Retrieved PHREEQC output lowers GPT agent accuracy on fixed simulations
GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points via a section index, while two Claude models show no detectable drop.
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GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points via a section index, while two Claude models show no detectable drop.
GPT-5.1 and GPT-5.2 lost 11.3 and 7.9 points reading reports via a section index; Claude models did not.
Authors report gains of 5.8% and 7.0% with token cuts of 51.9% and 22.9% on two benchmarks.

Microsoft Research shows long-running AI agents plateau in "idea basins"; a Fork-and-Flush context-reset method boosts scores 66% on equal compute.
RunningTab adds an environment-side task tab so LLM agents track unread files and unmet requirements.
Context Language Models edit their own context file, beating external managers on agent benchmarks while cutting FLOPs.
Empirical study of coding agent harnesses across 176 settings shows context management prevents overflow failures and bash-only interfaces cut costs for capable models.
Empirical study of 176 matched coding-agent harness configurations shows context management cuts overflow failures while planning and action-space choices trade accuracy against cost.
Tencent research finds agents lose control in 55-62% of trajectories when degraded control boundaries coincide with executable unsafe opportunities across five models and 16 domains.

Matt Pocock released the /wayfinder skill, an orchestrator layer that manages planning sessions, maps, and tickets for AFK coding agents.
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