Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
Large Language Models (LLMs) are increasingly deployed in interactive systems where understanding user intent precisely is paramount. A key capability for such systems is effective question clarificat...
Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol, issuing one primitive action per LLM round. While this enables frequent replanning, it is...
Future 6G networks will rely on Large Language Model (LLM) agents to manage the Radio Access Network (RAN). However, current architectures assume inter-agent messages convey objective facts. A message...
Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promises to provide the right support at the right time,...
The article discusses how AI-native companies such as Basis, Clay, and Exa Labs employ AI agents to enhance various business processes, including onboarding, account management, and developer integrations. It highlights the practical applications of these AI-driven workflows for enterprise leaders, illustrating how they can be adopted in similar contexts.
Release: datasette-mcp 0.2 "rows" from execute_sql is now an array of objects. Previously it was an array of arrays. This should help weaker models avoid losing track of which positional arr...
Vision Language Models have demonstrated remarkable proficiency in interpreting static visual artifacts, but modern data analysis is inherently dynamic, requiring the active interrogation of interacti...
Sharing analog integrated circuit designs remains difficult: foundry non-disclosure agreements restrict the process details a design depends on, and the testbenches behind published results are rarely...
MutMem V1 introduced retention-preserving, cryptographically authorized mutation for persistent agent memory but did not provide a complete portable verification contract or clean-install reproduction...
Crystal generators and tool-using agents propose structures faster than density functional theory (DFT) energy and phonon calculations or experiments can assess them. Deciding which candidates merit e...
Despite the growing use of LLM-powered agents to solve multi-step tasks in complex environments, existing benchmarks rarely test state preservation, performance across languages, and application to re...
Scientific methodology figures are essential for communicating complex methods clearly, yet creating them remains labor-intensive and typically requires multiple rounds of refinement. Recent image-gen...
Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is excessively cooperative, so a system under test can s...
Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual agents. However, most existing metho...
Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain largely inherited from terrestrial federated learning...
Motus2 is a self‑evolving general world model designed for dexterous manipulation. It integrates a shared‑weight model that offers three control interfaces—a policy, a simulator, and an evaluator—forming a closed decision‑and‑learning loop for policy improvement. The system scales both model size and data, progressing from large‑scale monocular egocentric data to synchronized stereo data and robot‑domain adaptation, while also incorporating tactile feedback and a biomimetic platform with dual arms and hands.
Agent2UCB is a new agentic system designed for Generative Engine Optimization (GEO), which refines content to boost its likelihood of being cited or summarized by generative AI search engines. The system autonomously evaluates nine GEO strategies for each content item, selects the most effective one, and speeds up this selection using a bandit-based Agent2UCB policy that blends large language model priors with real-time reward signals. Additionally, it offers a lightweight, text-only SEO readiness check that assesses readability, topical coverage, and EEAT-style credibility, and experiments on GEO-Bench demonstrate consistent visibility gains while maintaining SEO quality.
The paper examines how artificial intelligence impacts freedom of expression, focusing on two technologies: social media recommendation algorithms and conversational AI chatbots. It argues that recommendation algorithms shape speech visibility in the digital public sphere, making them relevant to free‑speech philosophy, while conversational AI raises questions about granting speech rights to agents and users’ right to information. The work also explores corporate speech rights for platforms and developers, aiming to highlight unresolved issues from AI’s rapid growth.
PokaiTrainer is a competitive Pokémon VGC agent that scales belief‑state search to handle simultaneous, large joint action spaces and stochastic outcomes. The system uses PokaiEngine, a Rust battle engine that efficiently enumerates joint action outcomes with high accuracy, and adapts Student of Games to solve each decision as a Bayesian matrix game under a compute budget. In live Showdown play, the agent achieved a 59% win rate against a human field averaging ~1320 Elo, reaching an Elo band of 1350‑1400 and briefly entering the top 500 of the format.