Why some quantum materials stall while others scale
In a new study, MIT researchers evaluated quantum materials’ potential for scalable commercial success — and identified promising candidates.
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Offline Fictitious Self-Play for Competitive Games
arXiv:2403.00841v2 Announce Type: replace-cross Abstract: Offline Reinforcement Learning (RL) enables policy improvement from fixed datasets without online interactions, making it highly suitable for real-world applications lacking efficient simulators. Despite its success in the single-agent setting, offline multi-agent RL remains a…
Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators
arXiv:2507.19349v2 Announce Type: replace Abstract: As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and optimization. However, acquiring such maps at high resolution is often cost-prohibitive, creating a severe…
A Generalized Information Bottleneck Theory of Deep Learning
arXiv:2509.26327v2 Announce Type: replace Abstract: The Information Bottleneck (IB) principle offers a compelling theoretical framework to understand how neural networks (NNs) learn. However, its practical utility has been constrained by unresolved theoretical ambiguities and significant challenges in accurate estimation. In…
Evaluating multiple models using labeled and unlabeled data
arXiv:2501.11866v3 Announce Type: replace Abstract: It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain, unlabeled data is plentiful. Here, we introduce Semi-Supervised…
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
arXiv:2505.16690v3 Announce Type: replace Abstract: Post-training of large language models is essential for adapting pre-trained language models (PLMs) to align with human preferences and downstream tasks. While PLMs typically exhibit well-calibrated confidence, post-trained language models (PoLMs) often suffer from over-confidence,…
ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis
arXiv:2510.10774v2 Announce Type: replace-cross Abstract: Existing Persian speech datasets are typically smaller than their English counterparts, which creates a key limitation for developing Persian speech technologies. We address this gap by introducing ParsVoice, the largest Persian speech corpus designed specifically…
Wavefront Coding for Accommodation-Invariant Near-Eye Displays
arXiv:2510.12778v1 Announce Type: cross Abstract: We present a new computational near-eye display method that addresses the vergence-accommodation conflict problem in stereoscopic displays through accommodation-invariance. Our system integrates a refractive lens eyepiece with a novel wavefront coding diffractive optical element, operating…
Balancing Synthetic Data and Replay for Enhancing Task-Specific Capabilities
arXiv:2510.11842v1 Announce Type: new Abstract: Adapting language models to new tasks through continued pretraining faces a fundamental trade-off: models must learn new capabilities while avoiding catastrophic forgetting of existing knowledge. While prior work has studied synthetic data generation techniques, the…
Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection
arXiv:2510.11852v1 Announce Type: new Abstract: Recent advances in open-source vision-language models (VLMs) offer new opportunities for understanding complex and subjective multimodal phenomena such as sarcasm. In this work, we evaluate seven state-of-the-art VLMs – BLIP2, InstructBLIP, OpenFlamingo, LLaVA, PaliGemma, Gemma3,…