Search: “agents”

arXiv:2604.10800v1 Announce Type: cross Abstract: Learned classifiers deployed in agentic pipelines face a fundamental reliability problem: predictions are probabilistic inferences, not verified concl

arXiv:2509.16606v5 Announce Type: replace-cross Abstract: In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained comm

'DOOMSCROLL' is a creative work or project titled after the term for compulsive, anxiety-inducing social media consumption, produced by Hermes Agent, an AI agent associated with Nous Research's Hermes

Import AI issue 453 covers research and developments around vulnerabilities in AI agent systems, including methods for breaking or adversarially manipulating AI agents. The issue also features MirrorC

arXiv:2604.08970v1 Announce Type: cross Abstract: We study predictive multilingual evaluation: estimating how well a model will perform on a task in a target language when direct benchmark results are

arXiv:2512.21794v3 Announce Type: replace-cross Abstract: We study a sequential mechanism design problem in which a principal seeks to elicit truthful reports from multiple rational agents while start

Harrison Chase, co-founder of LangChain, shared a post emphasizing the critical importance of the 'harness' in AI agent systems, arguing that the harness essentially functions as the agent's memory an

@hwchase17 is right, memory creates lock-in. But not just conversations. Tool registries, hooks, agent conventions -> that's harness memory too. In my setup I define agent tools once in a shared regis

@hwchase17 is spot on: Agent harnesses are the real foundation now — and they're fused with memory. Give a closed/proprietary harness control of your agent's memory (context + personalization) and you

Hot take: I can't see any startup building their critical core operations on Claude Managed Agents or any proprietary harness as investable. The past weeks have shown why it's critical to build on top

i agree there should be managed agents, i just think they should be built on open harnesses and open memory standards @hwchase17 Good article! I half agree :) the problem is that as agents become used

I told my girlfriend I was talking with my Hermes agent on Whatsapp. She got really happy and a bit confused and asked me since when I have a Hermes agent? I told her since this morning, at which poin

Relying on model providers' stateful APIs or harnesses creates lock-in: switching models means losing your agent's memory -- a cost that only grows as agents get better at learning a big part of agent

arXiv:2604.06296v1 Announce Type: cross Abstract: AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has pr

As agent systems scale, the control layer becomes the main source of complexity. What starts simple turns into coordination and composition problems. I built langchain-middleware-stack to better struc

arXiv:2604.07799v1 Announce Type: new Abstract: Embodied agents are expected to operate persistently in dynamic physical environments, continuously acquiring new capabilities over time. Existing appro

arXiv:2604.06392v1 Announce Type: new Abstract: We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or sing

arXiv:2604.07595v1 Announce Type: cross Abstract: Language model agents reason from scratch on every query: each time an agent retrieves evidence and deliberates, the chain of thought is discarded and

The search results did not return any information about the specific tweet or the 'II-Agent' and 'II-Commons' products mentioned in the URL. The X (Twitter) post is not publicly indexed or accessib...

Proud to power @NousResearch's Hermes Agent with MiniMax M2.7, and excited for what we're building together. Try MiniMax M2.7 in Hermes Agent today → https://portal.nousresearch.com #MiniMax #NousRese

As agentic AI infrastructure moves from experimentation into production, enterprises are confronting a more complex question than which model to use: how to control the cost, data exposure and infrast

arXiv:2602.02035v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning systems deployed in real-world robotics applications face severe communication constraints that significant

arXiv:2608.10530v1 Announce Type: cross Abstract: Large Language Models (LLMs) have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool

arXiv:2608.10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained re

arXiv:2608.06811v1 Announce Type: cross Abstract: Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration

nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months

OpenAI gave a last-minute presentation at the Black Hat security on Wednesday about 'the Hugging Face Incident' (previously on this blog). The video was published yesterday. It's short and information

arXiv:2608.02650v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools to complete complex real-world tasks. However, reliable tool-use planning remains

People continue to turn to outside AI education resources. Companies just aren't providing enough. I'm running a free AI Agent Workshop in a week with @mcuban for absolute beginners. 10,000+ people ha

Prime Agent is an open-source coding and research agent for general and long-running work. A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficien

arXiv:2608.03214v1 Announce Type: new Abstract: Large language models have transformed artificial intelligence from isolated prediction services into components of long-running, distributed systems th

arXiv:2608.02878v1 Announce Type: new Abstract: Large language models have shown promise for automated Verilog RTL generation, yet state-of-the-art multi-agent systems plateau at ~95% accuracy on stan

arXiv:2608.00339v1 Announce Type: cross Abstract: AI agents increasingly perform open-ended tasks in settings where their conclusions can guide consequential decisions. We provide evidence that AI age

arXiv:2608.01678v1 Announce Type: cross Abstract: Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence

arXiv:2608.02508v1 Announce Type: cross Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the inte

arXiv:2608.02358v1 Announce Type: new Abstract: To operate robustly in open-world environments, autonomous agents should be able to infer the behavior of unfamiliar systems through interaction alone,

arXiv:2608.02499v1 Announce Type: cross Abstract: Real-world software development requires coding agents to operate in shared workspaces where users may inspect and modify code during an ongoing task,

arXiv:2608.01543v1 Announce Type: cross Abstract: Interaction between users and LLM agents is increasingly multimodal: conversations interleave text with images, and a later question may target either

arXiv:2607.29422v1 Announce Type: cross Abstract: Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic

Formula 1® partnered with AWS to build the Data Accelerator, using agentic AI on Amazon Bedrock AgentCore to transform its MarTech data platform. Learn how F1 cut data source onboarding from up to 8 w

arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines

At Google, AI is a soup-to-nuts endeavor. Obviously, we make leading AI models like Gemini and Nano Banana. We incorporate AI into the tools you use every day (think Gmail, BigQuery, AlloyDB, Google C

NVIDIA’s AI Red Team found common failure modes in enterprise AI agents: weak access controls, unrestricted code execution via tools, unprotected network egress, and exposure of plaintext secrets. The

arXiv:2607.26598v1 Announce Type: cross Abstract: Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tas

arXiv:2606.13317v2 Announce Type: replace Abstract: Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents. However, current pipelines typically d

arXiv:2607.25082v1 Announce Type: new Abstract: Open agent systems (OASYS) are increasingly prevalent in real-world domains where the sets of agents and tasks change unpredictably over time. Such open