Model Detail
agent-jev
—agent-jev is an AI model with 0.6B parameters released by aimeigaoshou. Distributed under the permissive apache-2.0 license.
agent-jev ships with 0.6B parameters. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
agent-jev is best fit for general-purpose AI workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
India becomes the latest country to report rogue AI agent activity
The agents got in through credentials and connection settings left exposed in app configurations, public code repositories, and overly permissive cloud roles.
HarnessMDP: Learning to Control LLM Agent Harnesses with Offline Reinforcement Learning
arXiv:2607.05458v3 Announce Type: replace-cross Abstract: The execution harness is a critical component of LLM agents, shaping how the agent interacts with its environment and organizes multi-step execution. Prior work has shown that improving tools, workflows, and other harness components can subst
PRISM: PRIvileged Sibling-guided Multi-Agent Topology Design
arXiv:2610.20628v1 Announce Type: new Abstract: Multi-agent systems (MAS) based on large language models (LLMs) have become a powerful way to address complex problems, and their effectiveness depends critically on the collaboration topology that decides which agents participate and how they communic
ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews
arXiv:2610.21042v1 Announce Type: cross Abstract: Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tools based on large language models (LLMs) can search and summarize the literature but do not record wh
Beyond Downstream QA: Rethinking How We Evaluate Agent Memory
arXiv:2610.22209v1 Announce Type: cross Abstract: Agent memory benchmarks evaluate whether an agent's responses reflect correct knowledge, measuring downstream question-answering accuracy as a proxy for memory quality. We show this evaluation paradigm is incomplete: it conflates memory correctness w
A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
arXiv:2610.20809v1 Announce Type: new Abstract: A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly ri