Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources.However, creating extensive and high-quality MHQA datasets is challenging: (i) manual annotation is expensive, and (ii) current synthesis methods often produce simplistic questions or require extensive manual guidance.This paper introduces HopWeaver, the first cross-document framework synthesizing authentic multi-hop questions without human intervention.HopWeaver synthesizes bridge and comparison questions through an innovative pipeline that identifies complementary documents and constructs authentic reasoning paths to ensure true multi-hop reasoning.We further present a comprehensive system for evaluating the synthesized multi-hop questions.Empirical evaluations demonstrate that the synthesized questions achieve comparable or superior quality to human-annotated datasets at a lower cost.Our framework provides a valuable tool for the research community: it can automatically generate challenging benchmarks from any raw corpus, which opens new avenues for both evaluation and targeted training to improve the reasoning capabilities of advanced question answering models, especially in domains with scarce resources.The code for HopWeaver is publicly available 1 .

Maintaining consistency in long-term dialogues remains a fundamental challenge for LLMs, as standard retrieval mechanisms often fail to capture the temporal evolution of historical states.While memory-augmented frameworks offer a structured alternative, current systems rely on static prompting of closed-source models or suffer from ineffective training paradigms with sparse rewards.We introduce MemBuilder, a reinforcement learning framework that trains models to orchestrate multi-dimensional memory construction with attributed dense rewards.MemBuilder addresses two key challenges: (1) Sparse Trajectory-Level Rewards: we employ synthetic session-level question generation to provide dense intermediate rewards across extended trajectories; and (2) Multi-Dimensional Memory Attribution: we introduce contribution-aware gradient weighting that scales policy updates based on each component's downstream impact.Experimental results show that MemBuilder enables a 4Bparameter model to outperform state-of-the-art closed-source baselines, exhibiting strong generalization across long-term dialogue benchmarks. 1

Emotion Recognition in Conversations (ERC) is crucial for machines to understand dynamic human emotions. While Large Language Models (LLMs) show promise, their performance is often limited by challenges in interpreting complex conversational streams. We introduce a Clue of Emotion (CoE) framework, which progressively integrates key conversational clues to enhance the ERC task. Building on CoE, we implement a multi-stage auxiliary learning strategy that incorporates role-playing, speaker identification, and emotion reasoning tasks, each targeting different aspects of conversational emotion understanding and enhancing the model’s ability to interpret emotional contexts. Our experiments on EmoryNLP, MELD, and IEMOCAP demonstrate that CoE consistently outperforms state-of-the-art methods, achieving a 2.92% improvement on EmoryNLP. These results underscore the effectiveness of clues and multi-stage auxiliary learning for ERC, offering valuable insights for future research.

Disagreement detection is a crucial task in natural language processing (NLP), particularly in analyzing online discussions and social media content.Large language models (LLMs) have demonstrated significant advancements across various NLP tasks.However, the performance of LLMs in disagreement detection is limited by two issues: conceptual gap and reasoning gap.In this paper, we propose a novel two-stage framework, Concept Alignment and Reasoning Enhancement (CARE), to tackle the issues.The first stage, Concept Alignment, addresses the gap between expert and model by performing sub-concept taxonomy extraction, aligning the model's comprehension with human experts.The second stage, Reasoning Enhancement, improves the model's reasoning capabilities by introducing curriculum learning workflow, which includes rationale to critique and counterfactual to detection for reducing spurious association.Extensive experiments on disagreement detection task demonstrate the effectiveness of our framework, showing superior performance in zero-shot and supervised learning settings, both within and across domains.

Recent studies have demonstrated the potential of large language models (LLMs) in test case generation. However, LLMs often struggle to achieve high levels of test coverage when generating test cases for long methods. Long methods are one of the typical manifestations of code smells, characterized by excessive lines of code, complex control flows, deep nesting levels, and numerous variables. These characteristics make it difficult for traditional testing tools to cover most of the lines and branches of focal methods. To address this issue, this paper proposes a novel approach, DecoTest, to generate unit test cases for long methods based on LLMs. The proposed method leverages LLM and automated validation based on static analysis to derive high-quality decomposition and refactoring plans. After refactoring the long methods, test cases are generated, iteratively verified and repaired to produce the final test case suite. This paper also presents an experimental analysis of DecoTest. The results indicate that the proposed method outperforms existing LLM-based test case generation methods in terms of line coverage, branch coverage, and test execution pass rate.

Online design communities, where members exchange free-form views on others' designs, offer a space for beginners to learn visual design. However, the content of these communities is often unorganized for learners, containing many redundancies and irrelevant comments. In this paper, we propose a computational approach for leveraging online design communities to run a conversational agent that assists informal learning of visual elements (e.g., color and space). Our method extracts critiques, suggestions, and rationales on visual elements from comments. We present DesignQuizzer, which asks questions about visual design in UI examples and provides structured comment summaries. Two user studies demonstrate the engagement and usefulness of DesignQuizzer compared with the baseline (reading reddit.com/r/UI_design). We also showcase how effectively novices can apply what they learn with DesignQuizzer in a design critique task and a visual design task. We discuss how to use our approach with other communities and offer design considerations for community-powered learning support tools.

Modular power converters in combination with distributed control architecture has been promising in medium-voltage applications due to its modularity and scalability. The requirement of the synchronization accuracy is elevated dramatically for the communication network with advanced distributed control schemes. This paper presents a phase-locked loop (PLL)-based synchronization method with sub-ns synchronization accuracy based on the White Rabbit protocol. Digital perturbation injection method is proposed to estimate the PLL closed-loop characteristics, and the PLL stability is analyzed. Good agreements of the closed-loop transfer function and stability analysis have been achieved between the theoretical calculation and experimental results. A four-PEBB 6kV SiC-based converter is established to verify the distributed communication and control system.

Some results are reported from an empirical study of program characteristics, that are important in parallelizing compiler writers, especially in the area of data dependence analysis and program transformations. The state of the art in data dependence analysis and some parallel execution techniques are examined. The major findings are included. Many subscripts contain symbolic terms with unknown values. A few methods of determining their values at compile time are evaluated. Array references with coupled subscripts appear quite frequently; these subscripts must be handled simultaneously in a dependence test, rather than being handled separately as in current test algorithms. Nonzero coefficients of loop indexes in most subscripts are found to be simple: they are either 1 or -1. This allows an exact real-valued test to be as accurate as an exact integer-valued test for one-dimensional or two-dimensional arrays. Dependencies with uncertain distance are found to be rather common, and one of the main reasons is the frequent appearance of symbolic terms with unknown values.>