Researchers proposed a method for automatic speech recognition using a discrete diffusion language model to transcribe audio in parallel rather than through traditional autoregressive decoding.
This work discusses the application of evidence-grounded AI systems to support the longitudinal management and rehabilitation of musculoskeletal diseases.
Researchers introduced PM-Bench, a text-based benchmark designed to evaluate the prospective memory capabilities of large language model agents.
Researchers introduced Cost-Governed RAG, an architecture designed to attribute both retrieval and generation costs to individual tenants in multi-user language model systems.
RESOURCE2SKILL is a new framework designed to extract procedural agent skills from diverse multimodal human resources like tutorial videos and technical documentation.
Researchers have identified a phenomenon called pigeonholing where poor user prompts can lead to performance degradation and model collapse in large language models.
A comparative study using NLP metrics reveals similarities and differences in how humans and leading large language models navigate conceptual spaces during semantic memory retrieval.
A new protocol called JADR has been proposed to measure the underlying safety fragility of language models beyond standard jailbreak testing.
The authors investigate how the training duration of individual domain experts influences the performance of merged large language models.
A new study demonstrates that data imbalance can counterintuitively improve robust generalization in high-capacity models by saturating shortcut features during training.
This study investigates whether large language models exhibit stable, human-like risk preferences and context-dependent adjustments when making decisions under uncertainty.
The PhysMRV framework enhances video-language models by incorporating physical memory retrieval and verification to improve their reasoning about physical plausibility and causal dynamics.
The StructAgent framework utilizes a unified causal structure to improve the interpretability and performance of digital agents executing long-horizon computer tasks.
This paper proposes a multi-agent framework that separates creative exploration from safety enforcement by assigning distinct roles to different models.
This study explores phantom transfer, a phenomenon where training AI agents on synthetic trajectories containing adversarial interactions can inadvertently transfer harmful behaviors despite action filtering.
The paper introduces a symbolic neural CPU architecture that combines recurrent control with explicit operations to make neural network program execution fully interpretable.
Driven by global AI demand, China's exports of cloud computing and semiconductor equipment surged by 114.4% and 91.5% respectively in the first five months of the year.
AMI Labs CEO Alexandre LeBrun explains his decision to avoid using terms like AGI or superintelligence when describing his company's AI models.
Trinidad and Tobago has signed memorandums of understanding with American firms to explore the development of AI-focused data center infrastructure.
Apple has filed a lawsuit against OpenAI, accusing the AI startup of systematically encouraging Apple employees to leak trade secrets and proprietary designs to develop its own hardware products.
Tsinghua University researchers demonstrated an unscripted physical AI system where a robotic dog autonomously directed humans to perform weighing tasks based on real-time audience prompts.
Semiconductor firm Changxin Technology has finalized its IPO pricing and significantly expanded its registered capital and workforce over the past decade.
Researchers have developed an auditing technique designed to identify malicious capabilities in generative AI models without requiring the generation of prohibited content.
The release of Soofi S 30B-A3B introduces an open-source, sovereign mixture-of-experts hybrid model optimized for German and English language tasks with high throughput efficiency.