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新论文 · v1arXiv:2611.15219

A Multi-Parent Edge Assembly Crossover Driven Hybrid Genetic Algorithm for the Hamiltonian \textit{p}-Median Problem

Junkai Huang, Pengfei He, Jin-Kao Hao, Xin Yao

原文摘要

The Hamiltonian \textit{p}-median problem seeks to partition the vertices of a graph into exactly $p$ disjoint cycles while minimizing their total cost. This paper presents a hybrid genetic algorithm incorporating a multi-parent edge assembly crossover operator. The crossover combines a base solution with multiple donor solutions in a single recombination operation, thereby expanding the space of available edge exchanges. It uses virtual copies of the base solution to establish edge balance, together with an auxiliary directed graph and a budgeted search to construct valid AB-cycles. Extensive experiments on 215 benchmark instances across four sets show that the proposed algorithm improves the best-known solution values for 34 instances and matches those for another 168. Additional experiments indicate that a moderate number of parents provides beneficial diversity, whereas excessive parent counts, particularly when $p$ is large, lead to excessive population dispersion and hinder convergence.

cs.NE

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1649ACL Anthology · ACL 2026

SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding

Pengfei He University of Manitoba, Shaowei Wang University of Manitoba, Tse-Hsun Chen, Muhammad Asaduzzaman University of Windsor

原文摘要

Static program slicing is a fundamental software engineering technique for isolating code relevant to specific variables. While recent learning-based approaches using language models (LMs) show promise in automating slice prediction, they suffer from inaccurate dependency modeling and unconstrained generation, where LMs fail to capture precise data flow relations and produce slices containing hallucinated tokens and statements. To address these challenges, we propose SliceFormer, a novel approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. introduces two key innovations that directly target the identified limitations. First, to improve dependency modeling, we design dataflow-aware pretraining objectives that leverage data flow graphs DFG to teach models data dependencies through dataflow-preserving statement permutation and dataflow-aware span corruption. Second, to eliminate hallucination, we develop a constrained decoding mechanism that enforces both lexical and syntactic constraints. We evaluate SliceFormer on Java and Python program slicing benchmarks, demonstrating consistent improvements over state-of-the-art baselines with up to 22% gain in ExactMatch.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.27ACL Anthology · ACL 2026

How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior

Zidi Xiong, Yuping Lin Michigan State University, Wenya Xie University of Minnesota - Twin Cities, Pengfei He Michigan State University, Zirui Liu University of Minnesota - Twin Cities, Jiliang Tang Michigan State University, Himabindu Lakkaraju, Zhen Xiang University of Georgia

原文摘要

Memory is a critical component in large language model (LLM)-based agents, enabling them to store and retrieve past executions to improve task performance over time. In this paper, we conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. Specifically, we focus on two fundamental memory management operations that are widely used by many agent frameworks—memory addition and deletion—to systematically study their impact on the agent behavior. Through our quantitative analysis, we find that LLM agents display an experience-following property: high similarity between a task input and the input in a retrieved memory record often results in highly similar agent outputs. Our analysis further reveals two significant challenges associated with this property: error propagation, where inaccuracies in past experiences compound and degrade future performance, and misaligned experience replay, where some seemingly correct executions can provide limited or even misleading value as experiences. Through controlled experiments, we demonstrate the importance of regulating experience quality within the memory bank and show that future task evaluations can serve as free quality labels for stored memory. Our findings offer insights into the behavioral dynamics of LLM agent memory systems and provide practical guidance for designing memory components that support robust, long-term agent performance.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.715ACL Anthology · ACL 2026

Retrieval Heads are Dynamic

Yuping Lin Michigan State University, Zitao Li Zoom, Yue Xing Michigan State University, Pengfei He Michigan State University, Yingqian Cui Michigan State University, Yaliang Li Alibaba Group, Bolin Ding Alibaba Group, Jingren Zhou Alibaba Group 等 9 位作者

原文摘要

Recent studies have identified “retrieval heads” in Large Language Models (LLMs) responsible for extracting information from input contexts. However, prior works largely rely on static statistics aggregated across datasets, identifying heads that perform retrieval on average. This perspective overlooks the fine-grained temporal dynamics of autoregressive generation. In this paper, we investigate retrieval heads from a dynamic perspective. Through extensive analysis, we establish three core claims: (1) Dynamism: Retrieval heads vary dynamically across timesteps; (2) Irreplaceability: Dynamic retrieval heads are specific at each timestep and cannot be effectively replaced by static retrieval heads; and (3) Correlation: The model’s hidden state encodes a predictive signal for future retrieval head patterns, indicating an internal planning mechanism. We validate these findings on the Needle-in-a-Haystack task and a multi-hop QA task, and quantify the differences on the utility of dynamic and static retrieval heads in a Dynamic Retrieval-Augmented Generation framework. Our study provides new insights into the internal mechanisms of LLMs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表