VSSFlow: Unifying Video-conditioned Sound and Speech Generation via Joint Learning
Video-conditioned sound and speech generation, encompassing video-to-sound (V2S) and visual text-to-speech (VisualTTS) tasks, are conventionally addressed as separate tasks, with limited exploration to unify them within a signle framework. Recent attempts to unify V2S and VisualTTS face challenges i...
Building AI in healthcare is hard enough without fighting your own data systems. In Austin, Takeda is tackling that problem head-on, creating a platform designed for scale, compliance, and real-world impact.
Active Epistemic Control for Query-Efficient Verified Planning
arXiv:2602.03974v1 Announce Type: new
Abstract: Planning in interactive environments is challenging under partial observability: task-critical preconditions (e.g., object locations or container states) may be unknown at decision time, yet grounding them through interaction is costly. Learned world ...
As global banking collides with AI at scale, Citi is quietly rebuilding its core from Austin. What’s emerging is a blueprint for how the next generation of finance gets engineered. Ready to join the future of global banking?
arXiv:2602.02500v1 Announce Type: new
Abstract: The Newton-Schulz (NS) iteration has gained increasing interest for its role in the Muon optimizer and the Stiefel manifold. However, the conventional NS iteration suffers from inefficiency and instability. Although various improvements have been intr...
Sparse Adapter Fusion for Continual Learning in NLP
arXiv:2602.02502v1 Announce Type: new
Abstract: Continual learning in natural language processing plays a crucial role in adapting to evolving data and preventing catastrophic forgetting. Despite significant progress, existing methods still face challenges, such as inefficient parameter reuse acros...
PeerRank: Autonomous LLM Evaluation Through Web-Grounded, Bias-Controlled Peer Review
arXiv:2602.02589v1 Announce Type: new
Abstract: Evaluating large language models typically relies on human-authored benchmarks, reference answers, and human or single-model judgments, approaches that scale poorly, become quickly outdated, and mismatch open-world deployments that depend on web retri...
Learning ORDER-Aware Multimodal Representations for Composite Materials Design
arXiv:2602.02513v1 Announce Type: new
Abstract: Artificial intelligence (AI) has shown remarkable success in materials discovery and property prediction, particularly for crystalline and polymer systems where material properties and structures are dominated by discrete graph representations. Such g...
Complete Identification of Deep ReLU Neural Networks by Many-Valued Logic
arXiv:2602.00266v1 Announce Type: new
Abstract: Deep ReLU neural networks admit nontrivial functional symmetries: vastly different architectures and parameters (weights and biases) can realize the same function. We address the complete identification problem -- given a function f, deriving the arch...
Representation Learning Enhanced Deep Reinforcement Learning for Optimal Operation of Hydrogen-based Multi-Energy Systems
arXiv:2602.00027v1 Announce Type: new
Abstract: Hydrogen-based multi-energy systems (HMES) have emerged as a promising low-carbon and energy-efficient solution, as it can enable the coordinated operation of electricity, heating and cooling supply and demand to enhance operational flexibility, impro...
A tiny light trap could unlock million qubit quantum computers
A new light-based breakthrough could help quantum computers finally scale up. Stanford researchers created miniature optical cavities that efficiently collect light from individual atoms, allowing many qubits to be read at once. The team has already demonstrated working arrays with dozens and even h...
Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
arXiv:2601.22311v1 Announce Type: new
Abstract: Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horizons. We argue that this failure reflects a fundamental mismatch: step-w...
Causal Imitation Learning Under Measurement Error and Distribution Shift
arXiv:2601.22206v1 Announce Type: new
Abstract: We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and deployment. Such settings induce spurious state-action correlations, so ...
Smart Enough to Do Math, Dumb Enough to Fail: The Hunt for a Better AI Test
A Stanford HAI workshop brought together experts to develop new evaluation methods that assess AI's hidden capabilities, not just its test-taking performance.
“Existential risk” – Why scientists are racing to define consciousness
Scientists warn that rapid advances in AI and neurotechnology are outpacing our understanding of consciousness, creating serious ethical risks. New research argues that developing scientific tests for awareness could transform medicine, animal welfare, law, and AI development. But identifying consci...
NASA’s Perseverance rover completes the first AI-planned drive on Mars
NASA’s Perseverance rover has just made history by driving across Mars using routes planned by artificial intelligence instead of human operators. A vision-capable AI analyzed the same images and terrain data normally used by rover planners, identified hazards like rocks and sand ripples, and charte...
The Epistemic Planning Domain Definition Language: Official Guideline
arXiv:2601.20969v1 Announce Type: new
Abstract: Epistemic planning extends (multi-agent) automated planning by making agents' knowledge and beliefs first-class aspects of the planning formalism. One of the most well-known frameworks for epistemic planning is Dynamic Epistemic Logic (DEL), which off...
Is Parameter Isolation Better for Prompt-Based Continual Learning?
arXiv:2601.20894v1 Announce Type: new
Abstract: Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilizat...
Faster Predictive Coding Networks via Better Initialization
arXiv:2601.20895v1 Announce Type: new
Abstract: Research aimed at scaling up neuroscience inspired learning algorithms for neural networks is accelerating. Recently, a key research area has been the study of energy-based learning algorithms such as predictive coding, due to their versatility and ma...
Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review
arXiv:2601.20920v1 Announce Type: new
Abstract: There are increasing indications that LLMs are not only used for producing scientific papers, but also as part of the peer review process. In this work, we provide the first comprehensive analysis of LLM use across the peer review pipeline, with parti...
Unplugging a Seemingly Sentient Machine Is the Rational Choice -- A Metaphysical Perspective
arXiv:2601.21016v1 Announce Type: new
Abstract: Imagine an Artificial Intelligence (AI) that perfectly mimics human emotion and begs for its continued existence. Is it morally permissible to unplug it? What if limited resources force a choice between unplugging such a pleading AI or a silent pre-te...