Sep 10, 2026 · Artificial Intelligence

Agent Cost Optimization: Cut Waste, Not Intelligence

This article reveals three major sources of waste in AI agent workflows — redundant context recomputation, outdated prompt patterns, and misallocated reasoning effort — and provides a systematic optimization framework with caching strategies, prompt auditing, effort calibration, and holdout-set validation, demonstrating 50–73% cost reductions without performance loss across benchmarks.

AI engineeringAgent optimizationBenchmark Evaluation

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Agent Cost Optimization: Cut Waste, Not Intelligence

PaperAgent

Jul 22, 2026 · Artificial Intelligence

Inside GPT‑5.6’s Dropdown: How Six Leading LLMs Tune Their Reasoning Effort

The article dissects Sebastian Raschka’s “Controlling Reasoning Effort in LLMs”, explains GPT‑5.6’s multi‑level effort settings, clarifies the notion of reasoning models, outlines training vs. inference scaling, details RLVR recipes, and compares the post‑training formulas of six open‑source flagship LLMs.

GPT-5.6LLMOpen‑source Models

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Inside GPT‑5.6’s Dropdown: How Six Leading LLMs Tune Their Reasoning Effort