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Description

DeepSWE is an innovative and fully open-source coding agent that utilizes the Qwen3-32B foundation model, trained solely through reinforcement learning (RL) without any supervised fine-tuning or reliance on proprietary model distillation. Created with rLLM, which is Agentica’s open-source RL framework for language-based agents, DeepSWE operates as a functional agent within a simulated development environment facilitated by the R2E-Gym framework. This allows it to leverage a variety of tools, including a file editor, search capabilities, shell execution, and submission features, enabling the agent to efficiently navigate codebases, modify multiple files, compile code, run tests, and iteratively create patches or complete complex engineering tasks. Beyond simple code generation, DeepSWE showcases advanced emergent behaviors; when faced with bugs or new feature requests, it thoughtfully reasons through edge cases, searches for existing tests within the codebase, suggests patches, develops additional tests to prevent regressions, and adapts its cognitive approach based on the task at hand. This flexibility and capability make DeepSWE a powerful tool in the realm of software development.

Description

GPT-6 Cyber is a specialized artificial intelligence model developed by OpenAI for advanced cybersecurity research, vulnerability discovery, and authorized offensive security testing. The model combines frontier reasoning capabilities with cybersecurity-specific training to identify software weaknesses, investigate attack paths, and validate exploitable vulnerabilities. Its autonomous research capabilities allow security professionals to analyze complex codebases, reproduce known vulnerabilities, discover previously unknown flaws, and develop working proof-of-concept exploits. GPT-6 Cyber also assists with secure code reviews, vulnerability triage, patch generation, and verification of security fixes. During OpenAI's security research, the model helped identify 81 previously unknown high-severity vulnerabilities in Firefox and develop working exploits for 948 Linux kernel vulnerabilities. Its ExploitGym benchmark score of 48.5% represents a substantial improvement over GPT-5.6 Cyber's 32.1% result. Specialized alignment training helps the model follow rules of engagement, respect authorized testing boundaries, and avoid accessing unrelated systems during security investigations. Additional safeguards include cyber-specific activity monitoring, sandboxing recommendations, access restrictions, and controls designed to prevent harmful use against critical infrastructure. GPT-6 Cyber is available to selected approved customers through OpenAI Daybreak Red, with API and Codex access supporting advanced defensive research, vulnerability validation, and security testing workflows.