REVIEW 13 cited by
Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Human cognition typically involves thinking through abstract, fluid concepts rather than strictly using discrete linguistic tokens. Current reasoning models, however, are constrained to reasoning within the boundaries of human language, processing discrete token embeddings that represent fixed points in the semantic space. This discrete constraint restricts the expressive power and upper potential of such reasoning models, often causing incomplete exploration of reasoning paths, as standard Chain-of-Thought (CoT) methods rely on sampling one token per step. In this work, we introduce Soft Thinking, a training-free method that emulates human-like "soft" reasoning by generating soft, abstract concept tokens in a continuous concept space. These concept tokens are created by the probability-weighted mixture of token embeddings, which form the continuous concept space, enabling smooth transitions and richer representations that transcend traditional discrete boundaries. In essence, each generated concept token encapsulates multiple meanings from related discrete tokens, implicitly exploring various reasoning paths to converge effectively toward the correct answer. Empirical evaluations on diverse mathematical and coding benchmarks consistently demonstrate the effectiveness and efficiency of Soft Thinking, improving pass@1 accuracy by up to 2.48 points while simultaneously reducing token usage by up to 22.4% compared to standard CoT. Qualitative analysis further reveals that Soft Thinking outputs remain highly interpretable and readable, highlighting the potential of Soft Thinking to break the inherent bottleneck of discrete language-based reasoning. Code is available at https://github.com/eric-ai-lab/Soft-Thinking.
Forward citations
Cited by 13 Pith papers
-
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
DiscoLoop adds a discrete embedding channel to looped transformers to fix representational misalignment in two-hop reasoning, yielding near-perfect accuracy on synthetic tasks and better pretraining loss on real data.
-
Balancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
AGS uses the ratio of attention to image tokens vs text tokens to switch multimodal LLMs between latent and explicit reasoning, improving accuracy and cutting decoding steps.
-
Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models
Spoken math models that emit a 40%-compressed reasoning trace between question and answer beat full-reasoning baselines by ~3 accuracy points while using roughly one third of the text tokens.
-
SLPO: Scaling Latent Reasoning via a Surrogate Policy
SLPO adds a surrogate Gaussian policy and a learnable stopping gate so that outcome-reward RL can improve latent (continuous-vector) reasoning, raising Pass@8/16 in all 12 tested settings.
-
EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
Iterative latent thought refinement plus terminal text grounding lets diffusion models solve multi-step visual reasoning tasks at 92.1% average accuracy, beating DiffThinker by 8.3 points.
-
MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models
Adding a 0.6%-parameter recurrent working memory to frozen discrete diffusion LLMs yields an average 4.5-percentage-point gain on GSM8K, MATH-500, HumanEval, and MBPP.
-
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Reasoning messages between heterogeneous VLMs can be routed through the image-token span: a distilled universal codec plus affine alignment transmits latent traces across model families, cutting wall-clock time in sma...
-
Latent Collaboration in Multi-Agent Systems
Replacing text inter-agent dialogue with direct transfer of hidden-state (KV-cache) representations cuts output tokens by ~70-84%, speeds inference ~4x, and keeps multi-agent accuracy roughly on par or slightly better.
-
LaRe: Latent Refocusing for Multimodal Reasoning
LaRe performs iterative visual refocusing in latent space and reports accuracy gains with fewer tokens, but its main experiments compare against baselines trained with less data.
-
Throttling Web Agents Using Reasoning Gates
Rebus-based reasoning gates, puzzles built from random word/domain clue sets, impose token costs on LM web agents that are up to 9.2x the generator's cost.
-
Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It
A systematic human audit of SocialIQa, FauxPas-EAI and ToMi shows that benchmark scores are inflated or distorted by data flaws, rigid scoring, and sensitivity to phrasing.
-
Implicit Reasoning in Large Language Models: A Comprehensive Survey
A survey organizing implicit (silent) reasoning in LLMs into three execution paradigms, plus evidence, benchmarks, and challenges.
-
Improving Large Language Models with Concept-Aware Fine-Tuning
Adding lightweight multi-token auxiliary heads with a weighted future-token loss improves supervised fine-tuning of Llama-3-8B-Instruct across five diverse tasks.
Discussion (0). Sign in to comment.