The paper introduces a method for continual enterprise world model discovery, enabling an agent to learn and adapt to business rules in dynamic systems without prior knowledge. Using a ServiceNow environment called EnterpriseWorldShift, the authors evaluate their Continual Discovery Agent (CDA) across four rule-modification scenarios—discovery, revision, extension, and retirement—showing that CDA predicts rule effects more accurately than lookup-based approaches, improving IoU by up to 8.98 points. The agent can answer queries from its internal model without querying the live system.
arXiv:2608. 03764v1 Announce Type: new Abstract: Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively.
arXiv:2607. 07052v1 Announce Type: cross Abstract: AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems.
arXiv:2608. 01042v1 Announce Type: cross Abstract: Enterprise AI agents act across many apps whose data changes continuously, so an answer is correct only relative to what data existed and who could see it at the moment it was asked.
Large language model (LLM) agents are increasingly deployed as personal assistants. Existing evaluations, however, mostly use short, self-contained requests in static environments.
arXiv:2607. 07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wait for a human query before acting.
arXiv:2608. 10875v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as personal assistants.
arXiv:2608. 15138v1 Announce Type: new Abstract: Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs.
arXiv:2606. 30306v1 Announce Type: cross Abstract: Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions.
The Era by Eon benchmark tests enterprise agents by presenting questions that specify answer rules and require code to compute answers from a company’s data. In the original benchmark, top models answered 22–25 of 27 questions, barely distinguishing performance. The updated benchmark adds eight templates that rely on hidden facts not explicitly stated in any question or document, forcing agents to infer information from indirect data. Twelve agents were evaluated, with the best achieving 18 of 24 correct answers, while the hardest questions—requiring selection among similar records—were answered correctly only 1 out of 84 attempts across all agents.
The "Era by Eon" benchmark tests enterprise agents by presenting questions that specify answer rules and require code to compute answers from generated company data. While top models can answer most questions, the benchmark introduces eight new templates that rely on hidden facts not explicitly stated in any document, making the task harder. Evaluation of 12 agents shows that only the best agent correctly answers 18 of 24 attempts, with many questions remaining largely unsolved.
arXiv:2607. 20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score the final answer or a fixed "ground-truth" list of tools, both of which are fragile once the underlying data is live and stateful.
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history.