The paper introduces Probabilistic Latent Reasoning Models (PLRM), a parallelizable variational framework that moves reasoning from token‑by‑token language generation into a continuous latent space. PLRM uses a JEPA‑style objective to infer latent distributions in parallel and a mixture‑based prior with a discrete strategy variable to preserve multiple distinct reasoning trajectories. Experiments show PLRM matches deterministic latent models on single‑trajectory tasks and outperforms stochastic baselines on multi‑trajectory benchmarks while enabling efficient training.
arXiv:2606. 15160v1 Announce Type: cross Abstract: Reasoning capabilities of multimodal large language models (MLLMs) have improved considerably in recent years.
VaME (Variational Multimodal Embeddings) introduces a framework that treats latent reasoning as a learnable distribution over trajectories, enabling autoregressive exploration in latent space guided by answer reconstruction. By augmenting embedding-token readout with a latent-fused embedding and optimizing over stochastic variational trajectories via reinforcement learning with a Semantic Decoding Reward, VaME achieves superior performance on the MMEB-V2 benchmark and reasoning-intensive tasks like MRMR. The method also delivers at least a 4.25× inference speedup compared to deterministic latent autoregressive baselines.
arXiv:2606. 06447v1 Announce Type: cross Abstract: Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation.
arXiv:2606. 16222v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead.
Generative Recursive Reasoning (GRAM) extends Recursive Reasoning Models by introducing probabilistic multi‑trajectory computation, allowing neural reasoning systems to explore multiple latent trajectories instead of a single deterministic path. GRAM treats reasoning as a stochastic latent trajectory, enabling multiple hypotheses, alternative solution strategies, and scalable inference through recursive depth and parallel sampling. Trained via amortized variational inference, GRAM outperforms deterministic recurrent and recursive baselines on structured reasoning and multi‑solution constraint satisfaction tasks, and also supports unconditional generation.
arXiv:2606. 29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood.
Large language models often improve reasoning by generating explicit chain-of-thought (CoT), demonstrating the importance of intermediate computation. However, textual CoT forces this computation through a discrete, serial, and communication-oriented token stream: each reasoning step must be verbalized before the model can proceed, even when the underlying update is semantic, uncertain, or only partially formed.
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...
arXiv:2606. 10184v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) relies on the diversity of $K$ rollouts within each group; otherwise, the group-mean advantage $A^{(k)} = r^{(k)} - \mu_r$ collapses to zero.
The paper introduces LLoCoT, a looped latent‑reasoning framework that replaces the traditional left‑to‑right generation of chain‑of‑thought tokens with iterative refinement of a compact latent workspace using a shared transformer. By jointly updating latent slots over a few iterations and sampling latent tokens in parallel, LLoCoT feeds these into an autoregressive decoder for answer generation. Experiments on HumanEval and MBPP show that LLoCoT matches or surpasses explicit‑CoT baselines while dramatically reducing reasoning latency and increasing throughput.
arXiv:2607.26621v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities, motivating their use as the backbone of foundation recommendati...
Parason is a new framework that discovers and exploits both subtask and trial parallelism in large language model (LLM) reasoning. By converting sequential reasoning traces into structured parallel trajectories and training with Parallelism-Aware Group Relative Policy Optimization, it balances accuracy, latency, and parallelism. Experiments on mathematical reasoning benchmarks such as AIME24 and AIME25 show that Parason achieves an average acceleration of about 1.7× while maintaining competitive accuracy.