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TOPIC · ENTITY #17143

job-shop scheduling

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

RAC: Relational Agent Contract for constraint-governed engineering tasks (arXiv:2610.20935v1)

The paper introduces RAC, a harness-agnostic action/feedback contract for engineering agents that separates semantic decisions from mechanically derived realizations by specifying authoritative observations, semantic actions, tool-based realization, independent verification, and repair evidence. Evaluations show RAC improves reliability on constrained engineering tasks—achieving a 90.9% average clean-pass rate on PCB placement, routing, and repair (versus 74.0% for a requirements-only delegation baseline), 96.3% quality pass on job-shop scheduling (versus 59.6% for code-mediated delivery), and gains on 141 embedded-code tasks across four microcontrollers.

7.0

RESEARCH · 1 SOURCE · The Decoder

Microsoft Research's 'Fork-and-Flush' method reduces idea basins and improves long-running AI agents

Microsoft Research shows that growing chat context can trap autonomous 'autoresearch' agents in suboptimal 'idea basins' during long-running tasks. Their Fork-and-Flush approach forks agents into parallel branches that inherit workspaces but flush chat history; across 13 tasks (using GPT-5.6-terra and GPT-5.5 via GitHub Copilot CLI) it raised the min-max-normalized average score to 0.78 versus 0.47 for single runs, improving consistency as well.

7.0