Safety-aligned large language models (LLMs) face a dilemma between helpfulness and harmlessness, where LLMs are expected to refuse harmful requests while remaining helpful by explaining their decisions. In this work, we observe a phenomenon caused by this dilemma. When asked to explain a refusal triggered by a harmful query, LLMs disclose exploitable elements in their refusal explanations. These elements allow attackers to induce the generation of harmful content. We formalize this vulnerability as Induced Refusal Explication (IRE). To identify and exploit IRE, we propose Explication of Refusal (EoR), a black-box and multi-turn jailbreak method with a dual-agent framework. EoR induces and analyzes refusal explanations from victim models and then generates follow-up queries to jailbreak them. We evaluate EoR across nine victim models. EoR achieves an average attack success rate (ASR) of **37.05** within five turns. When we apply EoR to the queries that PAIR fails to jailbreak, EoR reaches an average ASR of **66.0** across the nine victim models, which demonstrates that exploiting refusal explanations is complementary to existing automated jailbreak methods. We release our data and code at https://anonymous.4open.science/r/EoR-6234.

Open-Domain Timeline Summarization (Open-TLS) seeks to construct coherent evolutionary narratives for a topic by distilling information from large, unstructured web streams. Existing retrieve-and-generate pipelines often fail to capture logically necessary but semantically distant evidence, resulting in macro-level causal gaps (missing intermediate stages) and micro-level element gaps (underspecified milestones). We propose Active Timeline Completion (ATC), an iterative framework that converts implicit narrative gaps into targeted retrieval questions, enabling reflection-driven evidence acquisition. ATC leverages three complementary mechanisms—parametric causal gap detection, historical trajectory analogy, and dynamic ontology-guided completion—while a Logical Utility Verification module ensures only evidence directly addressing these gaps updates the timeline. Experiments on Open-TLS demonstrate that ATC consistently outperforms strong baselines in both content coverage and temporal alignment, showing that active, question-driven retrieval improves the completeness and coherence of open-domain timelines.

Concept induction, which infers appropriate abstract categories from concrete instances, is both a core ontology engineering task and a testbed for LLM semantic abstraction. Although LLMs offer broad knowledge and reasoning that may ease expert-driven ontology construction, prior work shows that knowledge deficiencies still limit them in specialized domains, while systematic evaluation of their induction failures remains lacking. To address this gap, we propose a familiarity-controlled evaluation framework that uses knowledge probing to separate item familiarity from concept induction ability. The evaluation reveals a systematic tendency to induce overly general concepts: LLMs often produce semantically correct but overly broad labels, and direct property augmentation improves performance without resolving this granularity mismatch. We further propose PPACI, an ontology-prior-guided approach that builds concept prototypes by integrating concept definitions with member item properties. For each query group, PPACI uses item properties to retrieve local item-level and global group-level candidate concepts as induction anchors. Experiments across multiple LLMs show that PPACI reduces overly general predictions by 17–28 percentage points while maintaining competitive induction quality. Our code and data are publicly available at https://github.com/Orchideous77/PPACI.

Open-domain timeline summarization (TLS) faces challenges from information overload and data sparsity when processing large-scale textual streams. Existing methods struggle to capture coherent event narratives due to fragmented descriptions and often accumulate noise through iterative retrieval strategies that lack effective relevance evaluation. This paper proposes: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Intergration, which offers a novel perspective for open-domain TLS by time point completion and event element completion. R2A-TLS establishes an initial retrieval, reflection, and deep retrieval system that reduces noise through a double filtering mechanism that iteratively generates a timeline for each text which passes the filtering. Then, the system reflects on the initial timeline with the aim of identifying information gaps through causal chain analysis and FrameNet based element validation. These gaps are reformulated into targeted queries to trigger deep retrieval for refining timeline coherence and density. Empirical evaluation on Open-TLS dataset reveals that our approach outperforms the best prior published approaches.

Fine-grained Entity Typing (FET) has made great progress based on distant supervision but still suffers from label noise. Existing FET noise learning methods rely on prediction distributions in an instance-independent manner, which causes the problem of confirmation bias. In this work, we propose a clustering-based loss correction framework named Feature Cluster Loss Correction (FCLC), to address these two problems. FCLC first train a coarse backbone model as a feature extractor and noise estimator. Loss correction is then applied to each feature cluster, learning directly from the noisy labels. Experimental results on three public datasets show that FCLC achieves the best performance over existing competitive systems. Auxiliary experiments further demonstrate that FCLC is stable to hyperparameters and it does help mitigate confirmation bias. We also find that in the extreme case of no clean data, the FCLC framework still achieves competitive performance.