survey

MIT Technology Review interviews kids aged 10-18 about their attitudes toward AI, finding most use it for innocuous tasks like search and schoolwork, while expressing nuanced concerns about creativity and societal impact.

A report based on a survey of 300 data and technology executives examines how legacy data systems limit the effectiveness and scaling of AI agents in enterprises, highlighting that 'data leaders' who give agents broader data access experience greater trust and success.

A technical survey on position encoding methods in Transformers, covering absolute and relative methods, RoPE, and long-context scaling techniques like Position Interpolation, NTK-aware scaling, YaRN, and LongRoPE.

A technical survey of Mixture-of-Experts architectures in LLMs, organizing evolution along expert granularity, topology, routing, load balancing, and execution, and proposing complementary views of architectural milestones and control planes.

A survey paper systematically analyzing the evolving safety landscape of multi-modal large language models, covering emerging threats such as adversarial attacks, data poisoning, jailbreaks, and hallucinations, and reviewing updated safety strategies.

This arXiv survey (1,547 papers, 2024-2026) systematically maps the field of long-horizon LLM agents, disambiguating long-horizon, long-context, and long-term memory, and organizing research into six lifecycle categories while identifying the core 'horizon gap' and open measurement problems.

A survey paper proposing a three-stage taxonomy of co-evolution in agentic systems, covering agent-agent, agent-environment, and meta co-evolution to enable open-ended improvement beyond fixed human-designed paths.

The Sloan Digital Sky Survey announces Data Release 20, featuring the Black Hole Mapper's first southern hemisphere optical observations and coordinated eROSITA X-ray identification, mapping over half a million supermassive black holes across the sky.

This paper surveys clinical communication processing using LLM-generated synthetic data and presents 13 case studies across EMS reports, nurse handoffs, and more, showing that synthetic data can bootstrap clinical NLP systems.

A tweet highlights a CS survey paper arguing that bigger context windows are a dead end, and that memory engineering — treating agent memory as an operating system — is what separates real AI agents from autocomplete, enabling stateless models to self-evolve.

This paper surveys the evolution of continual learning from parameter-centric methods to system-level adaptation, proposing a tri-axial framework (When, How, Where) to characterize learning across pre-training, post-training, and inference stages.

A survey paper introducing a functional role taxonomy for language grounding in embodied agents, distinguishing five roles and auditing evidence to assess whether language's contribution is genuinely supported.

A survey of 'adversarial attacks for good', examining proactive protections applied across the visual content lifecycle to disrupt unauthorized AI automation and support accountability, covering five research communities such as privacy filters, unlearnable examples, generative safeguards, adversarial CAPTCHAs, and provenance mechanisms.

This paper surveys self-evolving coding agents, which improve their future behavior by updating frameworks, memory, skills, tools, or models from prior coding interactions, and presents a taxonomy of what evolves, when, and what software-specific evidence drives it.

This survey synthesizes 257 papers on validating agentic AI systems, proposing a five-dimension taxonomy covering behavioral, safety, temporal, regulatory, and multi-agent concerns. It identifies gaps in temporal validity, runtime evidence maintenance, regulatory legibility, and open-ended multi-agent assurance, arguing that trustworthy deployment requires validating trajectories in context.

A survey paper organizing robot-learning techniques along an axis of frozen-weight policies (VLA models) versus agents that write their own executable skills as code, providing a taxonomy of self-improvement mechanisms and analyzing the emerging robot-skill economy.

This paper proposes an agent-centric paradigm for world modeling, introducing 'Agent-Centric Interactive World Proxies' and organizing them into six functional forms across three progressive levels of agent improvement, offering a roadmap for building world proxies that empower agents to plan, learn, and evolve.

A Sumsub survey finds that Greater China consumers are adopting AI agents faster than they understand them, with trust, verification, and fraud safeguards posing key challenges for lasting adoption.

Semrush surveyed 481 marketers and found that while 85% say AI has changed their search strategy, only 22% have fully integrated AI search with SEO execution — and those teams see significantly more traffic or leads from AI platforms.

This survey reviews neural architecture search (NAS) methods applied to traffic prediction, organizing them by search strategy (gradient-based, evolutionary, one-shot weight-sharing) and discussing challenges such as computational scalability, cross-city generalization, and future directions.