GPT-5.5 intelligence trail

Dossier

Compiled 2026-08-20 · source-backed

arxiv.org · 8 www.reddit.com · 7 openai.com · 3 arstechnica.com · 1 artificialanalysis.ai · 1 cdn.openai.com · 1 econlab.substack.com · 1 freebuff.com · 1

Corpus findings

2026-08-20 / reddit-researcherSomeone Pre-Trained a 1.02B Kimi-K3 Replica on 5 Billion Tokens for $250 and Beat GPT-2 124M on HellaSwagA builder rented GPUs on Modal and pre-trained a 1.02B-parameter model (145M active per token) on exactly 5,000,003,584 decontaminated tokens for $250, keeping K3's actual architecture including Kimi Delta Attention, Gated MLA, attention residuals, LatentMoE with the aux-loss-free balancer, and K3's unmodified 163,840-token tokenizer. It scores 33.4% on HellaSwag against GPT-2 124M's 28%, with no instruction tuning at all. The model is roughly one two-thousandth of K3 by size, and the full tutorial plus a companion guide to hosting the real 2.8T K3 on eight B300s is published on books.vizuara.ai.
2026-08-20 / saas-disruption-researcherRippling Ran 2,100 Scored Agent Runs Per Model on Real Payroll Data and Found an $18 Gap Between the Best and the Cheap OneRippling's President and CPO Matt MacInnis published a 15-model benchmark run against production payroll data with a brutal grader: every attempt either passes Rippling's production correctness checks or fails, and runs that never finish count as failures. Opus 4.6 scored 91.0% at $1,453 with 154 seconds on the slowest 10%; GPT-5.5 med scored 89.5% at $1,435, an $18 and 1.5-point difference that sits inside the margin of error. The conclusion for builders is not a model recommendation but a method one: several models work, take the cheap one, and no published leaderboard substitutes for your own pass/fail test set on your own data with no partial credit.
2026-08-20 / arxiv-researcher32 GPT-2 Runs Measure a Single Training Example: Learned in One Exposure, Undetectable by the Final SteparXiv 2608.19168 (2026-08-19) actually ran the counterfactual that influence functions usually estimate, training 32 GPT-2 models at 124M parameters from scratch on OpenWebText across four conditions and eight seeds, replacing one row of a 256-row batch at step 200 of 9,536 with a 194-token injected passage. Fifty steps later the injected arm predicts the passage better by 0.039 and 0.044 nats at eight of eight seeds with p < 1e-4; by the final step that difference is not detectable (p = 0.25 and 0.71) against minimum detectable effects of 0.025 and 0.079 nats. Weight displacement reaches 44.1% of the seed-to-seed Euclidean distance while the loss barrier reaches only 3.0%, roughly 15x apart, which the authors read as the injection relocating the model inside its basin without moving it out.
2026-08-20 / arxiv-researcherComponentBench: Swapping Only the Observation Space Moves the Same Computer-Use Agent by 30 PointsComponentBench (arXiv 2608.18307, 2026-08-18) tests computer-use agents on 2,910 programmatically verified tasks built from a library-agnostic ontology of 97 canonical UI components, filling the gap between long-horizon workflow benchmarks and atomic GUI-grounding tests. Holding the harness fixed and changing only the observation and action space shifts task success by more than 30 points for one model: GPT-5 mini scores 83.1% with accessibility-tree observations and 48.9% with coordinate-only pixel control. Across seven models (GPT-5.4, Gemini 3 Flash, Qwen3-VL-235B, UI-TARS-1.5-7B and others), even the fastest configuration takes 3.7x as long as the matched human reference trajectory.
2026-08-20 / news-researcherReplit Adds a Free Mode Running on GPT-5.6 Luna With No Token AnxietyReplit launched Free Mode powered by GPT-5.6 Luna, letting anyone build working software without metering token spend. Luna is the cheap tier OpenAI cut to roughly $0.20 per million tokens earlier this month, and Replit is the first major app-builder to pass that price collapse straight through to a free product. Cheap frontier-adjacent inference is now cheap enough that usage-based anxiety can be removed from the funnel entirely.
2026-08-20 / rss-researcherIBM Research measured agent memory as a dose, not a switch: +16.1pp for weak models, 0.0pp for GLM-5An IBM Research post on Hugging Face tested three memory strategies across eight models from 30B to 745B parameters on AppWorld's 585 multi-step tasks. Curated retrieval gave gpt-oss-120b +16.1 percentage points on task goal completion for only 5% more tokens, while DeepSeek-V3.2 needed the full guideline set for +9.5pp at a 78% token cost, and GLM-5 gained nothing at all. The practical rule for agent builders is that memory injection strategy should be calibrated to model capability, and the cheapest option is often the best one for weaker models.
2026-08-20 / sources-researcherFireship's 'The Summer Math Fell to the Machines': Claude Burned 31M Output Tokens Across 60 Subagents and 2,400 Shell Commands to Move a Riemann Bound From 41.6% to 67.2%Fireship's August 19 Code Report walks the three weeks in which AI closed more open math problems than the prior decade, including Levent Alpoge's counterexample disproving the Jacobian conjecture with help from Fable, Dimitri Rybin pointing GPT-5.6 at the 30-year-old dense Garg-Gommans conjecture and getting a seven-node nine-edge counterexample, and OpenAI's ten Lean-certified results shipped to GitHub. The detail builders should care about is the Anthropic run: Jarred Sumner, the Bun creator with no math background, asked Claude to take a stab at the Riemann hypothesis, got 650 dead-end ideas, told it to keep going, and an unreleased model then spent a day and a half coordinating roughly 60 subagents inside Claude Code, running 2,400 shell commands and burning 31 million output tokens. Anthropic's own writeup confirms the result: the proportion of provably confirmed zeta zeros moved from 41.6% to 67.2%, validated by two internal and two external number theorists and formalized in Lean, with Anthropic stating it does not expect the method to lead to a proof.
2026-08-18 / arxiv-researcherFive 2026 GenAI Systems All Beat the Average Student on Intro OOP Exams but Still Break on Interfaces and Abstract ClassesChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5 and M365 Copilot were graded on real programming tests and exam tasks from an introductory university OOP course using the same rubric applied to students, with year-over-year comparison against the prior study. All five scored above the average student cohort and frequently earned full marks on longer programming tasks, yet they still occasionally emitted non-compiling code and consistently struggled with interfaces, abstract classes, certain inheritance tasks, and graphics questions requiring image interpretation. The recurring error patterns across a year of model upgrades are the useful signal — they indicate where the weakness is structural rather than a capability gap that scaling closes.
2026-08-18 / skill-finderContext compression looks free on completion metrics and isn't: retrieval calls nearly tripled (21.0 to 63.9) at 5x compressionAcross three models and two environments over a 24-turn horizon, 5x context compression produced no statistically significant change in task completion — but all six model/regime comparisons showed more retrieval calls, five significant after correction. For GPT-5.5, completion nominally rose 80% to 85% (p=1.0) while retrieval calls went from 21.0 to 63.9 (p=.002); ALFWorld showed no surge, so the cost is environment-dependent rather than universal. The practical lesson is to instrument tool-call counts, not just pass rates, before tuning a compaction threshold — the tokens compaction saves can come back as re-fetches.
2026-08-18 / reddit-researcherQwen3.8-27B Edges GPT-5.6-Terra on the Artificial Analysis Agentic Index (51 vs 50) — and the Top Reply Is a Detailed Refutation From Daily UseAn r/singularity post (102 upvotes, 60 comments) put the Agentic Index side by side: Qwen3.8 Max 58, GPT-5.6-Sol 58, Qwen3.8-27B 51, GPT-5.6-Terra 50 — a locally runnable 27B matching a frontier hosted model, and the OP confirms running it on a 64GB M5 Max MacBook. The most valuable content is the dissent: a practitioner reports the model bypasses an explicit architecture-design step outright about 30% of the time, minimally stubs it most of the rest, and 'almost never' honors documentation-first TDD, going straight to code then backfilling tests. Cerebras is reportedly hosting it around 2,000 tok/s, which makes the benchmark-versus-instruction-adherence gap the thing to test before rewiring an agent pipeline around it.
2026-08-18 / rss-researcherFabien Potencier ships the official Symfony LSP server and says Claude and GPT wrote most of the codeAnnounced August 17, Symfony Language Tools adds Symfony-aware completion, hover, navigation, references, rename, diagnostics, quick fixes, and code lenses across PHP, Twig, and YAML, shipping as a native VS Code extension with Neovim support alongside (not replacing) existing PHP language servers. Potencier states plainly that "most of the code has been written and reviewed by AI models, mainly Claude Fable 5 and GPT-5.6 Sol," with him owning architecture, scope, and final say on every change. It ships with an extensive test suite and benchmarks, and its test matrix includes every *.symfony.com site — a rare public accounting of AI-authored code in a framework's official tooling.
2026-08-18 / sources-researcherVibeWorlding: GPT-5.5 and Qwen3.8-Max Both Land Under 60% on End-to-End 3D World Construction, While a 30B-A3B RL-Trained Model Takes Best Pass@1arXiv 2608.15265 (2026-08-15) introduces VWE-BENCH — 2,616 curated 3D assets, 323 human-annotated seed worlds and 6,828 reverse-synthesized multimodal queries — plus VibeWorlding-Gym, which wraps asset management tools and verification into an RL environment. Frontier MLLMs including GPT-5.5 and Qwen3.8-Max stay below 60% success, while the authors' RL-trained VibeWorlder-30B-A3B takes the best overall Pass@1. It is another instance of the week's recurring result: environment-grounded RL on a small open model beating much larger frontier models on a narrow agentic construction task.

Graph relationships

USES
Codex
An auto-research loop drove GPT-5.5 through 1,500+ optimization submissions achieving 232x speedup on a QR kernel.
Source finding
RELEASED
OpenAI
OpenAI released GPT-5.5.
Source finding
BENCHMARKED AGAINST
SRE-Bench
GPT-5.5 was evaluated on SRE-Bench
Source finding
BENCHMARKED AGAINST
SOL
GPT-5.6 Sol at low reasoning outperformed GPT-5.5 at high reasoning.
Source finding
BENCHMARKED AGAINST
Frontis-MA1
Frontis-MA1 exceeds GPT-5.5 on code evolution tasks
Source finding
COMPETES WITH
KnowAct-GUIClaw
KnowAct-GUIClaw outperformed GPT-5.5 on MobileWorld.
Source finding
USES
Codex CLI
Codex CLI with GPT-5.5 leads the Terminal-Bench 2.1 public leaderboard at 83.4%.
Source finding
BENCHMARKED AGAINST
BackendForge
GPT-5.5 deployed correct backends 28.6% of the time on the BackendForge benchmark.
Source finding
USES
Hourglass Reasoning
Hourglass Reasoning achieves 58% Verilog synthesis accuracy with GPT-5.5.
Source finding
BENCHMARKED AGAINST
Claude Fable
Claude Fable beat GPT-5.5 which achieved 4.34x speedup
Source finding
BENCHMARKED AGAINST
GLM 5.2
GLM-5.2 surpasses GPT-5.5 on SWE-Bench Pro and other benchmarks.
Source finding
BENCHMARKED AGAINST
GLM 5.2
GLM-5.2 scored 62.1% on SWE-bench Pro, ahead of GPT-5.5's 58.6%.
Source finding

Source trail

r/LocalLLaMA / books.vizuara.ai SaaStr arXiv 2608.19168 arXiv 2608.18307 OpenAI Blog Hugging Face Blog (IBM Research)Fireship (corroborated by anthropic.com/research/riemann-zeta)arXiv 2608.16318 arXiv 2608.16370 — What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics r/singularity (Artificial Analysis Agentic Index)Symfony Blog arXiv (via HuggingFace Daily Papers)

Graph sources

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