SmolLM3: smol, multilingual, long-context reasoner

Learning to reason with LLMs

Reasoning about In-Context Samples for Machine-Translation

The paper proposes a fragment‑based reasoning framework for large language model–based machine translation. It extracts parallel source‑target fragments from retrieved similar examples and uses these fragments as intermediate reasoning traces to generate the final translation. Experiments with the Qwen3 model across six languages and multiple domains show that this approach outperforms standard k‑shot or basic drafting methods.

Multilingual Reasoning Cascades Need More Context

Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation.

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

arXiv:2609.10445v1 Announce Type: new Abstract: Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: m...

Contextual Causality with Large Language Models: A Survey

arXiv:2609.22409v1 Announce Type: new Abstract: Understanding contextual causality is critical for large language models (LLMs), as it enables them to accurately identify causal relations in specific...

Introducing HELMET: Holistically Evaluating Long-context Language Models