Retrieval-augmented language models improve knowledge-intensive question answering, but they remain brittle on multi-step questions, where an early retrieval error can steer subsequent reasoning toward the wrong entity or relation. Existing methods can often detect that the current answer or evidence is unreliable, yet they rarely turn this diagnosis into an explicit retrieval correction. We propose Verification-Guided Retrieval Revision (VRR), a framework that uses verification as a control signal for multi-step retrieval. VRR couples a Generator that proposes search and answers, a Judge that verifies answer correctness and evidential support while emitting structured failure diagnoses, and a Retrieval Context Manager (RCM) that converts these diagnoses into retriever-facing updates such as clue selection, query rewriting, and state-aware evidence ranking. To make this loop learnable, we train VRR in three stages: iterative cold-start training to stabilize search, judge-shaped policy optimization to improve long-horizon reasoning, and diagnosis-conditioned RCM training for retrieval adaptation. Across seven open-domain QA benchmarks with a fixed retriever and corpus, training on HotpotQA and transferring to out-of-domain datasets, VRR surpasses the strongest prior baseline by 4.2 EM on average under budget-aligned evaluation, with larger gains on multi-hop benchmarks (+5.4).

Large language models (LLMs) possess extensive world knowledge and have demonstrated strong potential for misinformation detection. A growing line of recent work prompts LLMs to produce structured reasoning before issuing a veracity judgment. However, we contend that such structured outputs do not spontaneously support the veracity decision. The emitted scores can satisfy the output schema while contributing little to veracity discrimination. Since annotating full reasoning paths is prohibitively costly for veracity tasks, we propose a Generation-Score Consistency framework that supervises through structured dimension scores and steers the LLM toward functional reasoning. Specifically, we train a compact score-to-label verifier on a small set of expert-annotated reasoning data, freeze it, and during supervised fine-tuning use a trainable score head to align the score-token hidden states of the LLM with the human-calibrated mapping. Introducing structured reasoning on its own costs accuracy relative to label-only supervision, while the consistency term recovers that cost and keeps the emitted scores coupled to the veracity decision. Controlled experiments on multimodal misinformation benchmarks show that the consistency term strengthens both veracity prediction and the discriminability of the emitted scores. Code and data are available at https://github.com/yun-nlp/Reasoning-Scores-for-Misinformation-Detection-EMNLP2026.

Temporal knowledge graph forecasting predicts future facts from historical snapshots, and its accuracy hinges on how well a model exploits the historical evidence available to each query. Existing methods route every query through a single reasoning pipeline, yet queries with the same predicate form can rest on very different histories and call for different inductive biases. We observe that query histories fall into three regimes: sparse histories with few prior events, recurrent histories dominated by stable repetition, and ambiguous histories with many competing candidates. Building on this observation, we propose QTI-MoE, a Query-Type-aware Inductive-bias Mixture-of-Experts framework. QTI-MoE characterises each query with a soft type label derived from its historical prefix and forwards it to one of three experts whose memory pathways carry complementary biases: recency-weighted aggregation for sparse queries, count-based repetition for recurrent queries, and diversity-normalised scoring for ambiguous queries. QTI-MoE achieves state-of-the-art performance on four standard benchmarks, and a type-stratified analysis confirms that each expert peaks on its target regime.

Event forecasting requires modeling historical event data to predict future events, and achieving accurate predictions depends on effectively capturing the relevant historical information that aids forecasting. Most existing methods focus on entities and structural dependencies to capture historical clues but often overlook implicitly relevant information. This limitation arises from overlooking event semantics and deeper factual associations that are not explicitly connected in the graph structure but are nonetheless critical for accurate forecasting. To address this, we propose a dual-criteria constraint strategy that leverages event semantics for relevance modeling and incorporates a self-supervised semantic filter based on factual event associations to capture implicitly relevant historical information. Building on this strategy, our method, termed ITHI (Integrating Three types of Historical Information), combines sequential event information, periodically repeated event information, and relevant historical information to achieve context-aware event forecasting. We evaluated the proposed ITHI method on three public benchmark datasets, achieving state-of-the-art performance and significantly outperforming existing approaches. Additionally, we validated its effectiveness on two structured temporal knowledge graph forecasting dataset.