The conversational capabilities of large language models hold significant promise for enabling scalable and interactive tutoring. Prior work has primarily evaluated general instructional competence while overlooking a critical dimension: the ability to adaptively guide learners based on their evolving cognitive states. This study takes instructional guidance as a core capability and asks whether LLMs can emulate expert tutors who dynamically adjust strategies in response to learners’ needs. We introduce GuideEval, a benchmark grounded in authentic educational dialogues that evaluates pedagogical guidance through a three-phase behavioral framework: (i) Assessment, inferring learner states; (ii) Orchestration, adapting instructional strategies; and (iii) Elicitation, stimulating proper reflections. Empirical results indicate that existing LLMs often fail to provide effective adaptive scaffolding when learners experience confusion or require redirection. Complementing quantitative results, we present a detailed failure case analysis, offering intuitive insights into these limitations. We further propose a behavior-guided finetuning strategy based on behavior-prompted instructional dialogues, substantially enhancing guidance performance. By shifting the focus from isolated content correctness to learner-centered state-aware interaction, this work advocates a more dialogic paradigm for evaluating Socratic LLMs.

Despite exceptional performance on static benchmarks, Large Language Models (LLMs) exhibit significant brittleness when evaluated on perturbed variants of the original questions. Current extrinsic interventions, prompt engineering or supervised fine-tuning, overlook an important intrinsic factor underlying this degradation: the distributional shifts in layer activations between original and perturbed questions. To address this, this work formulates the reasoning robustness challenge as an Unsupervised Domain Adaptation (UDA) problem and proposes the Elastic Domain Adaptation (EDA) method. Unlike rigid strategies that enforce a uniform alignment constraint, EDA dynamically aligns layer activations based on the specific layers and perturbation types. Specifically, EDA incorporates an Adaptive Layer Selection (ALS) to identify pivotal layers and employs an Elastic Shift Module (ESM) to selectively pull or push layer representations depending on the perturbation types. Extensive experiments across six LLMs and 15 perturbation types demonstrate that EDA achieves state-of-the-art performance on most perturbed benchmarks while preserving reasoning capability on original benchmarks.

Passage reranking plays a crucial role in information retrieval by refining the ordering of candidate passages to better reflect relevance. Existing listwise LLM reranker with Chain-of-Thought (CoT) reasoning can handle complex queries effectively, but they suffer from substantial redundancy and high latency due to sliding-window strategies, which repeatedly generate highly similar CoTs. To address this, we propose QReason, a decoupled framework that separates query-focused reasoning from window-specific passage relevance assessment. Specifically, QReason introduces a dedicated rewriter generates a ranking-oriented reasoning query once, capturing the query’s core intent while avoiding redundantly, which is then reused by a non-reasoning reranker across all windows. The rewriter is trained via a two-stage process that first uses supervised fine-tuning with relevant-passage guidance through semantic evidence to produce deeply grounded, query-focused CoTs. It then applies reinforcement learning to align CoT generation with both the inference-time setting and the reranking objective, optimizing listwise metrics and passage-level discrimination to produce reusable reasoning chains for reranking. Experiments on the BRIGHT benchmark demonstrate that QReason significantly reduces redundant reasoning, achieves ranking performance comparable to or better than strong reasoning-based rerankers, and outperforms existing query rewriting models.

Equipping Large Language Models (LLMs) with pedagogical tutoring capabilities holds significant promise for education. Existing approaches simulate tutor behaviors or preferences and use them to prompt or fine-tune LLMs for dialogue tutoring. However, such methods often fail to sustain high-quality pedagogical conversations that provide explicit stepwise scaffolding and adapt to learners’ evolving cognitive states. To address this, we propose ScaffoldLM, a planning-guided tutoring framework with an assessment-driven memory for multi-turn math dialogue tutoring. ScaffoldLM first generates a stepwise pedagogical plan from solution steps, which serves as a stable backbone for explicit scaffolding. During tutoring, the tutoring memory is updated by an assessment-driven control loop that infers the learner’s cognitive state, evaluates whether the current step target is met, and adaptively selects tutoring actions. The plan, step-level progress, inferred learner states, and dialogue history are maintained in memory to support coherent multi-turn guidance. Experiments on multi-turn math tutoring benchmarks demonstrate that ScaffoldLM substantially improves pedagogical tutoring quality over strong baselines. Code is publicly available at https://github.com/BNU-ERC-ITEA/ScaffoldLM.

Recently, large language models (LLMs) have shown the potential to enhance recommendations due to their sufficient knowledge and remarkable summarization ability. However, the existing LLM-powered recommendation may create redundant output, which generates irrelevant information about the user’s preferences on candidate items from user behavior sequences. To address the issues, we propose a framework UR4Rec that enhances reranking for recommendation with large language models through user preference retrieval. Specifically, UR4Rec develops a small transformer-based user preference retriever towards candidate items to build the bridge between LLMs and recommendation, which focuses on producing the essential knowledge through LLMs from user behavior sequences to enhance reranking for recommendation. Our experimental results on three real-world public datasets demonstrate the superiority of UR4Rec over existing baseline models.

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods.

Cold-start is a significant problem in recommender systems. Recently, with the development of few-shot learning and meta-learning techniques, many researchers have devoted themselves to adopting meta-learning into recommendation as the natural scenario of few-shots. Nevertheless, we argue that recent work has a huge gap between few-shot learning and recommendations. In particular, users are locally dependent, not globally independent in recommendation. Therefore, it is necessary to formulate the local relationships between users. To accomplish this, we present a novel Few-shot learning method for Cold-Start (FCS) recommendation that consists of three hierarchical structures. More concretely, this first hierarchy is the global-meta parameters for learning the global information of all users; the second hierarchy is the local-meta parameters whose goal is to learn the adaptive cluster of local users; the third hierarchy is the specific parameters of the target user. Both the global and local information are formulated, addressing the new user’s problem in accordance with the few-shot records rapidly. Experimental results on two public real-world datasets show that the FCS method could produce stable improvements compared with the state-of-the-art.