AI coding agents can tend to isolate research, running experiments and generating ideas that are then forgotten when context windows reset. This can waste tokens, as models then repeat the same mistakes and hit the same dead ends.
But new research argues that it’s not the model itself, but the overarching ‘tree,’ that needs tweaking. To that end, data scientists from the Gaoling School of Artificial Intelligence, Renmin University of China, and Microsoft Research have introduced Arbor, a “persistent hypothesis tree” that helps agents remember and refine learnings over long research sessions.
A long-lived coordinator manages research strategy across the tree, while short-lived executors spin up isolated worktrees to test different hypotheses. As results come back, the tree updates, narrowing and refining throughout experimentation.
In practical tests, this technique delivered more than two-fold performance gains over standard AI coding agents across real-world engineering tasks, for the
AI coding agents are making it easier than ever to produce software. Ensuring that software is secure before deployment is another matter — one that AWS thinks AI should help with too.
As enterprises adopt agentic development workflows, the volume of first-party code being created and modified is rising rapidly. Yet the process of validating vulnerabilities, determining whether they are exploitable, and fixing them often still depends on developers and security teams working through findings manually.
AWS is aiming to address that imbalance with Continuum, a new service designed to continuously discover, investigate, and remediate vulnerabilities in enterprise environments, whether the code is their own or from third parties.
Rather than simply generating alerts, the service is intended to help enterprises move findings through the entire remediation lifecycle, AWS VP of Security and Observability Chet Kapoor wrote in a blog post.
For first-party applications, Continuum can analyze cod
AI coding agents are becoming critical to software development, but the configuration files that guide them, such as Agents.md or Claude.md, can be “smelly.”
That means they can contain structural flaws, redundancies, or counterproductive workflows that bloat context, waste tokens, and make coding agents less reliable.
Researchers from the Department of Computer Science at Brazil’s Federal University of Minas Gerais hope to shed light on this problem, presenting what they call the “first catalog of smells” for coding agent configuration files. The most odorous? Lint and skill leakage, context bloat, and conflicting instructions.
“Our results show that these smells are widespread in practice,” the researchers wrote. Consequently, they “may directly influence how coding agents interpret project conventions, prioritize instructions, and perform development tasks.”
Smelly configs in the harness make models misbehave
Agents like Claude Code, Codex, Cursor, and Gemini are increasingly taking
Arbor's superior performance in AI optimization could accelerate advancements in machine learning, influencing future AI development strategies.
The post Arbor framework outperforms Claude Code and Codex by 2.5x in AI optimization benchmarks appeared first on Crypto Briefing.
Nvidia's ENPIRE hands an entire robot fleet to coding agents like Codex and Claude Code, letting them write training code, test it on real hardware, and improve without a human watching.
Software development has changed. Engineers no longer type most code by hand. They describe intent, and AI agents do the work. Modern tools plan tasks, edit across files, run tests, and open pull requests. Many now ship to production with limited supervision. No single tool fits every need. This guide covers the AI coding agents […]
The post Top AI Coding Agents and Development Platforms in 2026: Atoms, Devin, Windsurf, Cursor, Warp, and More Compared appeared first on MarkTechPost.
The post Cognition CEO Scott Wu: AI Coding Agents Are Here To Help, Not Replace Human Programmers appeared on BitcoinEthereumNews.com.
Cognition CEO Scott Wu: AI Coding Agents Are Here To Help, Not Replace Human Programmers Skip to content
Home AI News Cognition CEO Scott Wu: AI coding agents are here to help, not replace human programmers
Source: https://bitcoinworld.co.in/cognition-ceo-scott-wu-ai-coding-agents-not-replace-humans/
