Sampling plays an important role in long-form language-model reasoning. Over thousands of decoding steps, small changes in the candidate token set can compound into different reasoning trajectories, stability profiles, and final answers. Existing truncation methods such as top-$p$, min-$p$, and fixed top-$nσ$ sampling improve over unrestricted sampling, but they rely on fixed thresholds that cannot adapt to changes in entropy, task difficulty, training stage, or generation budget. We introduce Adaptive Nucleus Truncation Sampling (ANTS), which extends top-\(nσ\) sampling from a fixed decoding rule into an adaptive rollout-control mechanism for long-form generation. ANTS selects standardized neighborhoods around the maximum logit before temperature scaling, adapts the truncation width using an entropy-conditioned controller, and retains a no-truncation fallback arm to stabilize training when truncation becomes unsafe. On a 33B-total / 4B-active sparse Mixture-of-Experts reasoning model, ANTS improves average performance over percentage-based benchmarks by +1.9, +3.8, and +5.2 points at 8K, 16K, and 32K generation budgets, respectively. The strongest gains appear on instruction following and mathematical reasoning, with IFBench improving by more than 10 points at 32K and AIME 2025 improving by 7 points. Code generation reveals an important budget interaction. On Codeforces, ANTS trails the baseline at 8K, but reverses this gap and substantially improves ELO at 16K and 32K. These results suggest that sampler design should be treated not just as a decoding hyperparameter, but as part of how we stabilize and scale long-budget reasoning.

Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the framework of existing works proposed in other domains without considering the characteristics of TS data, leading to suboptimal performance. In this work, wepropose Adaptive Noise schedule for Time series diffusion models (ANT), which automatically predetermines proper noise schedules for given TS datasets based on their statistics representing non-stationarity. Our intuition is that an optimal noise schedule should satisfy the following desiderata: 1) It linearly reduces the non-stationarity of TS data so that all diffusion steps are equally meaningful, 2) the data is corrupted to the random noise at the final step, and 3) the number of steps is sufficiently large. The proposed method is practical for use in that it eliminates the necessity of finding the optimal noise schedule with a small additional cost to compute the statistics for given datasets, which can be done offline before training.

Why it's high time we stopped anthropomorphising ants We have long drawn parallels between ants and humans. Now we are comparing the insects to computers. Pollution is making many cities unlivable for their human inhabitants, but it is also tearing ant families and communities apart. Ants recognise each other by sniffing a thin layer of hydrocarbons on the outside of their exoskeletons; each colony has a specific "smell". But a new study reveals that ozone emissions can change the structure of these hydrocarbons.

WheneverMSTandδ-MBST19 have throughput close to RING, they achieve faster training, as they have better spectral properties. Comparison with MATCHA (Review #2) The reviewer is right that MATCHA [99] selects more frequently the25 important links.

Note that ˆφ is feasible for the constrained optimization problem. We refer to it as an "early stopping scheme" because the key idea is to return to the parameter values which gave the lowest validation error (see Section 7.8 of Goodfellow et al.[3]). In our implementation, we initialize two upper envelope networks with parametersφ and φ0, where φ is trained using the penalty loss, andφ0 records the parameters with the lowest validation error encounteredsofar. IfLφ > Lφ0, we count the number of consecutive times this occurs. Notonlyis this not standard practice, but to makeafair comparison across all algorithms, this would require, foreachofthe fivealgorithms, performing aseparate hyper-parameter search foreachofthe five environments.

Ant evolution favored large colonies over individual strength. Breakthroughs, discoveries, and DIY tips sent every weekday. Here's a fun (and creepy) fact: The Earth is home to approximately 20 quadrillion ants . To put zeroes on it, that's around 20,000,000,000,000,000 of the six-legged insects living all around us. How did such diminutive creatures attain their prominent--and ecologically vital -role on the planet?

New research shows ill pupae emit a chemical signal before ever leaving their cocoons. Breakthroughs, discoveries, and DIY tips sent every weekday. Ants are some of nature's most selfless animals. They practice social distancing when ill, consistently act for the good of the colony, and will die to protect their queen from outsiders. This evolutionary drive is so strong that at least one ant species will even willingly sacrifice before they leave their cocoons.