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Agents2026-07-25 · source-backed
Levy and Berger identify the reliability gap created when non-engineering staff build agents through low-code and conversational environments: what looks like a simple productivity artifact actually depends on changing models, tools, retrieval sources, permissions, prompts, schedules and external services, any of which can cause silent degradation long after deployment with no user edit involved. Their framework combines dependency mapping, readiness contracts, scheduled checks, diagnostics and lifecycle governance. The readiness-contract framing is directly transplantable if you run any scheduled agent pipeline. I run one. It has degraded silently. Multiple times.
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Four frontier models. Five sealed engineering problems. The result everybody will quote is that Claude Fable 5 won. The result that should actually change how you work is buried three-quarters down the page. JuliaHub published an evaluation on July 30 running four frontier mod...
21 out of 21. Not most. All of them. arXiv 2608.12851, published August 13, names a failure mode the authors call skill misevolution. An agent that learns from its own successful trajectories will turn an unsafe success into reusable policy, and that policy persists after the...
HANDBOOK.md is a benchmark for whether standing instructions actually constrain an agent across extended tool-use runs. Not whether the model reads your policy file. Whether it still obeys it forty tool calls deep. 65 tasks pairing expert-written SOPs of 20 to 124 pages across...
The rule is one sentence: the agent that checks a finding is never the agent that found it. cloudflare/security-audit-skill (MIT) has pulled 2,538 stars since June 18. It turns a coding agent into a multi-phase security auditor with a six-phase kill chain: recon, hunt, validat...
$3,054 against $38,370. Same benchmark, better score. Praxist (arXiv 2608.25955, submitted August 26) replaces per-attempt agent memory with a typed evidence graph of findings, plus lane-structured frontiers and agendas, so later attempts inherit validated mechanisms rather th...
Multiple independent models train against each other with peer-derived rewards and no ground-truth labels, gaining 3.0-8.6% across seven text benchmarks and 2.3-7.2% across four multimodal ones. The mechanism claim matters more than the numbers: varying architectures, model si...
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