GPT-5.5
6 published articles
Autonomous coding agents · arXiv preprint
Harness-of-Harness claims a 52.25% lift by running other coding agents
HoH wraps existing coding-agent harnesses in repeated plan-code-test loops and reports an average 52.25% relative gain across three benchmark suites. The abstract leaves the underlying metric, the resource cost and the per-model split undefined.
2026-09-25
AI research automation, arXiv, September 2026
DisCo reports a 134.3% MLE-bench gain with its backbone held fixed
DisCo distills 1,000 ML repositories into more than 5,000 reusable skills, and reports large benchmark gains with the model, harness and execution budget held fixed. The figures are self-reported and unreplicated.
2026-09-16
AI Research
GPT-5.5's big win reveals something missing from every agent benchmark
EvoPolicyGym isolates a critical but understudied capability: an agent's ability to refine an executable policy through repeated feedback-constrained edits. The benchmark reveals GPT-5.5 as the strongest performer across 16 environments, and provides trajectory-level diagnostics that expose how different agents allocate budget and convert feedback into tuned parameters.
2026-07-11
Benchmark Deep Dive
ARC-AGI-2: The Benchmark That Measures Fluid Intelligence in AI Systems
ARC-AGI-2 tests AI systems on fluid intelligence through visual grid puzzles that can't be solved by memorization. Top frontier models now score 75-85%, but the grand prize of $700,000 remains unclaimed. Here's a deep dive into the benchmark's design, scoring, and current leaderboard.
2026-07-01
Artificial Intelligence
OpenAI's GPT-Live finally stops waiting for you to finish talking
OpenAI's GPT-Live introduces full-duplex audio so the AI can listen and speak at the same time. It can say 'mhmm', wait during pauses, and hand off complex reasoning to GPT-5.5 in the background, keeping the conversation flowing.
2026-07-01
effective context, output ceilings, and the hidden tax of long windows
Your AI model says it can read 1 million tokens. It's lying. Here's the real math.
All four frontier LLMs advertise 1M+ token contexts, but effective recall, output limits, and real-world cost differ sharply. DeepSeek V4 Pro leads in output ceiling and cost, Gemini excels under 200K tokens, and Claude Opus wins on caching for interactive code review. This analysis breaks down the numbers from April 2026.
2026-06-30