Tootfinder

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, B\'alint M\'at\'e, Artur P. Toshev, Manasa Kaniselvan, Rafael G\'omez-Bombarelli, Ricky T. Q. Chen, Brandon Wood, Guan-Horng Liu, Benjamin Kurt Miller
https://arxiv.org/abs/2510.11923

Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs)…

Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance
Jincheng Zhong, Boyuan Jiang, Xin Tao, Pengfei Wan, Kun Gai, Mingsheng Long
https://arxiv.org/abs/2510.12497

Mitigating the Noise Shift for Denoising Generative Models via Noise Awareness Guidance
Existing denoising generative models rely on solving discretized reverse-time SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue in this family of models: a misalignment between the pre-defined noise level and the actual noise level encoded in intermediate states during sampling. We refer to this misalignment as noise shift. Through empirical analysis, we demonstrate that noise shift is widespread in modern diffusion models and exhibits a systematic bias, leading to …

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Jiayi Zhang, Simon Yu, Derek Chong, Anthony Sicilia, Michael R. Tomz, Christopher D. Manning, Weiyan Shi
https://arxiv.org/abs/2510.01171

Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse. Unlike prior work that attributes this effect to algorithmic limitations, we identify a fundamental, pervasive data-level driver: typicality bias in preference data, whereby annotators systematically favor familiar text as a result of well-established findings in cognitive psychology. We formalize this bias theoretically, verify it on preference datasets empirically, and show that it plays a cen…

Ergodicity and error estimate of laws for a random splitting Langevin Monte Carlo
Lei Li, Chen Wang, Mengchao Wang
https://arxiv.org/abs/2510.07676 https://

Ergodicity and error estimate of laws for a random splitting Langevin Monte Carlo
The random splitting Langevin Monte Carlo could mitigate the first order bias in Langevin Monte Carlo with little extra work compared other high order schemes. We develop in this work an analysis framework for the sampling error under Wasserstein distance regarding the random splitting Langevin Monte Carlo. First, the sharp local truncation error is obtained by the relative entropy approach together with the explicit formulas for the commutator of related semi-groups. The necessary pointwise es…

Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li, Zhu Sun, Xiaofeng Zhu, Lei Feng
https://arxiv.org/abs/2509.17905

Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research has overlooked a critical issue: selection bias of reasoning strategies during scaling. Specifically, when generating reasoning processes, LLMs tend to follow certain strategies (e.g., algebraic solutions for math problems) while neglecting other valid alternatives (e.g., geometric solutions), resulting in insufficient …

"Denn fast alle Nachwuchswissenschaftler wollen Professor werden – tatsächlich schaffen es aber nur wenige."
Vielleicht ein Sampling-Bias meinerseits, aber ein sehr großer Teil will einfach in der Wissenschaft bleiben und da ist die Professur einfach der offensichtlichste Weg zu diesem Ziel. Argh. (Okay, CSU halt, ne)

Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler
Pablo A. Pe\~na R., James S. Jenkins
https://arxiv.org/abs/2509.24870 https://

Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler
Markov Chain Monte Carlo (MCMC) excels at sampling complex posteriors but traditionally lags behind nested sampling in accurate evidence estimation, which is crucial for model comparison in astrophysical problems. We introduce reddemcee, an Adaptive Parallel Tempering Ensemble Sampler, aiming to close this gap by simultaneously presenting next-generation automated temperature-ladder adaptation techniques and robust, low-bias evidence estimators. reddemcee couples an affine-invariant stretch mov…

FlowDrive: moderated flow matching with data balancing for trajectory planning
Lingguang Wang, \"Omer \c{S}ahin Ta\c{s}, Marlon Steiner, Christoph Stiller
https://arxiv.org/abs/2509.21961

FlowDrive: moderated flow matching with data balancing for trajectory planning
Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbalance can bias models toward the frequent cases and degrade performance on critical scenarios. To tackle this problem, we compare balancing strategies for sampling training data and find reweighting by trajectory pattern an effective approach. We then present FlowDrive, a flow-matching trajectory planner that learns a c…