Online scams often unfold gradually through interaction, yet existing detection systems predominantly rely on snapshot-based signals and interruptive warnings, revealing two research gaps in the lack of signals that represent scam risk within conversational dynamics and the underexplored design of non-interruptive interaction. To address these gaps, we introduce multi-level alignment-based hints, informed by the Interactive Alignment Model, as a new detection signal for supporting sensemaking in scam-related conversations. These hints operationalize low-level lexical and syntactic alignments and high-level semantic and situation-model alignments between conversational participants, making conversational dynamics visible to users. We first conduct a preliminary evaluation on real-life scam dialogues, showing that as conversations approach scam attempts, low-level alignment scores remain stable while high-level alignment scores systematically decline, revealing a consistent cross-level pattern indicative of scam progression. Building on this insight, we conduct a user study with thirty participants, indicating that relative to the no-hint baseline, multi-level alignment-based hints increase precision by 0.25, recall by 0.16, and F1 score by 0.21, yielding substantially larger gains than the marginal improvements achieved by keyword-triggered alerts. Statistical analyses reveal that the proposed hints support earlier and more stable confidence formation over time, with ablation results further highlighting the effectiveness of combining alignment hints across levels in achieving these advantages.

Accurate requirement-to-code traceability is crucial for software maintenance. However, existing IR- and embedding-based methods are heavily dependent on lexical similarity, often yielding incomplete or inconsistent links across projects and languages and incurring high cost from long-context retrieval and prompting. This paper presents R2Code, an LLM-based semantic traceability framework designed to improve trace link accuracy while reducing inference cost. R2Code integrates three components: 1) a decomposition-enhanced Bidirectional Alignment Network (BAN) that aligns four-layer requirement semantics with corresponding code structures to support cross-level semantic matching; 2) a Self-Reflective Consistency Verification (SRCV) module that conducts explanation-guided consistency checking to calibrate link reliability; and 3) a Dynamic Context-Adaptive Retrieval (DCAR) mechanism that adjusts retrieval granularity and filters contexts using semantic-overlap weighting for efficient context utilization. Experiments on five public datasets spanning multiple domains and two programming languages demonstrate that R2Code consistently outperforms the strongest baselines, achieving an average F1 gain of 7.4%, while reducing token consumption by up to 41.7% through adaptive context control.

The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs). Recent efforts extend LLMs into multi-agent systems (MAS) that emulate collaborative development workflows, but these systems often fail due to three core deficiencies: under-specification, coordination misalignment, and inappropriate verification, arising from the absence of foundational SE structuring principles. This paper introduces Software Engineering Multi-Agent Protocol (SEMAP), a protocol-layer methodology that instantiates three core SE design principles for multi-agent LLMs: (1) explicit behavioral contract modeling, (2) structured messaging, and (3) lifecycleguided execution with verification, and is implemented atop Google’s Agent-to-Agent (A2A) infrastructure. Empirical evaluation using the Multi-Agent System Failure Taxonomy (MAST) framework demonstrates that SEMAP effectively reduces failures across different SE tasks. In code development, it achieves up to a $69.6 \%$ reduction in total failures for function-level development and $\mathbf{5 6 . 7 \%}$ for deployment-level development. For vulnerability detection, SEMAP reduces failure counts by up to $47.4 \%$ on Python tasks and $28.2 \%$ on $\mathrm{C} / \mathrm{C}++$ tasks.

Chart-to-code generation is a critical task in automated data visualization, translating complex chart structures into executable programs. While recent Multi-modal Large Language Models (MLLMs) improve chart representation, existing approaches still struggle to achieve cross-type generalization, memory efficiency, and modular design. To address these challenges, this paper proposes C2C-MolA, a multimodal framework that synergizes Mixture of Experts (MoE) with Low-Rank Adaptation (LoRA). The MoE component uses a complexity-aware routing mechanism with domain-specialized experts and load-balanced sparse gating, dynamically allocating inputs based on learnable structural metrics like element count and chart complexity. LoRA enables parameter-efficient updates for resource-conscious tuning, further supported by a tailored training strategy that aligns routing stability with semantic accuracy. Experiments on Chart2Code-160k show that the proposed model improves generation accuracy by up to 17%, reduces peak GPU memory by 18%, and accelerates convergence by 20%, when compared to standard fine-tuning and LoRAonly baselines, particularly on complex charts. Ablation studies validate optimal designs, such as 8 experts and rank-8 LoRA, and confirm scalability for real-world multimodal code generation.

Autonomous navigation is reshaping various domains in people’s life by enabling safe and efficient movement in complex environments. Reliable navigation requires path planning algorithms that compute optimal or near-optimal trajectories while satisfying task-specific constraints and ensuring obstacle avoidance. However, existing algorithms struggle with slow convergence and suboptimal solutions, particularly in complex environments, limiting their real-world applicability. To address these limitations, this paper presents the Multi-Strategy Enhanced Crayfish Optimization Algorithm (MCOA), a novel approach integrating three strategies: 1) Refractive Learning to enhance diversity and global exploration, 2) Stochastic Centroid-Guided Exploration to balance global and local search, and 3) Adaptive Competition-Based Selection to accelerate convergence and improve solution quality. Experimental results show that MCOA significantly improves the performance of 3D UAV path planning, reducing computation time by 69.2% and trajectory cost by 67.0% compared to 11 baseline algorithms, which demonstrates its effectiveness in autonomous navigation within complex environments.

The Transformer architecture and its core attention mechanism form the foundation of Pre-trained Language Models (PLMs) and have driven their remarkable progress across a wide range of code intelligence tasks. However, the quadratic complexity inherent in the attention mechanism poses scalability challenges. Recently, sub-quadratic architectures such as Mamba and Mamba-2 have emerged as compelling alternatives to the Transformer. While they have shown promising results and attracted increasing academic interest, their effectiveness in code intelligence tasks has not yet been fully explored.To fill this gap, we present the first systematic empirical study of Mamba-based PLMs on three typical code tasks (i.e., code completion, code generation, and code clone detection), covering both the code comprehension and generation categories to delve into their effectiveness and efficiency. We first pre-train two Mamba-based PLMs on code based on Mamba and Mamba-2, respectively. Subsequently, we evaluate these four PLMs against typical Transformer-based PLMs (e.g., CodeGPT) with Full fine-Tuning (FT) and Parameter-Efficient Fine-Tuning (PEFT) settings, demonstrating the overall superiority of Mamba-based PLMs across all code tasks. Subsequent experiments involve the architecture analysis via pre-training from scratch to isolate the influence of the training corpora and low-resource analysis via deliberately limiting the fine-tuning data volume. All demonstrate the superiority of Mamba-based PLMs in both efficacy and efficiency. Finally, we also extend the sizes of PLMs to larger scales (7B at most) and make comparisons with more diverse PLMs/LLMs. Experimental results demonstrate that pre-training corpora and tasks also heavily affect the code modeling performance, apart from architectures. This work provides a comprehensive investigation into Mamba-based PLMs in the context of code intelligence, uncovering their strengths, limitations, and potential for future applications.

Contrastive learning has recently achieved compelling performance in unsupervised sentence representation. As an essential element, data augmentation protocols, however, have not been well explored. The pioneering work SimCSE resorting to a simple dropout mechanism (viewed as continuous augmentation) surprisingly dominates discrete augmentations such as cropping, word deletion, and synonym replacement as reported. To understand the underlying rationales, we revisit existing approaches and attempt to hypothesize the desiderata of reasonable data augmentation methods: balance of semantic consistency and expression diversity. We then develop three simple yet effective discrete sentence augmentation schemes: punctuation insertion, modal verbs, and double negation. They act as minimal noises at lexical level to produce diverse forms of sentences. Furthermore, standard negation is capitalized on to generate negative samples for alleviating feature suppression involved in contrastive learning. We experimented extensively with semantic textual similarity on diverse datasets. The results support the superiority of the proposed methods consistently. Our key code is available at https://github.com/Zhudongsheng75/SDA

Reinforcement learning is a powerful methodology that enables self-adaptive systems to relearn and update their adaptation policy when dealing with unforeseen changes. To update the policy more efficiently, several knowledge reuse approaches have been proposed to speed up relearning. However, the current studies treat and reuse the knowledge integrally, which may result in increased relearning costs if the reused knowledge is inappropriate in the changed situation. Generally, some localized pieces of the knowledge are still appropriate for reuse if they are not related to the changes, while some pieces may become inappropriate for reuse if they are affected by the changes. This paper proposes a goal-oriented curriculum evolution method to realize finer-grained knowledge reuse, combining goal-oriented modeling and curriculum learning. The method is twofold: (1) at design time, we apply goal-oriented modeling to design a curriculum in which an RL problem is decomposed into sub-problems, so that knowledge can be decomposed into several pieces of localized knowledge for sub-problems, and (2) at runtime, we evolve the curriculum to reflect changes (i.e., update the sub-problems related to the changes), so that the affected pieces of knowledge can be locally updated to make them appropriate for reuse in the changed situation. The evaluation based on a cleaning robot shows that the relearning time was shortened, demonstrating the effectiveness of our method.

Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, travel time estimation). However, most existing methods only contrast within the same scale, i.e., treating road network and trajectory separately, which ignores valuable inter-relations. In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. We design domain-specific augmentations for road-road contrast and trajectory-trajectory contrast separately, i.e., road segment with its contextual neighbors and trajectory with its detour replaced and dropped alternatives, respectively. On top of that, we further introduce the road-trajectory cross-scale contrast to bridge the two scales by maximizing the total mutual information. Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies. We conduct prudent experiments based on two real-world datasets with four downstream tasks, demonstrating improved performance and effectiveness.

We give a fully polynomial-time approximation scheme (FPTAS) to count the number of independent sets on almost every $Δ$-regular bipartite graph if $Δ\ge 53$. In the weighted case, for all sufficiently large integers $Δ$ and weight parameters $λ=\tildeΩ\left(\frac{1}Δ\right)$, we also obtain an FPTAS on almost every $Δ$-regular bipartite graph. Our technique is based on the recent work of Jenssen, Keevash and Perkins (SODA, 2019) and we also apply it to confirm an open question raised there: For all $q\ge 3$ and sufficiently large integers $Δ=Δ(q)$, there is an FPTAS to count the number of $q$-colorings on almost every $Δ$-regular bipartite graph.

The service specification ss(P) of a protocol P defines the services provided by the protocol and its protocol specification ps(P) specifies the rules of message exchange to ensure the service. Protocol composition has been advocated as an attractive way to design complex protocols. Several techniques have been studied for composition of protocol specifications. In these techniques, to combine component protocols P and and to design R, ps(P) and ps(and) are first combined to obtain ps(R) and then inference rules are used to derive ss(R). In this paper, we explore an alternative strategy in which we allow composition to be specified at the service specification level (that is, ss(P) and ss(Q) are first combined to obtain ss(R)). Given ss(R), we provide an algorithm to mechanically combine ps(P) and ps(Q) to generate ps(R) such that ps(R) satisfies ss(R). We show that analysis of ss(R) is sufficient to ensure that ps(R) satisfies certain safety and liveness properties. This results in efficient validation as state space of ss(R) is typically significantly smaller than that of ps(R).

We propose a compositional technique to design multifunction protocols. The technique involves first designing the protocols performing the various functions separately and then combining them using a set of constraints. The constraints are used to specify the interactions between the component protocols. The interactions, for example, specify when a function has to be performed and whether two functions can be performed concurrently or not. We illustrate the use of our technique by designing several protocols. We give sufficient conditions to infer properties of the composite protocol from those of the component protocols.