survey

A categorized directory of LLM-based multi-agent papers, including a survey paper and organized list of frameworks, orchestration, problem solving, simulation, and benchmarks.

This survey paper introduces World Action Models (WAMs), a unified framework for embodied AI that integrates predictive state modeling with action generation. It provides a taxonomy of existing methods, analyzes the data ecosystem, and outlines evaluation protocols for this emerging paradigm.

The article discusses the challenge of maintaining user trust in AI agents that provide commercial recommendations, highlighting a lack of standards for transparency and responsibility. It calls for feedback from developers on implementing reliable and transparent recommendation mechanisms.

A survey reveals 80% of CEOs fear job loss if AI initiatives fail, emphasizing the need to empower early adopters and streamline management processes.

This survey paper proposes an evolutionary framework for LLM agent memory mechanisms, categorizing their development into three stages: storage, reflection, and experience. It analyzes core drivers such as long-range consistency and continual learning to provide design principles for next-generation agents.

This survey paper provides a comprehensive review of audio-visual intelligence within large foundation models, establishing a unified taxonomy, synthesizing core methodologies, and outlining key datasets, benchmarks, and open research challenges.

This comprehensive survey reviews the literature on world models for robot learning, covering their roles in policy learning, planning, and simulation. It highlights key paradigms, benchmarks, and future directions for predictive modeling in embodied agents.

A multi-institution survey proposes a three-layer trust framework to align technical, clinical, and human-centered requirements for trustworthy AI in mental-health support.

Anthropic launches a monthly survey of Claude users to gather qualitative data on how AI is changing work, aiming to better understand AI's economic impact.

A comprehensive survey on how transliteration bridges the script barrier in cross-lingual NLP, boosting transfer learning for low-resource languages and offering practical implementation guidance.

Gallup poll shows Gen Z AI usage rising but excitement falling from 36% to 22%, driven by job anxiety as nearly half see workplace risks outweighing benefits.

This paper presents a comprehensive survey of data mixing methods for LLM pretraining, formalizing the problem as bilevel optimization and introducing a taxonomy that distinguishes static (rule-based and learning-based) from dynamic (adaptive and externally guided) mixing approaches. The authors analyze trade-offs, identify cross-cutting challenges, and outline future research directions including finer-grained domain partitioning and pipeline-aware designs.

A Gallup poll reveals that 32% of Americans earning under $24,000 use AI for health advice instead of visiting doctors, compared to 14% overall, with an estimated 14 million U.S. adults skipping provider visits due to AI-generated health information in the past 30 days.

A comprehensive survey examining image classification into high-level and abstract categories, clarifying the tacit understanding of high-level semantics in computer vision through multidisciplinary analysis of commonsense, emotional, aesthetic, and interpretative semantics. The paper identifies persistent challenges in abstract concept image classification and emphasizes the importance of hybrid AI systems for addressing complex visual reasoning tasks.

A comprehensive survey reviewing recent advances in intrinsic interpretability for Large Language Models, categorizing approaches into five design paradigms: functional transparency, concept alignment, representational decomposability, explicit modularization, and latent sparsity induction. The paper addresses the challenge of building transparency directly into model architectures rather than relying on post-hoc explanation methods.

A comprehensive survey on foundation agents, proposing a modular brain-inspired architecture and covering self-enhancement mechanisms, multi-agent collaboration, and AI safety.

This blog post provides a high-level survey of machine learning applications in healthcare, covering medical imaging, wearables, and molecular biology. It highlights how ML can shift medicine from curative to preventative and improve hospital workflows without replacing healthcare workers.

Anthropic has launched the Anthropic Economic Index Survey, a monthly initiative using Anthropic Interviewer to collect qualitative data from Claude users regarding AI's impact on their work, productivity, and future expectations.