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Data Party THU
Data Party THU
Apr 25, 2026 · Artificial Intelligence

Google & Microsoft Harnesses: Core LLM Post‑Training Methods and 2025‑2026 Trends

These two recent papers—Microsoft’s M⋆, which evolves task‑specific memory harnesses, and Google’s AutoHarness, which automatically generates code‑level constraints—demonstrate reflective code evolution and tree‑search synthesis, achieving state‑of‑the‑art performance across diverse benchmarks and outlining LLM post‑training directions for 2025‑2026.

AgentAutoHarnessHarness
0 likes · 10 min read
Google & Microsoft Harnesses: Core LLM Post‑Training Methods and 2025‑2026 Trends
PaperAgent
PaperAgent
Apr 17, 2026 · Artificial Intelligence

How Automated Harnesses Are Revolutionizing LLM Agents: Memory and Action Constraints

This article reviews two recent papers that introduce automated harness methods—M⋆ for task‑specific memory programs and AutoHarness for code‑level action constraints—detailing their designs, reflective evolution processes, experimental evaluations across diverse benchmarks, and the broader shift toward harness‑centric LLM agent research.

AgentAutoHarnessLLM
0 likes · 10 min read
How Automated Harnesses Are Revolutionizing LLM Agents: Memory and Action Constraints
PaperAgent
PaperAgent
Apr 6, 2026 · Artificial Intelligence

Unlock AI Agents’ “Aha Moments” with AutoHarness – A Lightweight Governance Framework

This article introduces AutoHarness, an open‑source lightweight governance framework that gives AI agents their critical “aha moment” by handling context, tool governance, cost, observability, and session persistence, and provides a concise installation guide, code examples, and a six‑step pipeline architecture.

AutoHarnessGovernance FrameworkLLM
0 likes · 4 min read
Unlock AI Agents’ “Aha Moments” with AutoHarness – A Lightweight Governance Framework