Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
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.
The paper introduces Test-Time Environment Decomposition (TTED), a label‑free learning method that allows large language model agents to break down complex web environment observations into simpler sub‑modules during inference. By learning from experience within these sub‑environments, agents can compose the gained knowledge to improve performance in the full environment. Experiments on synthetic and realistic benchmarks show that this approach enhances compositional generalization and boosts real‑world web automation tasks.
LOC I (Locator‑Critic) is a training‑free framework that separates visual search from evidence verification in Vision‑Language Models. It uses a Locator agent to propose candidate visual evidence and a Critic agent to assess its relevance, engaging in an iterative refinement loop that progressively improves the evidence until it is sufficient to answer a question. The approach yields state‑of‑the‑art results on several complex visual benchmarks, boosting accuracy for both open‑weight models like Qwen3‑VL and proprietary models such as Gemini 2.5 Pro.
The paper introduces T-MAP, a trajectory‑aware evolutionary search technique designed to red‑team large language model agents by exploiting vulnerabilities that arise during multi‑step tool execution. Unlike traditional methods that focus on harmful text, T‑MAP uses execution trajectories to generate adversarial prompts that bypass safety guardrails and achieve harmful objectives through actual tool interactions. Experiments across various Model Context Protocol environments show that T‑MAP outperforms baseline methods in attack realization rate and remains effective against advanced models such as GPT‑5.2, Gemini‑3‑Pro, Qwen3.5, and GLM‑5.
The paper introduces KVoiceBench, KOpenAudioBench, and KMMAU—three Korean speech benchmarks created through agent-driven frameworks that adapt existing SpokenQA and ASR resources into Korean SpokenQA and audio understanding tasks. These benchmarks total 12,345 samples and are publicly released to evaluate SpeechLMs beyond English. The authors benchmark eight recent SpeechLMs, revealing significant English‑Korean performance gaps and divergent rankings between SpokenQA and audio understanding, highlighting multilingual weaknesses not apparent in English-only tests.
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.