High-performance training and inference for deep equivariant interatomic potentials
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic modeling tasks like molecular dynamics and high-throughput screening. The size of datasets and demands of downstream workflows are growing rapidly, making...
A Markov Chain Monte Carlo Method for Efficient Finite-Length LDPC Code Design
Low-density parity-check (LDPC) codes are among the most prominent error-correction schemes. They find application to fortify various modern storage, communication, and computing systems. Protograph-based (PB) LDPC codes offer many degrees of freedom in the code design and enable fast encoding and decoding....
DATETIME: A new benchmark to measure LLM translation and reasoning capabilities
This paper introduces DATETIME, a new high-quality benchmark designed to evaluate the translation and reasoning abilities of a Large Language Model (LLM) on datetimes. A datetime is simply a date and a time, for example ’11th.february.2023 ,1:12:31′. Datetimes are an interesting domain...
Guiding VLM Agents with Process Rewards at Inference Time for GUI Navigation
Recent advancements in visual language models (VLMs) have notably enhanced their capabilities in handling complex Graphical User Interface (GUI) interaction tasks. Despite these improvements, current frameworks often struggle to generate correct actions in challenging GUI environments. State-of-the-art commercial VLMs are black-boxes, and...
From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuning
Recent text-to-image diffusion models achieve impressive visual quality through extensive scaling of training data and model parameters, yet they often struggle with complex scenes and fine-grained details. Inspired by the self-reflection capabilities emergent in large language models, we propose ReflectionFlow, an inference-time...
TTRL: Test-Time Reinforcement Learning
This paper investigates Reinforcement Learning (RL) on data without explicit labels for reasoning tasks in Large Language Models (LLMs). The core challenge of the problem is reward estimation during inference while not having access to ground-truth information. While this setting appears elusive,...
Extreme mass ratio inspirals around topological stars
We study a point scalar charge in circular orbit around a topological star, a regular, horizonless soliton emerging from dimensional compactification of Einstein-Maxwell theory in five dimensions, which could describe qualitative properties of microstate geometries for astrophysical black holes. This is the...
Response of the LMC’s Bar to a Recent SMC Collision and Implications for the SMC’s Dark Matter Profile
The LMC’s stellar bar is offset from the outer disk center, tilted from the disk plane, and does not drive gas inflows. These properties are atypical of bars in gas-rich galaxies, yet the LMC bar’s strength and radius are similar to typical...
Robust Mixed-State Cluster States and Spurious Topological Entanglement Negativity
We investigate 1D and 2D cluster states under local decoherence to assess the robustness of their mixed-state subsystem symmetry-protected topological (SSPT) order. By exactly computing fidelity correlators via dimensional reduction of effective statistical mechanics models, we pinpoint the critical error rate for...
Topological Origin of Andreev Flatland in UTe$_2$: Implication for Josephson STM Measurements
We propose the surface of topological superconductors as a platform for realizing two-dimensional flat bands, where electron interactions play a crucial role. The surface flat bands originate from topological features supported by two key mechanisms: (1) a trivial Chern number prevents the...