Tooling1d ago
Cursor launches Computer Use: agents can control your computer and drive desktop apps like the iOS Simulator to test what they build
On 2026-10-23 (15:59 UTC) the official Cursor account @cursor_ai announced "Cursor can now control your computer": agents can drive desktop apps such as the iOS Simulator to test the features they build. A follow-up post says Computer Use lets agents click and type across any app on your computer, not just the browser. Cursor turns it on by itself when a task needs it, and you can also request it by @-mentioning Computer Use in your prompt; updating Cursor is enough to try it. A community changelog in September mentioned Computer Use on Mac in version 3.20.21, and this is the official launch. Supported platforms, the permission model and benchmarks were not published.
Agents14d ago
TestPrism: Nanjing University Exposes the Single-Reference Illusion in Coding Agent Test Generation, Slashing Pass Rates from 59.7% to 28%
Autonomous coding agents are increasingly leveraged to synthesize test suites for enterprise software. However, the standard practice of evaluating generated tests against a single reference solution overlooks alternative valid implementations and inflates perceived test quality. Researchers from Nanjing University and Hong Kong Polytechnic University unveil TestPrism (arXiv:2610.12289), exposing this systemic evaluation illusion. TestPrism comprises 300 test generation tasks sourced from 17 repositories and pairs them with 3,000 candidate implementations evenly divided into valid and invalid solutions. Its primary metric, the Joint Success Function, mandates that a test suite must simultaneously fail against buggy baselines, pass across all diverse valid implementations, and reject every invalid candidate. Evaluating fourteen frontier coding agent setups reveals that while models register a 59.67% success rate under legacy single-reference scoring, their actual Joint Success Function plunges to just 28.00%. To resolve these foundational flaws, the authors introduce TestHelix, an agentic synthesis harness leveraging peer cross-validation and recursive self-improvement to elevate Joint Success scores by up to 9.00 percentage points.
Research25d ago
GAGAR: Groupwise Agentic Grading and Advantage Redistribution Stabilizes Code Agent RL on 1.02T Models
Reinforcement learning for code agents typically relies on executable tests for binary rewards, leading algorithms like GRPO to assign identical scalar advantages to all passing rollouts regardless of code bloat, extraneous modifications, or architectural hygiene. Researchers from ByteDance, Peking University, and Tsinghua introduced GAGAR, a quality-aware advantage redistribution framework. By staging rollouts in a shared workspace where an SFT agentic grader ranks passing candidates and dynamically shifts credit toward clean solutions, GAGAR successfully stabilizes code agent RL on 310B Flash and 1.02T Pro models.
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