependent labels. This task is complicated by two key issues: the intricate correlations among labels and the partial overlap between labels and text relevance. Existing methods often fail to capture the semantic dependencies between labels or struggle to handle the ambiguities caused by partial overlaps, resulting in suboptimal representation learning. To address these challenges, we propose the Unified Contextual and Label-Aware Framework (UCLAF), which integrates a Label Attention Aware Network(LAN) and Correlation Aware Contrastive Learning (CACL) in a synergistic design. The Label Attention Aware Network explicitly models label dependencies by embedding labels and texts into a shared semantic space, aligning text representations with label semantics. Meanwhile, Correlation Aware Contrastive Learning refines these representations by dynamically modeling sample-level relationships, leveraging a contrastive loss function that accounts for the proportional overlap of labels between samples. This complementary approach enables UCLAF to jointly address complex label correlations and partial label overlaps. Extensive experiments on benchmark datasets demonstrate that UCLAF significantly outperforms state-of-the-art methods, showcasing its effectiveness in improving both representation learning and classification performance in MLTC tasks. We will release our code after the paper is accepted.
Dynamic Replanning for Improved Public Transport Routing
Authors: Abdallah Abuaisha; Bojie Shen; Daniel D. Harabor; Peter J. Stuckey; Mark Wallace
Venue: IJCAI 2025 (main)

Abstract: Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a "pull" approach, where users manually request replanning, and a novel "push" approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning.
Sketch Decompositions for Classical Planning via Deep Reinforcement Learning
Authors: Michael Aichmüller; Hector Geffner
Venue: IJCAI 2025 (main)

Abstract: In planning and reinforcement learning, the identification of common subgoal structures across problems is important when goals are to be achieved over long horizons. Recently, it has been shown that such structures can be expressed as feature-based rules, called sketches, over a number of classical planning domains. These sketches split problems into subproblems which then become solvable in low polynomial time by a greedy sequence of IW(k) searches. Methods for learning sketches using feature pools and min-SAT solvers have been developed, yet they face two key limitations: scalability and expressivity. In this work, we address these limitations by formulating the problem of learning sketch decompositions as a deep reinforcement learning (DRL) task, where general policies are sought in a modified planning problem where the successor states of a state s are defined as those reachable from s through an IW(k) search. The sketch decompositions obtained through this method are experimentally evaluated across various domains, and problems are regarded as solved by the decomposition when the goal is reached through a greedy sequence of IW(k) searches. While our DRL approach for learning sketch decompositions does not yield interpretable sketches in the form of rules, we demonstrate that the resulting decompositions can often be understood in a crisp manner.
LTLf+ and PPLTL+: Extending LTLf and PPLTL to Infinite Traces
Authors: Benjamin Aminof; Giuseppe De Giacomo; Sasha Rubin; Moshe Y. Vardi
Venue: IJCAI 2025 (main)

Abstract: We study two logics, LTLf+ and PPLTL+, to express properties of infinite traces, that are based on the linear-time temporal logics LTLf and PPLTL on finite traces. LTLf+/PPLTL+ use levels of Manna and Pnueli’s LTL safety-progress hierarchy, and thus have the same expressive power as LTL. However, they also retain a crucial characteristic of reactive synthesis for the base logics: the game arena for strategy extraction can be derived from deterministic finite automata (DFA). Consequently, these logics circumvent the notorious difficulties associated with determinizing infinite trace automata, typical of LTL synthesis. We present optimal DFA-based technique for solving reactive synthesis for LTLf+ and PPLTL+. Additionally, we adapt these algorithms to optimally solve satisfiability and model-checking for these two logics.
Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
Authors: Ángel Aso-Mollar; Diego Aineto; Enrico Scala; Eva Onaindia
Venue: IJCAI 2025 (main)

Abstract: In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme. We develop a best-first, heuristic search algorithm that operates over infinite decision spaces defined by control parameters and prove a notion of completeness in the limit under certain conditions. Our algorithm leverages the concept of delayed partial expansion, where a state is not fully expanded but instead incrementally expands a subset of its successors. Our results demonstrate that this novel search algorithm is a competitive alternative to existing approaches for solving planning problems involving control parameters.
Online Planning in MDPs with Stochastic Durative Actions
Authors: Tal Berman; Ronen I. Brafman; Erez Karpas
Venue: IJCAI 2025 (main)

Abstract: Stochastic planning problems are typically modeled as Markov Decision Processes, in which actions are assumed to be instantaneous and applied sequentially. Yet, real-world actions often have durations and are applied concurrently. This paper presents an online planning approach that can deal with durative actions with stochastic outcomes. Our approach relies on Monte Carlo Tree Search with a new backpropagation procedure and temporal reasoning techniques that address the need to not only choose which action to execute, but also when to execute it. We also introduce a novel heuristic that combines reasoning about time and probabilities. Overall, we present the first online planner for stochastic temporal planning, solving a richer problem representation than previous work while achieving state-of-the-art empirical results.
Rolling in Classical Planning with Conditional Effects and Constraints
Authors: Matteo Cardellini; Enrico Giunchiglia
Venue: IJCAI 2025 (main)

Abstract: In classical planning, conditional effects (CEs) allow modelling non-idempotent actions, where the resulting state may depend on how many times each action is consecutively repeated. Though CEs have been widely studied in the literature, no one has ever studied how to exploit rolling, i.e., how to effectively model the consecutive repetition of an action. In this paper, we fill this void by (i) showing that planning with CEs remains PSPACE-complete even in the limit case of problems with a single action, (ii) presenting a correct and complete planning as satisfiability encoding exploiting rolling while effectively dealing with constraints imposed on the set of reachable states, and (iii) theoretically and empirically showing its substantial benefits.
Concurrent Planning and Execution Using Dispatch-Dependent Values
Authors: Andrew Coles; Erez Karpas; Eyal Shimony; Shahaf Shperberg; Wheeler Ruml
Venue: IJCAI 2025 (main)

Abstract: Agents operating in the real world must cope with the fact that time passes while they plan. In some cases, such as under tight deadlines, the only way for such an agent to achieve its goal is to execute an action before a complete plan has been found. This problem is called Concurrent Planning and Execution (CoPE). Previous work on CoPE relied on a value function that assumes search will finish before actions are executed, causing the agent to be overly pessimistic in many situations. In this paper, we define a new value function that takes into account the agent's ability to dispatch actions incrementally. This allows us to devise a much simpler algorithm for concurrent planning and execution. An experimental evaluation on problems with time pressure shows that the new method significantly outperforms the previous state-of-the-art.
Solving MDPs with LTLf+ and PPLTL+ Temporal Objectives
Authors: Giuseppe De Giacomo; Yong Li; Sven Schewe; Christoph Weinhuber; Pian Yu
Venue: IJCAI 2025 (main)

Abstract: The temporal logics LTLf+ and PPLTL+ have recently been introduced to express objectives over infinite traces. These logics are appealing because they match the expressive power of LTL on infinite traces while enabling efficient DFA-based techniques, which have been crucial to the scalability of reactive synthesis and adversarial planning in LTLf and PPLTL over finite traces. In this paper, we demonstrate that these logics are also highly effective in the context of MDPs. Introducing a technique tailored for probabilistic systems, we leverage the benefits of efficient DFA-based methods and compositionality. This approach is simpler than its nonprobabilistic counterparts in reactive synthesis and adversarial planning, as it accommodates a controlled form of nondeterminism ("good for MDPs") in the automata when transitioning from finite to infinite traces. Notably, by exploiting compositionality, our solution is both implementation-friendly and well-suited for straightforward symbolic implementations.
Improvements to the Generate-and-Complete Approach to Conformant Planning
Authors: Liangda Fang; Min Zhan; Jin Tong; Xiujie Huang; Ziliang Chen; Quanlong Guan
Venue: IJCAI 2025 (main)

Abstract: Conformant planning is a computationally challenging task that generates an action sequence to achieve goal condition with uncertain initial states and non-deterministic actions. The generate-and-complete (in short, GC) approach shows superior performance on conformant planning, which iteratively enumerates the solution of a planning subproblem for a single initial state and attempts to extend it for all initial states until a conform solution is found. However, two major drawbacks of the GC approach hinder its performance: the computational overhead due to state exploration and the insertion of many redundant actions. To overcome the above drawbacks, we improve both verification and completion procedures. Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF.
Fast and Stronger Lower Bounds for Planar Euclidean Shortest Paths
Authors: Stefan Funke; Daniel Koch; Claudius Proissl; Christian Staib; Felix Weitbrecht
Venue: IJCAI 2025 (main)

Abstract: We consider the problem of quickly providing strong lower bounds for the planar Euclidean shortest path (ESP) problem. Such lower bounds are crucial for guiding the search in A* type approaches or for proving quality guarantees for algorithms that compute approximate solutions. Our contributions are two-fold: we show how to simplify ESP instances such that computing and storing a visibility graph becomes feasible while distances within the simplified instance are guaranteed to constitute lower bounds for the original problem instance. Furthermore we show how to precompute a space efficient data structure that allows to perform distance queries on visibility graphs within few microseconds with negligible space overhead.
Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
Authors: Maris F. L. Galesloot; Roman Andriushchenko; Milan Ceska; Sebastian Junges; Nils Jansen
Venue: IJCAI 2025 (main)

Abstract: Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not be robust against perturbations in the environment. Hidden-model POMDPs (HM-POMDPs) capture sets of different environment models, that is, POMDPs with a shared action and observation space. The intuition is that the true model is hidden among a set of potential models, and it is unknown which model will be the environment at execution time. A policy is robust for a given HM-POMDP if it achieves sufficient performance for each of its POMDPs. We compute such robust policies by combining two orthogonal techniques: (1) a deductive formal verification technique that supports tractable robust policy evaluation by computing a worst-case POMDP within the HM-POMDP, and (2) subgradient ascent to optimize the candidate policy for a worst-case POMDP. The empirical evaluation shows that, compared to various baselines, our approach (1) produces policies that are more robust and generalize better to unseen POMDPs, and (2) scales to HM-POMDPs that consist of over a hundred thousand environments.
Interval Selection with Binary Predictions
Authors: Christodoulos Karavasilis
Venue: IJCAI 2025 (main)

Abstract: Following a line of work that takes advantage of vast machine-learned data to enhance online algorithms with (possibly erroneous) information about future inputs, we consider predictions in the context of deterministic algorithms for the problem of selecting a maximum weight independent set of intervals arriving on the real line. We look at two weight functions, unit (constant) weights, and weights proportional to the interval’s length. In the classical online model of irrevocable decisions, no algorithm can achieve constant competitiveness. In this setting, we show that a simple algorithm that is faithful to the predictions is optimal, and achieves an objective value of at least OPT - η, with η being the total error in the predictions, both for unit, and proportional weights. When revocable acceptances (a form of preemption) are allowed, the optimal deterministic algorithm for unit weights is 2k-competitive, where k is the number of different interval lengths. We give an algorithm with performance OPT − η (and therefore 1-consistent), that is also (2k + 1)-robust. For proportional weights, there is an optimal (2φ + 1)-competitive algorithm, where φ is the golden ratio. We present an algorithm with parameter λ > 1 that is 3λ / (λ - 1) -consistent, and (4λ^2 + 2λ) / (λ - 1)-robust. Although these bounds are not tight, we show that for λ > 3.42 we achieve consistency better than the optimal online guarantee, while maintaining bounded robustness. We conclude with some experimental results on real-world data that complement our theoretical findings, and show the benefit of prediction algorithms for online interval selection, even in the presence of high error.
Partially Observable Reference Policy Programming
Authors: Edward Kim; Hanna Kurniawati
Venue: IJCAI 2025 (main)

Abstract: This paper proposes Partially Observable Reference Policy Programming, a novel anytime online approximate POMDP solver which samples meaningful future histories very deeply while simultaneously forcing a gradual policy update. We provide theoretical guarantees for the algorithm’s underlying scheme which say that the performance loss is bounded by the average of the sampling approximation errors rather than the usual maximum; a crucial requirement given the sampling sparsity of online planning. Empirical evaluations on two large-scale problems with dynamically evolving environments—including a helicopter emergency scenario in the Corsica region requiring approximately 150 planning steps—corroborate the theoretical results and indicate that our solver considerably outperforms current online benchmarks.
Optimal Planning to Coordinate Science Data Collection and Downlink for a Constellation of Agile Satellites with Limited Storage
Authors: Richard Levinson; Vinay Ravindra; Sreeja Roy-Singh
Venue: IJCAI 2025 (main)

Abstract: We present a novel Mixed Integer Linear Program formulation that produces optimal plans for a constellation of remote sensing satellites. The generalized formulation is applied to an operational NASA constellation to improve wildfire danger prediction. The planner generates integrated data collection and downlink plans for multiple agile satellites with limited storage capacity, minimum energy requirements, and temporal constraints. Observation targets and modes are associated with science rewards. The planner maximizes the aggregate rewards collected for all observations on all satellites. Our generalized model for integrated data collection and downlink uses a novel interval-based abstraction called Data Cycles, without time-indexed variables. Data cycles organize the multitude of observation and downlink opportunities from 1 second granularity into sequences of data collection and downlink intervals. Experiments using large-scale real-world data yield optimal 24-hr plans for an eight satellite constellation, which capture 99% of the ~23,000 available targets and 99.9% of available science rewards.
Semi-Clairvoyant Scheduling of Speculative Decoding Requests to Minimize LLM Inference Latency
Authors: Ruixiao Li; Fahao Chen; Peng Li
Venue: IJCAI 2025 (main)

Abstract: Speculative decoding accelerates Large Language Model (LLM) inference by employing a small speculative model (SSM) to generate multiple candidate tokens and verify them using the LLM in parallel. This technique has been widely integrated into LLM inference serving systems. However, inference requests typically exhibit uncertain execution time, which poses a significant challenge of efficiently scheduling requests in these systems. Existing work estimates execution time based solely on predicted output length, which could be inaccurate because execution time depends on both output length and token acceptance rate of verification by the LLM. In this paper, we propose a semi-clairvoyant request scheduling algorithm called Least-Attained/Perceived-Service for Speculative Decoding (LAPS-SD). Given a number of inference requests, LAPS-SD can effectively minimize average inference latency by adaptively scheduling requests according to their features during decoding. When token acceptance rate is dynamic and execution time is difficult to estimate, LAPS-SD maintains multiple priority queues and allows request execution preemption across different queues. Once the token acceptance rate becomes stable, LAPS-SD can accurately estimate the execution time and schedule requests accordingly. Extensive experiments show that LAPS-SD reduces inference latency by approximately 39% compared to state-of-the-art scheduling methods.