Hugging Face Trending Papers
Unrolled Flow Models for Reasoning
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arXiv Machine Learning
Oct 8
Unrolled Flow Models for Reasoning
arXiv:2610.09759v1 Announce Type: new Abstract: Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear. We prove that a flo...
Hugging Face Trending Papers
Oct 7
Efficient Reasoning with Flow Language Models
Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reasoning remains unclear. We inve...
arXiv AI
Jul 29
Penelope: Localized Latent Recurrence for Efficient Structured 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 Machine Learning
Sep 14
Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Models
The paper introduces Decision-Flow Sampling (DF‑Sample), a training‑free, data‑free inference framework that builds a hierarchical reasoning tree, evaluates entire trajectories, and back‑propagates utilities to guide branching decisions. Unlike local step‑wise sampling, DF‑Sample explicitly assesses global paths, enabling it to recover high‑quality, low‑probability reasoning chains that standard decoding misses. On the GPQA benchmark, DF‑Sample attains 45.6% accuracy, outperforming power sampling (38.9%) and GRPO (39.9%) and consistently surpassing baselines across multiple models and benchmarks, demonstrating significant latent reasoning potential in pretrained LLMs.
arXiv AI
Jul 2
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
arXiv:2607. 00341v1 Announce Type: cross Abstract: Large language models achieve strong performance on many reasoning tasks when allowed to externalize intermediate steps as Chain-of-Thought (CoT).
arXiv Machine Learning
Oct 9
State Stream Transformer (SST) V2: Parallel Training of Nonlinear Recurrence for Latent Space Reasoning
The State Stream Transformer (SST) V2 introduces a parameter‑efficient, FFN‑driven nonlinear recurrence that streams latent states horizontally across a sequence, enabling continuous latent deliberation at inference time. A two‑pass parallel training procedure approximates this recurrence, making co‑training practical. Experiments on GSM8K and GPQA‑Diamond show that SST achieves a 61.11% architectural capacity bound and a 15.15‑point improvement over a fine‑tuning‑matched baseline, while reducing GSM8K errors by 46%.