Topic
Stanford
3 episodes
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Meta Harness: End to End Optimization of Model Harnesses
Meta-Harness automates harness engineering by using a coding agent to search over harness code, giving it full access to prior execution traces and scores via a filesystem rather than compressed summaries. On text classification, it improves 7.7 points over prior systems while using 4× fewer context tokens; on math reasoning, a single discovered harness improves IMO-level problems by 4.7 points; on TerminalBench-2, it ranks #1 for Claude Haiku 4.5 agents. The core insight is that harnesses operate over long horizons—a single retrieval or storage choice affects behavior many steps later—so rich, adaptive access to full diagnostic history beats compressed feedback.
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Stanford Researchers Introduce TRACE: A Capability Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
Stanford's TRACE system converts recurring agent failures into targeted synthetic training environments, using LoRA experts and MoE routing to close specific capability gaps without retraining the whole model.
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Stanford's DeLM cuts multi Agent costs 50%
Justy and Cody dissect Stanford's DeLM framework, which claims to halve multi‑agent costs by removing a central orchestrator. They weigh the technical merits, practical impact for product teams, and the hidden scheduling trade‑offs.