research survey
30 articles about research survey in AI news
IBM Research Survey Proposes Framework for Optimizing LLM Agent Workflows
IBM researchers published a comprehensive survey categorizing approaches to LLM agent workflow optimization along three dimensions: when structure is determined, which components get optimized, and what signals guide optimization.
Omar Saadoun's PaperWiki AI Agents Now Generate Personalized Research Surveys
Omar Saadoun announced that his PaperWiki platform now uses AI agents to generate personalized survey papers from a user's LLM-generated knowledge base. These surveys are self-improving and update automatically as new papers are published.
40-Author Survey Unveils 'Levels × Laws' Framework for Agent World Models
A 40-author survey introduces a 'levels × laws' framework for world models in AI agents, spanning 3 capability levels and 4 law regimes, synthesizing 400+ works. It provides a shared vocabulary for designing and evaluating world models across traditionally siloed research communities.
Anthropic Survey: 81,000 People Rank AI Economic Hopes & Fears
Anthropic published new research analyzing the economic hopes and worries expressed by 81,000 people in a prior survey on AI. The findings aim to guide AI development toward public priorities.
Survey Paper 'The Latent Space' Maps Evolution from Token Generation to Latent Computation in Language Models
Researchers have published a comprehensive survey charting the evolution of language model architectures from token-level autoregression to methods that perform computation in continuous latent spaces. This work provides a unified framework for understanding recent advances in reasoning, planning, and long-context modeling.
Pseudo Label NCF: A Novel Approach to Cold-Start Recommendation Using Survey Data and Dual Embeddings
New research introduces Pseudo Label NCF, a method that enhances Neural Collaborative Filtering for extreme data sparsity. It uses survey-derived 'pseudo labels' to create dual embedding spaces, improving ranking accuracy while revealing a trade-off between embedding separability and performance.
Graph Engineering Survey Proposes System-Level Intelligence
Survey proposes Graph Engineering using dynamic graphs to coordinate LLM agents, targeting System Intelligence beyond individual agents. No empirical benchmarks yet.
Survey: Embodied Manipulation Data Fits Five-Layer Pyramid
A new survey organizes embodied manipulation data into five layers — real-robot, UMI, egocentric, simulation, general — and analyzes how models combine them. The framework maps data quality against cost, highlighting UMI as a key bridge.
239-Paper Survey Maps How AI Agents Self-Improve via Scaffold Updates
A survey of 239 papers shows 68% of AI agent self-improvement methods focus on scaffold updates rather than model retraining, raising evaluation quality concerns.
100+ Papers Surveyed: LLMs' Metacognition Gap
A systematic survey of 100+ papers reveals gaps in LLM metacognition, including 10-30% miscalibration in top models like GPT-4 and Claude 3.
World Action Models Survey Unifies 100+ Methods Under One Taxonomy
A survey reviews 100+ world action models, unifying world models, video generation, and VLA policies under one taxonomy.
111-Page Survey Maps 5 AGI Levels: Responder to Ecosystem
111-page survey from US/China labs defines 5 AGI levels, argues epistemic exploration — not better answering — is key. Challenges scaling orthodoxy.
AI Memory Survey: Three Systems Needed for Human-Like Recall
A new survey paper proposes that modern AI requires three distinct memory systems—parametric, retrieval, and agent memory—to achieve human-like cognition, highlighting control as the key bottleneck.
AI Adoption Saves Average US Worker 2.5 Hours Weekly, New Survey Shows
A new survey finds the average American worker using AI reports saving 2.5 hours per week, a 6% time reduction. Early data suggests these time savings may be translating into broader productivity growth.
Survey Benchmarks Four Approaches to Synthetic Brain Signal Generation for BCI Data Scarcity
A comprehensive survey categorizes and benchmarks four methodological approaches to generating synthetic brain signals for BCIs, addressing data scarcity and privacy constraints. The authors provide an open-source codebase for comparing knowledge-based, feature-based, model-based, and translation-based generative algorithms.
Survey: 40% of Non-Managers Say AI Saves Them No Time at Work
A Guardian report highlights a growing divide: 92% of executives say AI makes them more productive, while 40% of non-managers report it saves them no time, creating a 'workslop' tax.
arXiv Survey Maps KV Cache Optimization Landscape: 5 Strategies for Million-Token LLM Inference
A comprehensive arXiv review categorizes five principal KV cache optimization techniques—eviction, compression, hybrid memory, novel attention, and combinations—to address the linear memory scaling bottleneck in long-context LLM inference. The analysis finds no single dominant solution, with optimal strategy depending on context length, hardware, and workload.
New CASIA Benchmark Exposes Fragmented Face Swapping Evaluation
CASIA researchers released a face swapping survey and benchmark on April 27, 2026, aiming to standardize evaluation across fragmented GAN and diffusion model methods.
Beyond Sequence Generation: The Emergence of Agentic Reinforcement Learning for LLMs
A new survey paper argues that LLM reinforcement learning must evolve beyond narrow sequence generation to embrace true agentic capabilities. The research introduces a comprehensive taxonomy for agentic RL, mapping environments, benchmarks, and frameworks shaping this emerging field.
90 Hours of Black Myth: Wukong Fuel New World Model Benchmark
A new survey and benchmark rethinks interactive world models as game engines, with a data engine collecting over 90 hours of Black Myth: Wukong gameplay.
Gallup: 50% of US Workers Now Use AI on the Job, Doubling Since 2023
A Gallup survey of nearly 24,000 US workers in Q1 2026 shows 50% now use AI at work, up from just 21% in 2023. This marks a critical mass for enterprise AI tools and signals a shift from experimentation to operational integration.
The Next Frontier for Self-Driving Cars: Teaching AI to Think Like a Human
A new survey argues that autonomous driving's biggest hurdle is no longer perception but a lack of robust reasoning. The integration of large language models offers a path forward but creates a critical tension between slow deliberation and split-second safety.
How Academics Are Using CLAUDE.md to Automate Research Code
A new presentation reveals how researchers use Claude Code's CLAUDE.md to automate literature reviews, data analysis, and paper writing workflows.
Frank AI Claims to Automate Customer Interviews at Scale, Cutting Research Time from 6 Weeks to 3 Days
Frank AI automates customer interviews via video, voice, or WhatsApp, generating insights overnight. The company claims this cuts research time from six weeks to three days and reduces costs versus traditional $500-$1,000 per interview.
Why Traditional Retail Metrics Break Down in Agentic Commerce
Valtech's 2026 research shows 96% of retailers face integration barriers, 48% are stuck in AI pilot purgatory, and nearly 75% can't link AI spend to metrics, as agentic commerce fragments customer journeys beyond traditional measurement frameworks.
64% of UK Consumers Want to Use Agentic AI for Shopping
Commerce and PayPal research shows 64% of UK consumers want agentic AI for shopping, with Gen Z and Millennials leading. This signals a readiness for autonomous AI assistants in retail, challenging brands to integrate agentic systems.
LLM-Based Customer Digital Twins Predict Preferences with 87.7% Accuracy
A new arXiv paper proposes using LLM-based 'customer digital twins' (CDTs) — agents built from individual Reddit review histories via RAG — to perform conjoint analysis. The CDTs predict actual user preferences with 87.73% accuracy in a computer monitor case study, offering a scalable alternative to traditional market research.
LLM Agents Will Reshape Personalization
Researchers propose that LLM-based assistants are reconfiguring how user representations are produced and exposed, requiring a shift toward inspectable, portable, and revisable user models across services. They identify five research fronts for the future of recommender systems.
Semantic Needles in Document Haystacks
Researchers developed a framework to test how LLMs score similarity between documents with subtle semantic changes. They found models exhibit positional bias, are sensitive to topical context, and produce unique scoring 'fingerprints'. This matters for any application relying on LLM-as-a-Judge for document comparison.
Study: People Rely on AI for Medical Advice, But Quality Evidence Lags
A new paper reveals people are frequently using AI for medical advice, but most research uses outdated models and lacks comparison to the non-AI information people would otherwise seek.