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20242026
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cs.CL2026

Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages

Haolin Li, Haipeng Zhang, Mang Li +4

Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and unstable cross-lingual alignment. To a…

cs.CL20262 cited

Kimi K2.5: Visual Agentic Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +339

We introduce Kimi K2.5, an open-source multimodal agentic model designed to advance general agentic intelligence. K2.5 emphasizes the joint optimization of text and vision so that…

cs.CL2026

DiffuSpeech: Silent Thought, Spoken Answer via Unified Speech-Text Diffusion

Yuxuan Lou, Ziming Wu, Yaochen Wang +6

Current speech language models generate responses directly without explicit reasoning, leading to errors that cannot be corrected once audio is produced. We introduce \textbf{``Sil…

cs.CL2026

Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth

Michelle Yuan, Weiyi Sun, Amir H. Rezaeian +5

Transformers have become the foundational architecture for a broad spectrum of sequence modeling applications, underpinning state-of-the-art systems in natural language processing,…

cs.CL2025

Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance

Yao Wang, Di Liang, Minlong Peng

Supervised fine-tuning (SFT) is a pivotal approach to adapting large language models (LLMs) for downstream tasks; however, performance often suffers from the ``seesaw phenomenon'',…