GenAI4ELab/papercli-papers huggingface dataset:
LAMDA: Two-Phase HPO via Learning Prior from Low-Fidelity Data
Authors: Fan Li; Shengbo Wang; Ke Li
Venue: AAAI 2026 (main)

Abstract: Hyperparameter Optimization (HPO) is crucial in machine learning, aiming to optimize hyperparameters to enhance model performance. Although existing methods that leverage prior knowledge—drawn from either previous experiments or expert insights—can accelerate optimization, acquiring a correct prior for a specific HPO task is non-trivial. In this work, we propose to relieve the reliance on external knowledge by learning a reliable prior {directly} from low-fidelity (LF) problems. We introduce {Lamda}, an algorithm-agnostic framework designed to boost any baseline HPO algorithm. Specifically, {Lamda} operates in two phases: (1) it learns a reliable prior by exploring the LF landscape under limited computational budgets, and (2) it leverages this learned prior to guide the HPO process. We showcase how the {Lamda} framework can be integrated with various HPO algorithms to boost their performance, and further conduct theoretical analysis towards the integrated Bayesian optimization and bandit-based Hyperband. We conduct experiments on 56 HPO problems spanning diverse domains and model scales. Results show that {Lamda} consistently enhances its baseline algorithms. Compared to nine state-of-the-art HPO algorithms, our {Lamda} variant achieves the best performance in 51 out of 56 HPO tasks while it is the second best algorithm in the other 5 cases.
Priority-Based Graph-Enhanced Reinforcement Learning for Robust Analog Circuit Optimization
Authors: Jintao Li; Zhenxin Chen; Sicheng He; Ao-Jin Li; Shui Yu
Venue: AAAI 2026 (main)

Abstract: A primary motivation for analog integrated circuit (IC) design automation is the inefficiency of manual design in meeting increasingly stringent specifications, which often involve over 10 objectives. 
Recent advances in reinforcement learning (RL) emerge as a promising method, yet gaps remain when considering full design specifications, especially under process-voltage-temperature (PVT) variations.
Excessive objectives lead to diminished reward signals, while varying PVT conditions result in conflicting gradients, both of which result in inefficient exploration.
To address these, we propose a priority-based graph-enhanced RL framework.
Specifically, using fuzzy logic converts quantitative rewards into qualitative priority signals, mitigating reward deterioration and enhancing exploration via entropy regularization. 
Furthermore, a graph-based representation compresses high-dimensional objective spaces under PVT variations into low-dimensional manifolds, enabling dynamic resource allocation to variation-sensitive regions and resolving gradient conflicts. 
Empirical results on various real-world analog ICs demonstrate that our method significantly outperforms existing RL algorithms, achieving superior solution quality and reducing simulation overhead.
Nested Depth Search
Authors: Junkang Li; Tristan Cazenave; Swann Legras; Arthur Queffelec; Veronique Ventos
Venue: AAAI 2026 (main)

Abstract: Nested Monte Carlo Search (NMCS) has numerous applications, ranging from chemical retrosynthesis to quantum circuit design. We propose a generalization of NMCS that we named Nested Depth Search (NDS), in which a fixed depth search is used during a higher-level playout to generate the states sent to lower-level exploration. We establish the runtime of NDS and provide algorithms to compute the exact probability distribution of sequences generated by NDS. Experiments with the Set Cover problem and the Multiple Sequence Alignment problem show that NDS outperforms NMCS with the same time budget.
A Solution Space Transformation-Guided Co-Evolution for Energy-Saving Distributed Heterogeneous Flexible Job Shop Scheduling
Authors: Tao Li; Xingchen Li; Haoyue Ma; Zhi-Hui Zhan
Venue: AAAI 2026 (main)

Abstract: Solving energy-saving distributed heterogeneous flexible job shop scheduling problem (ES-DHFJSP) aims to enhance industrial production efficiency while minimizing energy consumption. State-of-the-art co-evolutionary algorithms have emerged as effective approaches for addressing ES-DHFJSP. However, existing methodologies demonstrate compromised convergence rates and excessive computational overhead when confronted with vast search spaces. In this work, we propose a novel solution space transformation-guided co-evolution algorithm (SSTCE) to overcome this limitation. In SSTCE, we first establish an inter-job similarity metric and incorporate constrained hierarchical clustering with optimal leaf ordering (CHC-OLO) to generate clustered job sets, which are subsequently utilized for population initialization that achieves a favorable balance between convergence and diversity. To enhance search capability in expansive solution spaces, we devise a dynamic solution space transformation mechanism that effectively reduces inefficient searches within the algorithm. Furthermore, we develop tailored local search strategies leveraging domain-specific knowledge of DHFJSP properties. Extensive experimental evaluations across 20 benchmark instances demonstrate that SSTCE significantly outperforms existing evolutionary algorithms in solving ES-DHFJSP.
Post-Hoc Refinement for Multitask Symbolic Regression via Consensus-Accelerated Shapley Analysis
Authors: Xinyue Li; Wang Hu; Yu Zhang
Venue: AAAI 2026 (main)

Abstract: Multitask genetic programming (MTGP) is one of the primary methods for solving multitask symbolic regression (MTSR), the problem of discovering mathematical expressions for multiple interconnected tasks simultaneously. However, conventional MTGP approaches discard a wealth of valuable knowledge from the population of expressions due to their inherent “winner-take-all” selection criteria. To address this, we introduce MTGP with bidirectional cooperation and consensus-accelerated Shapley analysis (MTGP-BS), a method whose core is a novel post-hoc refinement framework that shifts from selection to synthesis. Our method first employs a consensus-accelerated Shapley analysis to reliably identify important subexpressions by multi-model attribution. Second, to supply this analysis with high-quality candidates, we design a bidirectional subexpression cooperative extraction method to create a refined archive of effective components by improving knowledge transfer and filtering out redundancies. These allow MTGP-BS to synthesize superior expressions by integrating knowledge dispersed throughout the entire population. On diverse MTSR problems, our algorithm statistically outperformed state-of-the-art approaches in 140 out of 160 direct comparisons, with its effectiveness and practical utility further verified by real-world case studies and in-depth ablation analyses.
Efficient Few-Step Solution Generation via Discrete Flow Matching for Combinatorial Optimization
Authors: Yuanshu Li; Di Wang; Wei Du; Xuan Wu; Peng Zhao; Yubin Xiao; You Zhou
Venue: AAAI 2026 (main)

Abstract: Combinatorial optimization problems (COPs) are fundamental to many real-world applications where efficiently producing high-quality solutions is critical. Recent advances in diffusion-based non-autoregressive models have reformulated solving COPs as a generative process, achieving promising results. However, almost all of these methods still suffer from accumulated errors and high inference costs due to the multi-step stochastic denoising process. To address these issues, we propose EFLOCO, an efficient discrete flow matching method for solving COPs, learning structured and deterministic solution trajectories. EFLOCO replaces noise-driven updates with smooth and guided transitions, thereby improves inference stability and quality. Furthermore, we introduce an adaptive time-step scheduler that makes more efforts in critical transition regions, yielding strong performance under few-step constraints. Experiments on standard Traveling Salesman Problems (TSPs) and Asymmetric TSPs (ATSPs) show that our method consistently outperforms both learning-based and heuristic baselines in terms of solution quality and inference speed.
Random is Faster than Systematic in Multi-Objective Local Search
Authors: Zimin Liang; Miqing Li
Venue: AAAI 2026 (main)

Abstract: Local search is a fundamental method in operations research and combinatorial optimisation. It has been widely applied to a variety of challenging problems, including multi-objective optimisation where multiple, often conflicting, objectives need to be simultaneously considered. In multi-objective local search algorithms, a common practice is to maintain an archive of all non-dominated solutions found so far, from which the algorithm iteratively samples a solution to explore its neighbourhood. A central issue in this process is how to explore the neighbourhood of a selected solution. In general, there are two main approaches: 1) systematic exploration and 2) random sampling. The former systematically explores the solution's neighbours until a stopping condition is met -- for example, when the neighbourhood is exhausted (i.e., the best improvement strategy) or once a better solution is found (i.e., first improvement). In contrast, the latter randomly selects and evaluates only one neighbour of the solution. One may think systematic exploration may be more efficient, as it prevents from revisiting the same neighbours multiple times. In this paper, however, we show that this may not be the case. We first empirically demonstrate that the random sampling method is consistently faster than the systematic exploration method across a range of multi-objective problems. We then give an intuitive explanation for this phenomenon using toy examples, showing that the superior performance of the random sampling method relies on the distribution of ``good neighbours''. Next, we show that the number of such neighbours follows a certain probability distribution during the search. Lastly, building on this distribution, we provide a theoretical insight for why random sampling is more efficient than systematic exploration, regardless of whether the best improvement or first improvement strategy is used.
CRAF: A Clinical Reasoning-Adaptive Framework via Reinforcement Learning for Similar Case Retrieval
Authors: Jie Lin; Lei Jiang; Zongyi Chen; Liansheng Wang
Venue: AAAI 2026 (main)

Abstract: With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval scenarios, implicit reasoning cues within clinical data often hinder current models from effectively capturing deep semantic associations between queries and cases. Query rewriting and expansion techniques based on reasoning offer a promising solution to this challenge by uncovering and completing the latent clinical intent behind user queries, thereby enhancing semantic coverage and reasoning sensitivity. In this paper, we propose CRAF, a clinically adaptive reasoning framework tailored for similar case retrieval. Our method generates clinical reasoning paths and incorporates a fine-grained semantic reward mechanism, enabling efficient query rewriting through reinforcement learning. Experimental results on the PMC-Patients benchmark demonstrate that CRAF consistently delivers robust improvements across multiple retrieval tasks, achieving reasoning performance comparable to that of commercial models.
EoH-S: Evolution of Heuristic Set Using LLMs for Automated Heuristic Design
Authors: Fei Liu; Yilu Liu; Qingfu Zhang; Tong Xialiang; Mingxuan Yuan
Venue: AAAI 2026 (main)

Abstract: Automated Heuristic Design (AHD) using Large Language Models (LLMs) has achieved notable success in the past two years. Despite the effectiveness of existing approaches, they only design a single heuristic to serve all problem instances, often inducing poor generalization across different distributions or sizes. To address this issue, we propose Automated Heuristic Set Design (AHSD), a new methodology for LLM-driven AHD. The aim of AHSD is to automatically design a small-sized complementary heuristic set to serve diverse problem instances, such that each problem instance could be optimized by at least one heuristic in this set. We propose Evolution of Heuristic Set (EoH-S), which realizes AHSD using an evolutionary search framework. It incorporates a complementary population management and a memetic search to design a set of heuristics. Extensive experiments on online bin packing, traveling salesman problem, and capacitated vehicle routing problem show that EoH-S consistently outperforms existing AHD methods. The resulting heuristics exhibit complementary performance across instances of varying sizes and distributions.
Theoretical and Empirical Analysis of Lehmer Codes to Search Permutation Spaces with Evolutionary Algorithms
Authors: Yuxuan Ma; Valentino Santucci; Carsten Witt
Venue: AAAI 2026 (main)

Abstract: A suitable choice of the representation of candidate solutions is crucial for the efficiency of evolutionary algorithms and related metaheuristics. We focus on problems in permutation spaces, which are at the core of numerous practical applications of such algorithms, e.g., in scheduling and transportation. Inversion vectors (also called Lehmer codes) are an alternative representation of the permutation space S(n) compared to the classical encoding as a vector of n unique entries. In particular, they do not require any constraint handling. Using rigorous mathematical runtime analyses, we compare the efficiency of inversion vector encodings to the classical representation and give theory-guided advice on their choice. Moreover, we link the effect of local changes in the inversion code space to classical measures on permutations like the number of inversions. Finally, through experimental studies on linear ordering and quadratic assignment problems, we demonstrate the practical efficiency of inversion vector encodings.
Ordinal Secretaries with Advice
Authors: Hasti Nourmohammadi; Ying Cao; Bo Sun; Xiaoqi Tan
Venue: AAAI 2026 (main)

Abstract: We study the ordinal secretary problem, where a sequence of candidates arrives in uniformly random order, and the goal is to select the best candidate using only pairwise comparisons. We consider a learning-augmented setting that incorporates potentially erroneous predictions about the best candidate’s position. Our goal is to design online algorithms that balance robustness against poor predictions while having high performance when predictions are accurate. Using an optimization-based framework, we develop deterministic and randomized algorithms that extend classical strategies and explicitly model the trade-off between consistency and robustness. Also, we show the flexibility of our approach by applying it to multiple secretary problem variants, including multiple-choice and rehiring.
Exact Combinatorial Multi-Class Graph Cuts for Semi-Supervised Learning
Authors: Mohammad Mahdi Omati; Yasin Salajeghe; Mahshad Moradi; Arash Amini
Venue: AAAI 2026 (main)

Abstract: Semi-supervised learning (SSL) on graphs is critical in applications where labeled data are scarce and costly, yet existing graph-based methods often degrade under extreme label sparsity or class imbalance, yielding trivial or unstable solutions. We introduce \textbf{CombCut}, the first exact combinatorial optimization framework for multi-class graph-based semi-supervised learning that operates directly on binary one-hot assignments, without any convex relaxation or heuristic volume constraints. By employing a minorization–maximization (MM) scheme, CombCut transforms each step into a structured linear assignment problem solved efficiently via network-flow algorithms. Total unimodularity guarantees integral iterates, and our theoretical analysis establishes both monotonic ascent of the true discrete objective and convergence of every limit point to a Karush–Kuhn–Tucker (KKT) stationary solution of the original combinatorial problem. Our approach requires no hyperparameter tuning and scales near-linearly in the number of vertices. Empirical evaluation on MNIST, Fashion-MNIST, and CIFAR-10 with as few as 1–5 labels per class shows that CombCut excels in worst-case labeling scenarios, significantly outperforming state-of-the-art graph-SSL baselines and yielding more stable and accurate label propagation under severe supervision constraints.
Towards a Rigorous Understanding of the Population Dynamics of the NSGA-III: Tight Runtime Bounds
Authors: Andre Opris
Venue: AAAI 2026 (main)

Abstract: Evolutionary algorithms are widely used for multi-objective optimization, with NSGA-III being particularly effective for problems with more than three objectives, unlike NSGA-II. Despite its empirical success, its theoretical understanding remains limited, especially regarding runtime analysis. A central open problem concerns its population dynamics, which involve controlling the maximum number of individuals sharing the same fitness value during the exploration process. In this paper, we make a significant step towards such an understanding by proving tight runtime bounds for NSGA-III on the bi-objective OneMinMax (2-OMM) problem. We show that, for population sizes n+1 ≤  µ = O(log(n)^c (n+1)) where c<1 is a constant, NSGA-III requires Ω(n^2 \log n / \mu) generations in expectation for covering the Pareto front, providing one of the first lower bounds for NSGA-III on a classical benchmark. Complementing this, we also improve the best known upper bound for NSGA-III on the m-objective OneMinMax problem (m-OMM) of O(n log(n)) generations by a factor of µ / (2n/m + 1)^(m/2) for constant m and (2n/m + 1)^(m/2) ≤ µ ∈ O((log n)^(1/2) (2n/m + 1)^(m/2)). This yields tight runtime bounds for m=2, and the surprising result that NSGA-III outperforms NSGA-II by a factor of µ/n in the expected runtime.
Multiple-Interval Coverage for Resource Management of Passive Surveillance Systems
Authors: Jan Pikman; Přemysl Šůcha; Claire Hanen; Zdeněk Hanzálek
Venue: AAAI 2026 (main)

Abstract: Passive surveillance systems (PSS) are used to detect and track various targets by processing the electromagnetic signals they release. The study and design of the resource management algorithm for these systems revealed several phenomena and combinatorial problems with crucial theoretical properties. In this article, we first prove the completeness of the algorithm used to generate receiver settings that determine which frequency bands the PSS monitors. Next, we formulate a new optimization problem called multiple-interval coverage (MIC), which is used to determine how often each of the generated settings must be used by the PSS. We show that the MIC problem is closely related to the multicover problem, which is an extension of the well-known set cover problem. The uniqueness of MIC stems from the fact that both covered elements and covers are multiple-intervals. We propose a notation to distinguish between different variants of the problem and prove that some of them can be solved in polynomial time. Finally, we prove that the MIC problem is NP-hard even when restricted to 2-interval covers.
Breadth-First Search vs. Restarting Random Walks for Escaping Uninformed Heuristic Regions
Authors: Daniel Platnick; Dawson Tomasz; Eamon Earl; Sourena Khanzadeh; Richard Valenzano
Venue: AAAI 2026 (main)

Abstract: Greedy search methods such as Greedy Best-First Search (GBFS) and Enforced Hill-Climbing (EHC) often struggle when faced with Uninformed Heuristic Regions (UHRs) like heuristic local minima or plateaus. In this work, we theoretically and empirically compare two popular methods for escaping UHRs: breadth-first search (BrFS) and restarting random walks (RRWs).  First, we derive the expected runtime of escaping a UHR using BrFS and RRWs, based on properties of the UHR and the random walk procedure, and then use these results to identify when RRWs will be faster in expectation than BrFS.  Next, we evaluate these methods for escaping UHRs by comparing standard EHC, which uses BrFS to escape UHRs, to variants of EHC called EHC-RRW, which use RRWs for that purpose. EHC-RRW is shown to have strong expected runtime guarantees in cases where EHC has previously been shown to be effective. Finally, we run experiments with these approaches on PDDL planning benchmarks to better understand their relative effectiveness for escaping UHRs.
Not Just for Archiving: Provable Benefits of Reusing the Archive in Evolutionary Multi-objective Optimization
Authors: Shengjie Ren; Zimin Liang; Miqing Li; Chao Qian
Venue: AAAI 2026 (main)

Abstract: Evolutionary Algorithms (EAs) have become the most popular tool for solving widely-existed multi-objective optimization problems. In Multi-Objective EAs (MOEAs), there is increasing interest in using an archive to store non-dominated solutions generated during the search. This approach can 1) mitigate the effects of population oscillation, a common issue in many MOEAs, and 2) allow for the use of smaller, more practical population sizes. In this paper, we analytically show that the archive can even further help MOEAs through reusing its solutions during the process of new solution generation. We first prove that using a small population size alongside an archive (without incorporating archived solutions in the generation process) may fail on certain problems, as the population may remove previously discovered but promising solutions. We then prove that reusing archive solutions can overcome this limitation, resulting in at least a polynomial speedup on the expected running time. Our analysis focuses on the well-established SMS-EMOA algorithm applied to the commonly studied OneJumpZeroJump problem as well as one of its variants. We also show that reusing archive solutions can be better than using a large population size directly. Finally, we validate our theoretical findings through experiments on well-known practical optimization problems.
Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics
Authors: Shahaf S. Shperberg; Natalie Morad; Lior Siag; Ariel Felner; Dor Atzmon
Venue: AAAI 2026 (main)

Abstract: Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specified. We build upon the state-of-the-art optimal bidirectional search algorithm, BAE*, designed for consistent heuristics, and introduce several variants of BAE* specifically tailored for the bounded-suboptimal context. Through experimental evaluation, we compare the performance of these new variants against other bounded-suboptimal bidirectional algorithms as well as the standard weighted A* algorithm. Our results demonstrate that each algorithm excels under distinct conditions, highlighting the strengths and weaknesses of each approach.
Deeper Treatment of the Bi-objective Search Framework
Authors: Shawn Skyler; Dor Atzmon; Ariel Felner; Oren Salzman; Carlos Hernández Ulloa; Sven Koenig
Venue: AAAI 2026 (main)

Abstract: In Bi-Objective Search (BOS), the task is to compute the Pareto-optimal frontier of paths in a graph with two cost values per edge. Recent work introduced a general BOS framework that classifies search nodes and studies how ordering functions affect expansion order. In this paper, we continue this line of research. We further refine the classes of nodes and show that many nodes that were added to the open list and are classified as never-expand nodes still need to be extracted and further examined. Additionally, we introduce a method that enables constant-time dominance checks for the MIN and MAX ordering functions. This allows a practical usage of these ordering functions, as we demonstrate in our experimental section.