| Challenge: | Using TTS, Reasoning Models (RMs) are able to perform tasks such as math and coding with limited results. |
| Approach: | They evaluate 12 Reasoning Models across a diverse suite of MT benchmarks, examining three scenarios: direct translation, forced-reasoning extrapolation, and post-editing. |
| Outcome: | The proposed approach improves translation quality on three domains, with inconsistent results for general-purpose RMs and performance plateauing. |
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| Challenge: | Recent advances have adapted this paradigm to Multimodal Foundation Models (MFMs), unlocking their potential in multimodal reasoning and generation. |
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Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models (2025.emnlp-main)
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Kaiyan Chang, Yonghao Shi, Chenglong Wang, Hang Zhou, Chi Hu, Xiaoqian Liu, Yingfeng Luo, Yuan Ge, Tong Xiao, JingBo Zhu
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Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation (2026.findings-acl)
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| Challenge: | Large language models exhibit strong reasoning when guided by chain-of-thought exemplars . collecting large, high-quality reasoning datasets remains laborious and resource-intensive . |
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| Challenge: | Existing evaluations of test-time scaling assume that a reasoning system should always give an answer to any question provided. |
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Scaling Evaluation-Time Compute with Reasoning Models as Evaluators (2026.findings-acl)
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Seungone Kim, Ian Wu, Jinu Lee, Xiang Yue, Seongyun Lee, Minkyeong Moon, Carolin Lawrence, Kiril Gashteovski, Julia Hockenmaier, Graham Neubig, Sean Welleck
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Parallel Test-Time Scaling for Latent Reasoning Models (2026.acl-long)
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| Challenge: | Parallel test-time scaling is a pivotal approach for enhancing large language models. |
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| Challenge: | Inducing models to think for longer can increase accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. |
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Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention (2026.acl-long)
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