Northwestern University

Headlines · 2

1. 15h
   Google Research open-sources Harness² agent framework that rewrites its own harness

   Google Research has open-sourced Harness² (Recursive Agent Harnessing for an Open World) on GitHub under Apache 2.0, supporting the OpenCode and Codex executors and defaulting to OpenCode. The framework leaves model weights untouched and instead rewrites eight editable components — system prompt, guardrails, memory, tool guidance, subagents, skills, scripts and plugins — as files: task-level recursion uses a propose-probe-compose loop that compares execution traces of candidate harnesses on the same task without needing ground-truth answers, while harnessing-level recursion saves accepted changes and experience notes in an improvement library for later tasks. In the authors' own benchmarks, GLM-5 on OpenCode rose from 64.2 to 75.7 on WorkBuddy-Bench, partial pass rates on the legal LAB benchmark improved by up to 11.8 points, and JobBench gained up to 4.5 points. Google Research stresses that Harness² remains a research project rather than an officially supported product, that each recursion round costs extra candidate agent runs, and that network access is unrestricted by default while the Codex executor disables the sandbox.

   r/LocalLLaMA · 🔥 68
2. 6d
   Princeton's Arvind Narayanan warns AI chatbots may become 'truth oracles' for news

   Arvind Narayanan, a Princeton computer scientist and co-author of “AI Snake Oil,” warned in a keynote at Northwestern University's McCormick Foundation Center computing-and-journalism symposium that growing trust in AI chatbots could turn them into “truth oracles,” pushing journalism toward a turning point it has not seen in 200 years. He said that once chatbots become the default entry point for checking facts, news organizations will lose one of their most basic functions — answering “what is true” — leaving them the parts machines handle poorly, such as contested issues, narrative, storytelling and clearly stated context; he described the shift as a “thin burger with thick bread,” with less execution and more judgment. Narayanan also argued that reporting, verification, analysis and distribution have been broken apart, so one- or two-person teams can now produce high-quality journalism, and called for journalists to take part in decisions about how AI platforms tune their algorithms, and even for taxes on AI and social media companies to counter the power imbalance with publishers.

   科技新报 · 🔥 0

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