The paper introduces Untied Self-Conditioning, a sampler that corrects a train–inference mismatch in flow‑matching language models. By dampening redundant directions in the self‑conditioning input and approximating a step‑average prediction from history, the method improves generation quality without retraining. On LangFlow and ELF‑B datasets, it dramatically lowers perplexity and is preferred in the majority of pairwise comparisons.
arXiv:2606. 01838v1 Announce Type: cross Abstract: Agentic language model systems alternate between two structurally distinct step types: structured tool calls (short, deterministic, low perplexity) and open-ended planning/reasoning steps (long, complex, high perplexity).
The paper introduces SelfMorph, a method that lets frozen large language models adapt their internal computation topology at inference time based on the input. It combines three morphing strategies—BASE, REPEAT, and BRANCH—into a unified space and uses a lightweight selector trained via distillation to choose the best morphology for each prompt. Experiments on 4B–27B models show that SelfMorph improves performance by an average of 8.06% on in‑distribution benchmarks and 4.45% on unseen benchmarks, outperforming other settings in most cases.
arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept seve...
arXiv:2605. 21854v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.
Large language models can solve complex multi‑hop tasks but often fail on simple two‑hop queries, even when each hop is individually correct. In a controlled symbolic setting, the authors find that models generalize reliably when the second hop follows the training distribution, but always fail when it deviates. Mechanistic analysis shows that successful generalization relies on consistent intermediate representations across contexts, whereas failures arise from a mismatch between lower‑layer representation construction and upper‑layer mapping to outputs. The study proposes a recurrent‑style training strategy that improves out‑of‑distribution two‑hop generalization.
Flow Reasoning Models (FRMs) are a new framework that turns continuous flow models into efficient recurrent reasoners for structured tasks. By self‑conditioning a flow model on its own past outputs, FRMs iteratively refine solutions, allowing parallel decision making and revision. The authors introduce Fixed‑Point Forcing (FPF) to mitigate exposure bias at deeper recursion, and report near‑perfect solve rates on Sudoku‑Extreme, Zebra, and Maze‑Unique, outperforming existing masked‑diffusion and specialized baselines while using far fewer inference FLOPs.
The paper challenges the common practice of selecting latent world models based on one‑step prediction loss, arguing that this metric poorly predicts planning performance. Using a frozen‑decoder protocol, the authors decompose one‑step error into encoder‑inherent and predictor‑added components and evaluate goal‑conditioned planning across three continuous‑control environments. Their experiments show that improvements in one‑step fidelity do not correlate with planning success, with correlation coefficients near zero and inconsistent exchange rates across tasks.
The paper investigates two key design levers for Continuous Diffusion Language Models (CDLMs): self‑conditioning and embedding geometry. It shows that self‑conditioning mitigates a train/inference mismatch and can be implemented efficiently via a two‑level interpolant called DoubleCat, which significantly boosts TinyGSM accuracy. By adding discrete reveals to DoubleCat, the authors create SemiCat, achieving discrete‑diffusion‑level performance and outperforming discrete diffusion on several likelihood‑based benchmarks at 1.7B parameters. The study also finds that cross‑entropy training drives embeddings toward a geometry with decoding error rates comparable to one‑hot representations, and briefly explores additional options such as hybrid corruption processes and asynchronous noise schedules.
The paper introduces Self-Regularized Flow Language Models (SERF), a self‑supervised framework that enhances continuous diffusion language models by leveraging the denoising trajectory. SERF employs noisy‑to‑clean self‑distillation, where an exponential moving average teacher provides additional supervision along the same interpolation path, and offers two variants—SERF‑L aligning intermediate representations and SERF‑P aligning output token distributions. Experiments on three continuous language‑model paradigms show significant gains in training speed, generation quality, and sampling efficiency, achieving up to 7.7× faster training, 24.3% MAUVE improvement, and 28.8% higher GSM8K accuracy. "whyItMatters":"The results demonstrate that noisy‑to‑clean self‑distillation can substantially improve continuous diffusion language models without external pretraining or inference changes, offering a practical path to faster, higher‑quality language generation."
Osprey is a target‑agnostic pre‑training method that bootstraps draft models for speculative decoding from existing small language models. By pruning to a shallow backbone, restoring language‑modeling capability with next‑token pretraining, and adapting via vocabulary alignment and distillation, Osprey reduces per‑target work to a lightweight adaptation step. Experiments show that a single Osprey backbone improves mean acceptance length by up to 22.7% and increases tokens per second by 17.5% across several large target models, especially on out‑of‑domain and multilingual data.
The paper introduces the Latent Generative Solver (LGS), a neural PDE solver that combines a Physics VAE, a Pyramidal Flow-Forcing Transformer, and input noising to achieve generalization across twelve PDE families and stable long-term rollouts. LGS matches or surpasses deterministic baselines on one-step predictions, outperforms them on 5- and 10-step rollouts, and significantly reduces long-horizon error while cutting compute costs. It also adapts efficiently to unseen higher-resolution systems, demonstrating strong empirical performance on 2D regular-grid PDE simulations.
arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.