CopT reverses CoT by eliciting a draft answer first then using continuous-embedding contrastive verification and on-policy thinking to reflect and correct, yielding up to 23% higher accuracy and 57% fewer tokens without training.
hub
SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
hub tools
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
Laser reformulates visual reasoning via Dynamic Windowed Alignment Learning to maintain latent superposition of global features, delivering 5.03% average gains over Monet and over 97% fewer inference tokens on six benchmarks.
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
SeLaR selectively applies latent soft reasoning in LLMs via entropy gating and contrastive regularization, outperforming standard CoT on five benchmarks without training.
PILOT internalizes strategic planning into compact LLMs by using a hyper-network to generate query-conditioned latent guidance vectors that stabilize reasoning trajectories and improve benchmark performance with negligible added latency.
MoLEM achieves a 10.40% average accuracy improvement in continual learning tasks across math, science, and code by using dynamic latent memory experts with a frozen base model and stage-specific autoencoders for routing.
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.
citing papers explorer
-
Dynamic Mixture of Latent Memories for Self-Evolving Agents
MoLEM achieves a 10.40% average accuracy improvement in continual learning tasks across math, science, and code by using dynamic latent memory experts with a frozen base model and stage-specific autoencoders for routing.
- Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression