Long-horizon conversational agents need persistent memory beyond a fixed context window, yet memory effectiveness depends on the units stored and retrieved. Existing systems either discard fine-grained details during compression, retrieve units that are too coarse or context-poor, or fragment discourse into entity-relation triples. We propose Neo-Davidsonian Discourse Units: enriched Elementary Discourse Units (EDUs) that each express one event or fact with normalized entities, temporal grounding, and enough context to stand alone. The resulting units are non-lossy at the session level, atomic for retrieval, and naturally expose event arguments for graph-based associative recall. We instantiate this representation in EMem, which retrieves EDUs with dense representations and relevance filtering based on large language models, and EMem-G, which further organizes sessions, EDUs, and arguments in an event graph and applies Personalized PageRank. On LoCoMo and LongMemEval_S benchmarks, our methods improve over prior state-of-the-art by up to 21.3% while using up to 7× shorter effective context.

Chain-of-thought (CoT) reasoning in large language models often produces redundant steps that increase generation cost and obscure when reasoning has effectively completed. Existing stopping methods rely on output-level or token-level signals, overlooking internal representation dynamics that reflect reasoning progress. We propose STARS (STate-Aware Reasoning Stopping), a framework that leverages spectral analysis of hidden-state representations to detect transitions in reasoning phases. Our key insight is that structural changes in the representation space capture shifts in reasoning states, enabling early and reliable stopping decisions. To address representation instability, where semantically similar steps can induce different hidden states, we focus on spectral structure rather than raw activations, improving robustness to context-dependent drift. Experiments on four mathematical and logical reasoning benchmarks show that STARS reduces generation cost by over 45% under fixed token budgets while improving accuracy over standard CoT baselines.

Large language models (LLMs) tool-use policies select actions from a finite candidate set at each reasoning step. We observe that this action-selection interface is already a linear contextual scorer: under a restricted output vocabulary, the LM head computes per-action scores from the last-layer hidden state, exactly the structure assumed by linear contextual bandits. Existing online adaptation methods for LLMs operate indirectly through prompts or retrieved memories, leaving this decision layer fixed and providing no explicit mechanism for action-level credit assignment or uncertainty-aware exploration. We propose OLIVIA, a lightweight online adaptation layer that replaces the frozen action-selection head with a per-action contextual linear bandit, selects actions via LinUCB exploration on the LLM’s hidden state and updates only its per-action sufficient statistics from step-level feedback via a rank-one Sherman–Morrison rule. Without backbone fine-tuning and additional LLM calls per step, OLIVIA adds negligible overhead relative to LLM inference. Across four tool-use benchmarks ToolBench, TaskBench, TaskBench-MM, BFCL and two backbones Qwen3-4B, Mistral-7B, OLIVIA outperforms prompt-based online adaptation and contextual-bandit variants.

The task of information extraction (IE) is to extract structured knowledge from text. However, it is often not straightforward to utilize IE output due to the mismatch between the IE ontology and the downstream application needs. We propose a new formulation of IE, Text2DB, that emphasizes the integration of IE output and the target database (or knowledge base). Given a user instruction, a document set, and a database, our task requires the model to update the database with values from the document set to satisfy the user instruction. This task requires understanding user instructions for what to extract and adapting to the given DB/KB schema for how to extract on the fly. To evaluate this new task, we introduce a new benchmark featuring common demands such as data infilling, row population, and column addition. In addition, we propose an LLM agent framework OPAL (Observe-Plan-Analyze LLM) which includes an Observer component that interacts with the database, the Planner component that generates a code-based plan with calls to IE models, and the Analyzer component that provides feedback regarding code quality before execution. Experiments show that OPAL can successfully adapt to diverse database schemas by generating different code plans and calling the required IE models. We also highlight difficult cases such as dealing with large databases with complex dependencies and extraction hallucination, which we believe deserve further investigation.

The field of open relation extraction (ORE) has recently observed significant advancement thanks to the growing capability of large language models (LLMs). Nevertheless, challenges persist when ORE is performed on specific topics. Existing methods give sub-optimal results in five dimensions: factualness, topic relevance, informativeness, coverage, and uniformity. To improve topic-oriented ORE, we propose a zero-shot approach called PriORE: Open Relation Extraction with a Priori seed generation. PriORE leverages the built-in knowledge of LLMs to maintain a dynamic seed relation dictionary for the topic. The dictionary is initialized by seed relations generated from topic-relevant entity types and expanded during contextualized ORE. PriORE then reduces the randomness in generative ORE by converting it to a more robust relation classification task. Experiments show the approach empowers better topic-oriented control over the generated relations and thus improves ORE performance along the five dimensions, especially on specialized and narrow topics.

Relation extraction (RE) aims to identify semantic relationships between entities within text. Despite considerable advancements, existing models predominantly require extensive annotated training data, which is both costly and labor-intensive to collect. Moreover, these models often struggle to adapt to new or unseen relations. Few-shot learning, aiming to lessen annotation demands, typically provides incomplete and biased supervision for target relations, leading to degraded and unstable performance. To accurately and explicitly describe relation semantics while minimizing annotation demands, we explore the definition only zero-shot RE setting where only relation definitions expressed in natural language are used to train a RE model. We introduce REPaL, comprising three stages: (1) We leverage large language models (LLMs) to generate initial seed instances from relation definitions and an unlabeled corpus. (2) We fine-tune a bidirectional Small Language Model (SLM) with initial seeds to learn relations for the target domain. (3) We expand pattern coverage and mitigate bias from initial seeds by integrating feedback from the SLM’s predictions on the unlabeled corpus and the synthesis history. To accomplish this, we leverage the multi-turn conversation ability of LLMs to generate new instances in follow-up dialogues, informed by both the feedback and synthesis history. Studies reveal that definition-oriented seed synthesis enhances pattern coverage whereas indiscriminately increasing seed quantity leads to performance saturation. Experiments on two datasets show REPaL significantly improved cost-effective zero-shot performance by large margins.

In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem’s two successive sub-tasks: first with a hard-label decision making and second with a question generation aided by various entailment reasoning methods. Such usual cascaded modeling is vulnerable to error propagation and prevents the two sub-tasks from being consistently optimized. In this work, we instead model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework on both sub-tasks by a large margin, achieving new state-of-the-art results. Further ablation studies support that our framework can generalize to different backbone models.