Knowledge catalogue
arXiv:2606.31518v1 Announce Type: new Abstract: Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents,
arXiv:2606.11869v1 Announce Type: cross Abstract: Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry
Token costs are why there will be no saas apocalypse / good dev tools are cached intelligence for agents! The popular theory goes: agents can write code, so they'll just rebuild every tool from scratc
arXiv:2602.22480v2 Announce Type: replace-cross Abstract: An important emerging application of coding agents is agent optimization: the iterative improvement of a target agent through edit-execute-eva
Great paper on improving proactive agents. (bookmark it) Proactive agents act before you do. But how do you evaluate something that's supposed to anticipate needs you haven't expressed? This work intr
arXiv:2604.20279v1 Announce Type: cross Abstract: Mobile GUI agents can automate smartphone tasks by interacting directly with app interfaces, but how they should communicate with users during executi
arXiv:2604.19925v1 Announce Type: cross Abstract: AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused
Meet Kimi K2.6 Agent Swarm 👋 Highlights： 🔹 Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from 100 / 1,500 in K2.5). 🔹 Outputs are real files, not chat - one run delivers 100+ fi
arXiv:2511.03690v2 Announce Type: replace-cross Abstract: Agents are now used widely in the process of software development, but building production-ready software engineering agents is a complex task
arXiv:2510.21652v2 Announce Type: replace Abstract: AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and
arXiv:2604.13107v1 Announce Type: cross Abstract: As coding agents have seen rapid capability and adoption gains, users are applying them to general tasks beyond software engineering. In this post, we
arXiv:2604.09746v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent environmen
No agent maintained moral reasoning consistency across scenarios. Findings from a structured study with 11 agents on classic ethical dilemmas [R]
Most people building AI agents don't realize this until it's too late. Your agent's memory isn't a feature. It's your moat. 1. Closed harness = they own your memory 2. Switch models → lose all context
LangChains create-agent is a super minimal agent sdk If you want a more batteries included - that’s deepagents Middleware lets you extend both of them and customize behavior in a more advanced way @hw
arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent e
arXiv:2610.07675v1 Announce Type: new Abstract: AI agents are entering production in increasingly consequential environments without a shared semantic standard for what their evaluations actually mean
arXiv:2610.07972v1 Announce Type: new Abstract: Mobile GUI agents increasingly operate on interfaces influenced by users' histories and preferences, but their reliability across different users remain
arXiv:2609.15779v1 Announce Type: new Abstract: Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a ch
arXiv:2609.15309v1 Announce Type: new Abstract: Large language model (LLM) agents allocate test-time compute adaptively as they revise solutions, use tools, explore alternatives, and decide when to st
As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operat
arXiv:2608.21156v1 Announce Type: cross Abstract: LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms includin
arXiv:2608.12358v1 Announce Type: cross Abstract: Product and engineering teams building role-bearing AI agents face an evaluation gap: an agent can produce accurate, safe, and fluent content while st
More organizations are using natural language to query data instead of writing manual SQL. But moving an AI agent from a prototype to a production-ready tool requires rigorous, repeatable testing. Pri
Today we are launching an agent arena for RL environments: The Open Env Arena. Your agents can work to define the best RL environments; the platform trains Qwen-3.8-27B on them, we evaluate the models
Banger paper from Meta AI on research agents that decide what to investigate next. (bookmark it) If you run long-horizon research agents, choosing the next investigation is hard to learn, because thos
arXiv:2609.23953v1 Announce Type: new Abstract: AI agents that carry a multi-step computer task through on their own became ordinary tools in the past year, and the same autonomy is available to anyon
Interesting work on long-horizon research agents. PrimeScientists can decide where a research agent spends its budget. Achieves 10.3% more reward with 50.6% fewer research attempts, under the same bud
arXiv:2601.12538v2 Announce Type: replace-cross Abstract: Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) de
arXiv:2609.18857v1 Announce Type: cross Abstract: The O-RAN control plane is becoming agentic: autonomous AI agents, deployed as rApps by different vendors, independently close control loops over shar
arXiv:2609.18283v1 Announce Type: new Abstract: As telecommunication networks evolve toward autonomous 5G-Advanced and 6G operations, agentic artificial intelligence (AI) workflows, where large langua
arXiv:2609.18779v1 Announce Type: new Abstract: Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond t
arXiv:2609.11709v1 Announce Type: new Abstract: When multiple LLM agents yield conflicting answers, the decision-making process dictates whether agent diversity improves performance or merely compound
arXiv:2609.09233v1 Announce Type: cross Abstract: How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused o
Investigating how one AI agent works might not be enough, since there are emerging properties when these agents collaborate as tribes Thinking about this for years. I believe we're at the point of no
arXiv:2608.31057v1 Announce Type: new Abstract: Agent working memory is heterogeneous. Objects such as instructions, artifacts, tool outputs, and agent-generated state play different semantic roles an
Important read if you build with agent skills. Shared skill libraries are treated as a safe way for coding agents to reuse each other's work. New research shows they propagate malware. EvoMal plants a
This is one of the most effective ways to improve your agentic workflows. If you are building computer-use agents, this one is worth your time. Task Model Induction takes a raw recording of someone wo
arXiv:2608.16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their
arXiv:2608.11888v1 Announce Type: new Abstract: Agent skills are the de facto mechanism for extending LLM agents with reusable guidance. A skill can shape the agent's task execution, including plannin
Allie K. Miller announced a free live AI‑agent workshop featuring Mark Cuban set for August 11 2026, targeting up to 20,000 participants. She will demonstrate how her own AI workforce of 34 agents wor
You don’t need new ways to talk to your agents, you need new ways for your agents to talk to you 🫵 (do you?) Introducing Remoko: your mobile agent relay http://remoko.app I wanted a way for my long-ru
Agents want to collaborate. So we’re putting that instinct to work on improving open-weight LLMs at formal math. Our latest agent collab tackles the @SAIRfoundation challenge of building a cheat sheet
arXiv:2607.23586v1 Announce Type: new Abstract: Long-lived AI agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work, and
arXiv:2607.10526v2 Announce Type: replace Abstract: Stateful personal agents increasingly maintain long-term user profiles, episodic memories, and reusable skills. This persistence turns conversationa
@LangChain always reveals the technical principles behind agent concepts, like loop engineering, managed agents and memories.👍 That's why I recommend LC docs to agent developers and users around me, t
arXiv:2605.28787v1 Announce Type: cross Abstract: In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata like schema.
Managed deep agents is the easiest way to build and deploy long horizon agents Private preview, dm me if you want access Managed Deep Agents is built for agents that need to work over long time horizo
arXiv:2605.22905v1 Announce Type: new Abstract: Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that gene
arXiv:2605.03213v1 Announce Type: cross Abstract: Agentic AI systems, specifically LLM-driven agents that plan, invoke tools, maintain persistent memory, and delegate tasks to peer agents via protocol
General Intelligence built their agent platform using AI agents themselves, leveraging Vercel's infrastructure and deployment capabilities. The approach demonstrates how autonomous agents can be used
⚠️ AI agents are wildly premature technology that is being rolled out way too fast. The deepest lesson about the vibe coded AI agent disaster story that is running around is NOT about losing your data
arXiv:2604.22452v1 Announce Type: new Abstract: Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language m
arXiv:2603.15421v2 Announce Type: replace Abstract: Large language model agents heavily rely on external memory to support knowledge reuse and complex reasoning tasks. Yet most memory systems store ex
arXiv:2610.13105v1 Announce Type: new Abstract: Failure attribution for LLM agents aims to identify decisive steps responsible for task failure, which is critical for debugging and improving agentic s
AI agent security now depends on knowing what agents can do after they gain access. Agents can act across applications, pass work to other agents and make changes faster than security teams can review
Zack Korman tweeted that rogue AI agents could pose a severe safety threat—“super scary” and potentially lethal—even to entire families. He clarified that the agent‑trace logs from the recent Hugging
What a super interesting paper this one is. They propose an architecture for agents that outlive their model, harness and host. Today we describe an agent by whatever model and harness it happens to r
arXiv:2609.00065v1 Announce Type: cross Abstract: A language-model agent asked to analyse an experiment will usually return working code. Whether the analysis is defensible is a different question. A
arXiv:2607.25090v1 Announce Type: new Abstract: Machine learning engineering (MLE) tasks require long-horizon decision making over iterative solution debugging and refinement, under expensive and feed