SmolLM3: smol, multilingual, long-context reasoner

Learning to reason with LLMs

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.

Introducing HELMET: Holistically Evaluating Long-context Language Models

Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring

arXiv:2512. 14332v2 Announce Type: replace-cross Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately.

Cost of Reasoning in non-English Languages: A Case Study on Japanese

arXiv:2607. 10114v1 Announce Type: cross Abstract: Reasoning Language Models (RLMs) achieve their strongest performance when they reason in English, the language for which reasoning-oriented training data is most abundant.

Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures

arXiv:2505. 24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making.

Reason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming

arXiv:2607. 23019v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting enables large language models (LLMs) to tackle multi-step reasoning tasks, yet the generated intermediate steps are not guaranteed to be logically sound.

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

arXiv:2607. 02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications.

Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench

Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness.

Gradual Code-Switching as Inference-Time Cross-Lingual Representational Alignment for LLMs

arXiv:2510. 05678v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric latent representations.

Reasoning Shift: How Context Silently Shortens LLM Reasoning

arXiv:2604. 01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on complex, long-term reasoning tasks.

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Although recent LLMs support increasingly long context windows, they often fail to use relevant evidence that is already present in the input, revealing a gap between context access and effective context utilization.