Prépublication
· 2026 arXiv
EN
Scientific discovery requires developing worthwhile hypotheses into practical investigations, yet learning from this evolving research process remains challenging. We introduce an operational definition of valuable and testable research hypotheses: evidence-gr...
Jianyuan Zhong, Chenwei Wu et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
A reasoning trace can accompany a correct answer without providing effective supervision for a particular student. We study how to construct traces that improve learning beyond answer-only supervision by considering both their intermediate content and their ac...
Shaoang Li, Yanhang Shi et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Speculative decoding typically characterizes speedup by draft tokens accepted per verification round. Lossless verification preserves the target model's output distribution and thus expected completion length, making this metric a meaningful efficiency proxy....
Chiatzen Wang, Bill Xu et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
While deriving text embeddings directly from frozen LLMs has emerged as a promising direction, existing methods focus almost exclusively on English, leaving multilingual settings largely unexplored. In this work, we present the first systematic study of multil...
Ailiang Lin, Zhuoyun Li et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Speculative decoding has become a mainstream technique for accelerating large language model (LLM) inference by using a lightweight draft model to generate candidate tokens that are subsequently verified by the target model. However, agentic workloads introduc...
Jiaming Xu, Hanzhen Wang et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
The Italian legislative process is documented in detailed parliamentary records hosted in institutional websites. However, much of this information is available only as unstructured text, limiting its systematic reuse. We applied a controlled, systematic LLM-b...
Matilde Contestabile, Chiara Ferrara et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Perceived gender inference from names is important for inclusive scientometrics research, yet existing methods perform poorly on non-Western scholars. We address this gap for Chinese names, which appear in two forms, Hanzi and Pinyin, its Romanized form (e.g.,...
Lisette Espín-Noboa, Mingrong She et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimodal scenarios involving compositional requirements, we move beyond conventional binary verifiers that produce...
Bowei Liu, Xinchen Zhang et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Long contexts enlarge key--value (KV) caches, increasing memory use and attention costs during generation. Compressing these caches must preserve information for unseen questions. We introduce **Latent Compaction**, which learns a compact document-specific cac...
Yingfa Chen, Xingyu Shen et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Brazilian Portuguese remains underserved by open-source language models, and the few that are safety-aligned rarely document what did not work or validate comparisons with statistical significance. We present Manacá-1B-Instruct-2, the second-generation post-tr...
Bruno Leonardo Santos Menezes, Carlos Leonardo Souza Cardoso et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Understanding the social intelligence of large language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infe...
Ali Zamani, Soroush Esfahanian et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Multilingual dense retrievers place all languages in one shared space so that a question finds relevant documents, regardless of which language they are written in. We show that the same question, asked in different languages, retrieves substantially different...
Faeze Ghorbanpour, Shaghayegh Kolli et al.
#cs.CL