The representation learning of knowledge graphs (KGs) is a longstanding research problem. While graph neural networks (GNNs) have driven recent progress, they still struggle with encoding textual features and subtle relationships of KGs, particularly in conveying key information to large language models (LLMs). The emergence of multi-modal LLMs (MLLMs), which combine linguistic and visual understanding, presents an intriguing opportunity: Could their vision capabilities inspire mental visualization, facilitating conceptual thinking and semantic reasoning akin to human cognition? To investigate this premise, we propose SeeKG, an innovative framework that transforms KGs into visually rendered representations as image inputs for MLLMs. We evaluate SeeKG under both training-free and supervised fine-tuning settings, where the experimental results show that SeeKG excels in understanding KG sub-graphs and achieves competitive performance even without training or demonstrations. Further fine-tuning on small-batch data reveals that it outperforms state-of-the-art LLM-based methods by substantial margins across multiple KG completion datasets.

Emotional intelligence (EI) in large language models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive. A good response should improve the user’s emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2×2 taxonomy covering Support, Defense, Repair, and Charm. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator’s per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9%). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management.

Large reasoning models (LRMs) have achieved remarkable success on complex tasks, yet their tendency to "overthink” leads to inefficiencies. Although "save-thinking” prompts are intended to mitigate this issue, we find that LRMs still frequently enter the "Still-thinking” mode instead of the expected "No-thinking” mode, especially when facing difficult queries. First, we examine the LRM’s confidence when terminating thinking process, finding that high perplexity signals internal uncertainty and precedes the thinking process. Second, we analyze the divergence in internal attention distributions across different LRMs. Third, we investigate where the LRM allocates its attention within the prompt, identifying a critical "re-reading” pattern. Based on these analyses, we propose an attention intervention method to regulate this behavior. While this intervention successfully suppresses thinking, we find it causes a drop in accuracy, confirming that the thinking process is functional. Our work provides a mechanistic explanation for this behavior, highlighting the trade-off between instruction following and reasoning correctness, and offers insights for developing more controllable LRMs.

On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy distillation and reinforcement learning with verifiable rewards. However, we find that its standard advantage-weighted policy gradient suffers from three structural weaknesses, including high-variance updates, vanishing gradients in zero-advantage regions, and exploration bottlenecks for models with limited capability. We therefore propose asymmetric on-policy distillation (AOPD), which replaces ineffective policy gradient with localized divergence minimization in non-positive advantage regions while preserving positive reinforcement learning. Experiments on mathematical reasoning benchmarks show that AOPD consistently outperforms standard OPD, with average gains of 4.09 / 8.34 under strong / weak initialization, respectively. AOPD also maintains higher policy entropy during training and better capability retention during sequential tool-use adaptation. Our source code is available at [repository](https://github.com/stargazer319/AOPD).

Memory management is vital for LLM agents in long-term and personalized interactions.Most previous work studies how to retrieve and use memory, but pays less attention to how memory is extracted.We find two main limitations in existing methods.First, extraction is “ahead-of-time”: the agent saves information before it knows future tasks.A single summary prompt often mixes details, events, and relations, so useful information is lost.Second, extraction is usually one-off. Without verification, errors and hallucinations may stay in memory for a long time. To address these limitations, we propose ProMem, a proactive memory extraction framework.It separates details, events, and relations, and uses different extraction strategies for each type.It also checks completeness to recover missed events and verifies facts at the atomic level to reduce hallucinations.Experiments show that ProMem improves memory completeness and QA accuracy, while keeping a good balance between quality and token cost.

Large language model (LLM) agents increasingly depend on external memory to support long-horizon interaction. Yet many current memory systems still rely on largely fixed default rules once their pipelines are configured. They often apply the same extraction, retrieval, and evidence-organization choices across inputs. This is restrictive. Different memory needs require different choices. We propose AdaMemPIP, an adaptive memory orchestration framework for LLM agents. AdaMemPIP uses an LLM to route both memory storage and memory usage. For storage, it selects extractors for each new batch of dialogue records and writes the extracted items to matched memory libraries. For usage, it selects retrieval methods, target libraries, and organization strategies for each query, then executes the resulting pipeline. The framework is also extensible: new extractors, retrievers, stores, and organizers can be added without changing the overall control logic. This makes AdaMemPIP a flexible framework to long-term agent memory.

Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due to the unclear value of intermediate steps. It is hard to separate their contributions from the final result. In this paper, we propose a dense process supervision method based on fact utility estimation, which models the reasoning process as the accumulation of discrete evidence facts. We first extract structured facts from raw observations and organize them into an explicit fact store. To support credit assignment, we then cluster semantically equivalent facts and infer the posterior utility of each fact cluster using Bayesian estimation over group rollouts. Finally, we convert the estimated fact utilities into dense step-level rewards to guide RL training. Experiments on seven single-hop and multi-hop QA benchmarks show that our method consistently outperforms existing baselines. Ablation studies validate clear relative improvements on multi-hop QA compared to outcome reward-only training.

In this paper, we identify a critical problem, “lost-in-retrieval”, in retrieval-augmented multi-hop question answering (QA): the key entities are missed in LLMs’ sub-question decomposition. “Lost-in-retrieval” significantly degrades the retrieval performance, which disrupts the reasoning chain and leads to the incorrect answers. To resolve this problem, we propose a progressive retrieval and rewriting method, namely ChainRAG, which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation. Each step in our retrieval and rewriting process builds upon the previous one, creating a seamless chain that leads to accurate retrieval and answers. Finally, all retrieved sentences and sub-question answers are integrated to generate a comprehensive answer to the original question. We evaluate ChainRAG on three multi-hop QA datasets—MuSiQue, 2Wiki, and HotpotQA—using three large language models: GPT4o-mini, Qwen2.5-72B, and GLM-4-Plus. Empirical results demonstrate that ChainRAG consistently outperforms baselines in both effectiveness and efficiency.

Parameter-efficient finetuning (PEFT) is a key technique for adapting large language models (LLMs) to downstream tasks. In this paper, we study leveraging knowledge graph embeddings to improve the effectiveness of PEFT. We propose a knowledgeable adaptation method called KnowLA. It inserts an adaptation layer into an LLM to integrate the embeddings of entities appearing in the input text. The adaptation layer is trained in combination with LoRA on instruction data. Experiments on six benchmarks with two popular LLMs and three knowledge graphs demonstrate the effectiveness and robustness of KnowLA. We show that KnowLA can help activate the relevant parameterized knowledge in an LLM to answer a question without changing its parameters or input prompts.

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.

This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.

Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.