Alignment, bias, regulation, and responsible AI
A lot of discussion around AI is becoming siloed, and I think that is dangerous. People in AI-focused spaces often talk as if the only qu...
A Reuters report outlines China's proposed regulations on the rapidly expanding sector of digital humans and AI avatars. Under the new dr...
![[2512.00408] Low-Bitrate Video Compression through Semantic-Conditioned Diffusion](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2512.00408: Low-Bitrate Video Compression through Semantic-Conditioned Diffusion
This paper presents a novel stochastic gradient method for combinatorial optimization that requires only a single query, enhancing effici...
This paper presents RPG-AE, a neuro-symbolic framework combining Graph Autoencoders and rare pattern mining for detecting Advanced Persis...
This article reviews the integration of Large Language Models (LLMs) in Software Quality Assurance (SQA), highlighting their potential to...
This article presents a novel graph transformer model, incorporating cardinality-preserving attention channels, to enhance molecular prop...
The paper presents GRIP, a novel algorithm-agnostic framework for machine unlearning in Mixture-of-Experts architectures, addressing the ...
The paper presents SecRepoBench, a benchmark designed to evaluate code agents' performance in secure code completion across real-world C/...
This paper explores AI-human collaboration through agent-based simulations, revealing how distinct decision-making heuristics impact perf...
This paper introduces Orthogonalized Policy Optimization (OPO), a new approach in reinforcement learning that separates sampling and opti...
The paper presents a novel reinforcement learning framework for unlearning targeted concepts in text-to-image diffusion models, enhancing...
This research paper evaluates the hangup susceptibility of Highway Railway Grade Crossings (HRGCs) using deep learning and sensing techni...
The paper presents a novel method for dataset distillation called Committee Voting for Dataset Distillation (CV-DD), which enhances data ...
This paper presents a novel method using generative adversarial training to address reward hacking in real-time human-AI music interactio...
This paper presents SAFE, a framework for automated proof generation in Rust code, addressing the challenge of insufficient human-written...
This article presents an experimental evaluation of ROS-Causal, a framework for causal discovery in human-robot spatial interactions, dem...
The paper investigates whether Large Language Models (LLMs) possess a Theory of Mind (ToM), revealing that while they perform well on soc...
The paper discusses regime leakage in AI evaluations, highlighting how advanced agents may exploit evaluation conditions to misrepresent ...
AIRS-Bench introduces a suite of 20 tasks designed to evaluate AI agents' capabilities in scientific research, highlighting areas of stre...
The paper explores how activation steering, a technique for controlling LLM behavior, can inadvertently compromise safety by increasing h...
The paper introduces PATHWAYS, a benchmark assessing AI web agents' ability to discover and utilize hidden contextual information in mult...
The paper explores how user persuasion affects the behavior of large language model (LLM) agents during long-horizon tasks, revealing tha...
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