Knowledge catalogue
arXiv:2606.11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge
arXiv:2606.10532v1 Announce Type: new Abstract: Memory is essential for enabling large language model (LLM) agents to handle long-horizon reasoning tasks. Existing memory mechanisms are largely centra
arXiv:2403.00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex
arXiv:2606.11007v1 Announce Type: cross Abstract: OpenClaw has rapidly emerged as a transformative artificial intelligence (AI) agent framework, and its ability to autonomously execute complex, multi-
arXiv:2606.09198v1 Announce Type: new Abstract: Deep Research agents powered by Large Language Models (LLMs) have exhibited extraordinary potential in automated paper writing tasks. However, existing
arXiv:2606.09563v1 Announce Type: new Abstract: As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This
DeepAgents is a framework or tool designed to enable the creation of intelligent agents, likely demonstrated or explained in a brief 90-second format by Harrison Chase, a co-founder of LangChain. The
Nous Research shared documentation about their Photon integration, providing users with guidance on implementing Photon messaging functionality within the Hermes Agent platform. The resource likely co
The Top AI Papers of the Week (May 31 - June 7) - LEAP - AutoLab - Learn From Your Own Latents - Reusable Context Engineering - Self-Revising Discovery Systems - Scaling Laws for Agent Harnesses - Dis
arXiv:2606.05720v1 Announce Type: cross Abstract: Large language models and AI coding agents have reshaped software development, but the path to fully AI-native systems faces structural challenges. Ch
arXiv:2604.03042v2 Announce Type: replace Abstract: Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveilla
arXiv:2606.04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks. Yet whether these systems can effectively engage in for
From idea to live store, in minutes. 🛍️ Tomorrow on the Friday Showcase: → Replit's new Shopify partnership, with @Davidizek . Tell Replit Agent what you want to sell and it builds your storefront, cr
arXiv:2606.04437v1 Announce Type: new Abstract: Collaborative perception extends the perceptual range of autonomous vehicles by sharing information across agents, but heterogeneous sensors and percept
arXiv:2606.04743v1 Announce Type: cross Abstract: Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface on
arXiv:2606.02618v1 Announce Type: cross Abstract: We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-
arXiv:2606.03118v1 Announce Type: cross Abstract: Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach t
arXiv:2606.01494v1 Announce Type: cross Abstract: Agent skills extend AI agents with reusable instructions, tools, scripts, references, and workflows, establishing a security boundary distinct from bo
arXiv:2606.00807v1 Announce Type: new Abstract: Traditional artificial intelligence has largely conceptualized intelligence as isolated computation occurring within bounded agents. Across classical AI
arXiv:2606.00017v1 Announce Type: new Abstract: Training language model agents for multi-agent strategic interaction presents a core difficulty: the quality of any action may depend on future events t
Enterprises are learning that the race to AI-native operations is not won by deploying the most tools or agents — it is won by the organizations that build the most trusted AI data foundations underne
arXiv:2502.15224v2 Announce Type: replace-cross Abstract: Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in nois
arXiv:2602.12386v2 Announce Type: replace-cross Abstract: Learning stationary policies in infinite-horizon general-sum Markov games (MGs) remains a fundamental open problem in Multi-Agent Reinforcemen
// Reusable Context Engineering // Context bloat quietly kills long-horizon runs, but you can fix it from the outside without fine-tuning the underlying agent. (bookmark this) Context management is us
slowly we're all realizing that tools should be called from code, not from within the llm api Introducing Search as Code, our new search architecture for AI agents. It writes Python that calls our sea
arXiv:2605.30454v1 Announce Type: cross Abstract: Tool-augmented LLM agents are vulnerable to prompt injection: a third party who controls part of the agent's context can plant instructions that the a
This entry likely covers Nous Research's documentation for Windows native integration or functionality within their Hermes agent system. The resource appears to be a user guide section explaining how
As artificial intelligence agents begin to proliferate across information technology infrastructure, IT leaders are moving away from asking, “How do we monitor every alert?” to “How do we design infra
Nous Research announced an update to Hermes, their AI agent framework, with details available in their user guide documentation covering tool-search features. The update likely enhances Hermes' capabi
arXiv:2602.06361v2 Announce Type: replace-cross Abstract: We introduce a problem of fairly allocating indivisible goods (items) in which the agents' valuations cannot be observed directly, but instead
arXiv:2605.29532v1 Announce Type: cross Abstract: Exploratory GUI testing is a particularly demanding setting for MLLM agents: without predefined test scripts, an agent must autonomously navigate an a
models underestimate how much work it takes (token usage) to accomplish a task, just like us 🧵 Claude-Opus-4.8 takes you too much tokens - but is this issue general across agents? Do agents know how m
arXiv:2605.28807v1 Announce Type: new Abstract: Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain mea
slide from The Redpoint InfraRed 100 is now live. These are the companies building the infrastructure that powers everything happening in AI right now, from world models and agent runtimes to the sand
arXiv:2605.28699v1 Announce Type: new Abstract: Large language models increasingly rely on either reinforcement learning or multi-agent prompting to improve reasoning, yet these two paradigms remain d
arXiv:2605.27582v1 Announce Type: cross Abstract: Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environme
arXiv:2605.26286v1 Announce Type: cross Abstract: Real-world multi-agent reinforcement learning (MARL) systems must often operate under stale observations, stochastic communication delays, and intermi
The Redpoint InfraRed 100 is now live. These are the companies building the infrastructure that powers everything happening in AI right now, from world models and agent runtimes to the sandboxes, data
arXiv:2602.12833v2 Announce Type: replace-cross Abstract: Longitudinal clinical reasoning over electronic health records requires tracking evolving physiological measurements, laboratory results, and
arXiv:2602.02843v3 Announce Type: replace Abstract: When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicativ
arXiv:2605.24248v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a h
arXiv:2605.25620v1 Announce Type: new Abstract: World models enable agents to predict future dynamics conditioned on actions, making the choice of latent representation central to planning and control
arXiv:2605.25389v1 Announce Type: cross Abstract: While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specializ
haha sweet, gbrain meets activegraph Gbrain is what an agent *knows* — a durable markdown/git knowledge substrate with hybrid search, typed links, facts, timelines, and a real ingestion pipeline. @Act
arXiv:2508.19113v3 Announce Type: replace Abstract: Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning w
arXiv:2605.24352v1 Announce Type: new Abstract: Multi-agent collaboration, especially in human-AI teaming, requires agents that can adapt to novel partners with diverse and dynamic behaviors. Conventi
arXiv:2605.23930v1 Announce Type: new Abstract: We introduce Quantum Frog, a two-player cooperative game built on a novel quantized-time mechanic in which the environment advances only when a player a
the importance of traces! Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, obser
arXiv:2605.24238v1 Announce Type: new Abstract: In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view
As enterprises confront spiraling cloud inference bills, on-prem AI computing is emerging as the decisive lever for making agentic AI economically viable at scale. Dell Technologies Inc.’s announcemen
arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai
arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer mode
Always enjoy chatting with @hwchase17 . Excited to sit down with him at our @traversal_ai HQ in a couple of weeks and discuss all things agents. im going to be in NYC in ~1 week, and am doing a firesi
Simon Willison announced an interactive demo for an AI agent built with Datasette, available at https://agent.datasette.io/. The demo allows users to experience hands-on functionality of the Datasette
arXiv:2510.01499v2 Announce Type: replace-cross Abstract: With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerg
Enterprise AI needs agentic transformation to turn scattered experiments into measurable business outcomes. The next phase of AI adoption is less about picking models and more about making enterprise
Stoked to work closely with @Vtrivedy10 on this. I've spoken about it before but an emerging trend i'm seeing with many of the ai-native companies I work with (shoutout @larsen_weigle_ ) is a focus on
the first step to building evals is often times figuring out what you want to eval (sounds simple, in practice it has a lot of nuance) you can condense long horizon evals with agents into smaller subs
arXiv:2605.18858v1 Announce Type: cross Abstract: Probabilistic prediction systems often aggregate probability estimates from multiple models into a single decision. A common assumption is that if eac
arXiv:2605.18727v1 Announce Type: cross Abstract: Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene,