arXiv:2601.08058v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting often improves the reasoning performance of large language models (LLMs), but the internal signal that trigg...
arXiv:2608.30462v1 Announce Type: cross Abstract: Large language models exhibit substantial performance variation across languages, even when solving semantically equivalent tasks. Existing analyses...
arXiv:2603. 03031v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning.
arXiv:2607. 25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens.
arXiv:2609.07406v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation, but explicit rational...
arXiv:2606.13061v3 Announce Type: replace Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. D...
Large Reasoning Models often overthink, producing redundant reasoning steps that consume many tokens. The authors observe that efficient reasoning steps cluster in a concentrated region of latent space, while verbose steps lie outside this region. By projecting deviating hidden states back into the region via a quadratic program, they propose a training‑free framework that cuts token generation by up to 52.8% while improving accuracy by up to 12.1% across several benchmarks.
arXiv:2601. 03595v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) exhibit human-like cognitive reasoning strategies (\eg backtracking, cross-verification) during the reasoning process, which improves their performance on complex tasks.
arXiv:2606. 15160v1 Announce Type: cross Abstract: Reasoning capabilities of multimodal large language models (MLLMs) have improved considerably in recent years.
arXiv:2608. 13570v1 Announce Type: cross Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings.
arXiv:2606. 01243v1 Announce Type: cross Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over explicit Chain-of-Thought (CoT).
INTACT introduces a small external encoder that induces latent reasoning in a frozen backbone model by mapping hidden states to compact continuous representations, called soft-token blocks. The encoder is trained via distillation to mimic written reasoning steps while preserving the backbone’s behavior, and then fine‑tuned for answer correctness. Experiments on four frozen backbones (1B–8B) show that INTACT improves accuracy on GSM8K and other out‑of‑domain benchmarks, reduces latency by up to 19%, and outperforms existing latent‑reasoning baselines at 3B and 8B scales.