Efficient reasoning is critical for large reasoning models (LRMs) deployed in agent systems with strict context constraints. Existing methods predominantly rely on global length penalties that overlook step-level structure, inadvertently compressing critical derivations while retaining low-value detours. Hence, we argue for a principled, step-aware alternative that preserves critical steps and compresses low-value detours. However, it raises three fundamental challenges: the difficulty of obtaining step-level supervision, the need for reward signals that induce differentiated length pressure, and the requirement to balance training stability with exploration on complex problems. We propose SmartThinker, a step-level token allocation framework that learns to selectively preserve or compress individual reasoning steps. At its core is Step-Level Length Control Policy Optimization (SCPO), which provides label-free feedback by online estimation of each step’s contribution from the change in correct-answer likelihood after removing it. We further introduce reasoning density, defined as contribution per token, to relax length pressure on high-value steps while intensifying it on low-density ones. Finally, SCPO stabilizes training through length-bias-corrected advantages and difficulty-adaptive clipping, achieving superior accuracy-efficiency trade-offs across nine in-domain and out-of-domain benchmarks.

Reasoning efficiency in Text-to-SQL is an important yet underexplored problem. Chain-of-Thought (CoT) reasoning models simplify traditional Text-to-SQL workflows and significantly improve the ability to handle complex queries. However, existing methods typically generate long reasoning processes for all queries. This leads to high token consumption and inference latency, increasing system cost and limiting practical deployment. To address this issue, we propose Ada-SQL, an adaptive reasoning model for Text-to-SQL that jointly optimizes reasoning mode selection and token budget control via a two-stage reinforcement learning framework coupled with a token budget model. This enables the model to adaptively decide whether to reason and how much reasoning budget to allocate based on query difficulty. Experimental results show that Ada-SQL reduces output token consumption by more than 65% while maintaining competitive execution accuracy, achieving a favorable performance-efficiency trade-off. Code and models will be released at https://github.com/sxhText2SQL/Ada-SQL.

Long-horizon LLM agents often fail from procedural errors such as forgetting constraints, misordering tool calls, or stopping prematurely. Because these slips are unpredictable, the same agent can succeed on one run and fail on an identical task later. The resulting execution trajectories contain rich signals about which procedural choices lead to success or failure, yet existing approaches that extract reusable guidance from agent trajectories either introduce noise through imprecise distillation or depend on external retrievers for deployment. We propose SkillForge, a training-free framework that rethinks both the construction and deployment of procedural guidance: it pairs successful and failed attempts on the same task to identify critical procedural choices, consolidates and filters candidate guidance to remove low-quality entries, and loads relevant guidance on demand at inference time only when the agent’s current context signals its relevance. Across agent benchmarks including 𝜏²-Bench and Terminal-Bench, SkillForge improves repeated-run consistency, reducing the consistency gap by up to 37% and lowering total token usage by up to 23.6% compared to baselines, while moderately improving single-trial performance. These results suggest a practical path toward distilling high-quality procedural guidance from past trajectories for more consistent and efficient long-horizon agents.

Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.

Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three popular open-domain tasks: fact checking, slot filling, and question answering, and evaluate a diverse set of retrievers. We find that the retrievers exhibit popularity bias, significantly under-performing on rarer entities that share a name, e.g., they are twice as likely to retrieve erroneous documents on queries for the less popular entity under the same name. These experiments on AmbER sets show their utility as an evaluation tool and highlight the weaknesses of popular retrieval systems.

A capable, automatic Question Answering (QA) system can provide more complete and accurate answers using a comprehensive knowledge base (KB). One important approach to constructing a comprehensive knowledge base is to extract information from Wikipedia infobox tables to populate an existing KB. Despite previous successes in the Infobox Extraction (IBE) problem (e.g., DBpedia), three major challenges remain: 1) Deterministic extraction patterns used in DBpedia are vulnerable to template changes; 2) Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic-based extraction of unlinkable entities yields low precision, hurting both accuracy and completeness of the final KB. This paper presents a robust approach that tackles all three challenges. We build probabilistic models to predict relations between entity mentions directly from the infobox tables in HTML. The entity mentions are linked to identifiers in an existing KB if possible. The unlinkable ones are also parsed and preserved in the final output. Training data for both the relation extraction and the entity linking models are automatically generated using distant supervision. We demonstrate the empirical effectiveness of the proposed method in both precision and recall compared to a strong IBE baseline, DBpedia, with an absolute improvement of 41.3% in average F1. We also show that our extraction makes the final KB significantly more complete, improving the completeness score of list-value relation types by 61.4%.

Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.