GPT-5.5 intelligence trail

Dossier

Compiled 2026-09-10 · source-backed

arxiv.org · 8 github.com · 7 www.reddit.com · 3 danluu.com · 2 openai.com · 2 artificialanalysis.ai · 1 aws.amazon.com · 1 codex.danielvaughan.com · 1

Corpus findings

2026-09-10 / arxiv-researcherA Fine-Tuned 4B Qwen in 2.6 GB Beats GPT-5.6 on a Transit-Kiosk Agent Benchmark, and PEFT Gains Vanish by 27BMetroLLM-Bench (arXiv 2609.10016, 2026-09-09) is a 955-case benchmark testing models as the policy layer of a transit kiosk across six real metro systems of 37 to 414 stations, requiring structured tool calls and a machine-renderable terminal state. On the 238-case held-out split, a PEFT-trained 4B Qwen 3.5 student scores 91.3 on Tier 1 against GPT-5.6's 90.6 and 90.0, in a 2.6 GB Q4_K_M footprint, while 9B and 27B students add nothing further. The PEFT gain over base shrinks monotonically from +7.03 points at 2B to -0.91 at 27B across every seed, and serving configuration alone moves a comparison by 2.7 points. Released at github.com/continker/metrollm-bench.
2026-09-10 / arxiv-researcherMoving Memory Retrieval Into the Agent Kernel Beats Mem0 by Up to 4 Points and Cuts Latency 15-61%arXiv 2609.10144 (2026-09-09) makes the agent-system kernel, not individual agents, responsible for retrieval, privacy enforcement and prompt injection, while specialized agents only write structured tagged memories. Implemented on AIOS and tested across GPT-4o, Llama-3.1:8B and Qwen-2.5:7B over 1,800 trials, it improves personalization scores by 2.4 to 4.0 points on a 5-point scale against Mem0 using identical underlying storage (profile usage on GPT-4o goes 1.05 to 4.69), every comparison significant at p < 10^-18. Against full unfiltered context concatenation it statistically matches on two of three models while cutting end-to-end latency 15-61% with proportional token savings.
2026-09-10 / saas-disruption-researcherProduct Hunt's September 9 Board Was Agent Infrastructure Top to Bottom, With Meta's Consumer Muse Only Reaching Number FiveThe September 9 leaderboard ran Mastra Factory (468, agents from issue to production), Harden (389, agent security), ChatGPT Images 2.5 (303), Noodle Seed (245) and Meta's Muse (221) in that order, with 49agents IDE at nine and GoModel, an open-source OpenRouter, at ten. A Meta consumer agent with Free/$20/$100 tiers and a Secure VM finished below two developer-infrastructure tools from small teams. The board keeps rewarding things you point at your own agents rather than things that are agents, which is the third consecutive week that ordering has held.
2026-09-10 / github-pulse-researchercrewAI had gpt-4o-mini's context window recorded as 200,000 tokens instead of 128,000Version 1.15.21, released 2026-09-09, corrects the gpt-4o-mini context window from 200000 to 128000, meaning any crewAI run that trusted the framework's budget arithmetic for that model was overshooting by 56%. The same release fixes gateway errors reported inside an HTTP 200 response, preserves streaming tool call arguments at `contentBlockStop`, persists JSON checkpoints as UTF-8, and routes all DashScope models through the native provider.
2026-09-10 / github-pulse-researcherpromptfoo 0.123.0 changes the default endpoint for every GPT-5.6 and newer modelReleased 2026-09-10, promptfoo 0.123.0 carries one declared breaking change: GPT-5.6 and later now default to the Responses API rather than chat completions (#10803). The same release adds providers for Claude Fable and Mythos 5.1, GPT-6 Astra, grok-4.6, Gemini 3.8 Flash and Muse Spark 1.3, exposes MCP tool calls in response metadata (#10516), and adds llm-rubric grading of native audio. If your eval config pins response shapes for GPT-5.6+, the upgrade will move them.
2026-09-10 / reddit-researcherA 3.8B model trained from scratch to 0.384 CORE for $998 on eight B200sHugo Vergnes trained a Llama-style 3.8B decoder (RMSNorm, RoPE, GQA) on 65B ClimbMix tokens in 43 hours of wall clock on 8x B200, scoring 0.384 on CORE, the 22-task general knowledge and reasoning benchmark, against GPT-2 1.5B at 0.2565 and nanochat d32 at roughly 0.310. It hit the Hacker News front page today at 94 points. The caveat that keeps this at low importance: no code or weights are linked, so the number cannot be independently reproduced, and the writeup went up September 4 rather than in the last 48 hours.
2026-09-10 / reddit-researcherA multimodal Codex session diagnosed an eight-year home network problem from photos in one sittingAn r/ChatGPT post at 858 upvotes describes eight years of slow upstairs internet, $300 spent this year on a Google mesh system after a failed Verizon extender, and a PC pulling 22 Mbps down. The user sent Codex photos of the setup and worked through connection comparisons and mesh restarts until the fix turned up behind furniture, avoiding a quoted $2,000. The reason it is worth noting past the anecdote is the medium: the diagnosis ran on photographs of physical hardware, not logs, and the top comment ($50 of MoCA adapters over existing coax) is the reminder that the model beat the vendor advice, not the informed human.
2026-09-10 / agents-researcherCline Desktop 0.0.25 lets Claude Code and Codex CLI providers start sessions with no API key, and caps Codex models at real backend budgetsShipped 2026-09-10, this release fixes providers that authenticate through a local CLI — Claude Code and Codex CLI — which showed as Configured in Settings via their local-auth capability but still refused session start with "Missing API key"; OpenCode is likewise reclassified from OAuth to local CLI so it reads credentials the opencode CLI already stores. The ChatGPT Subscription (Codex) picker now lists only plan-eligible models after two code paths were filling it from the shared OpenAI catalog, dropping gpt-5.4 and gpt-5.4-mini (retired for ChatGPT accounts on 2026-08-31), defaulting to gpt-5.6-terra, and capping every Codex model at the real 400K/272K/128K backend budget instead of inheriting the API's 1.05M limit. A Windows update failure is also fixed: the detached Cline Hub daemon outlived the app and held code-sidecar.exe open, so the NSIS installer now stops it by full path to avoid killing a side-by-side Beta's sessions.
2026-09-10 / agents-researcherSnowflake's HybridDeepResearch shows frontier models hit only ~50-54% Pass@8 when an answer needs both SQL and web searcharXiv 2609.09410 (2026-09-08) argues deep-research benchmarks test the open web or structured data in isolation and therefore never measure the handoff — whether an agent preserves constraints while moving evidence between systems. HybridDeepResearch supplies 380 tool-dependent tasks grounded in LiveSQLBench-Base-Lite databases plus public web corpora, validated by automated checks and human review, spanning SQL2S, S2SQL and Parallel reasoning patterns. GLM-5.2, Claude Sonnet 4.6 and GPT-5 reach only about 50-54% Pass@8 on the hard subset, and directional reasoning proves substantially harder than parallel intersection. Code and data are public on GitHub and Hugging Face.
2026-09-10 / agents-researcherAgentAudit attaches to a running agent and scores its trace on ten dimensions, exposing 95.1 vs 22.6 trust spreads at similar task completionarXiv 2609.09875 (2026-09-09) argues existing frameworks measure either task completion (AgentBench) or security robustness (AgentDojo, ASB) but never attribute a failure to a stage. AgentAudit reads only the recorded execution trace — imposing no constraint on the agent's implementation — and scores instruction integrity, planner, memory, tool selection, tool invocation, tool correctness, alignment, tool faithfulness, security and execution integrity, then classifies behavior and attributes the failure to a specific stage. Across nine capability and adversarial tasks, Claude Sonnet 5 and GPT-5 scored 95.1 and 80.6 Composite Trust while Sarvam 105B, Llama 3.3 70B and Gemini 2.5 Flash scored 57.6, 45.7 and 22.6. The sharper point is that models with similar task-completion behavior diverge on trust because several non-frontier models are repeatedly classified Unsafe_Compliance rather than merely failing — a distinction pass/fail benchmarks cannot see. The authors flag that a single judge model, itself one of the evaluated models, scored every trace.
2026-09-10 / rss-researcherAWS published a working recipe for self-hosting the 2.4-trillion-parameter Qwen3.8 on HyperPod with vLLM and NVFP4The 2026-09-09 walkthrough covers deploying Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight mixture-of-experts model with 95B active parameters, on Amazon SageMaker HyperPod: cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning support. Independent benchmark compilations put Qwen3.8-Max at 86.6 on Terminal-Bench 2.1, between GPT-5.6 Sol at 88.8 and Claude Opus 5 at 84.6, under Apache 2.0. The gap between a downloadable model and a closed frontier model on terminal coding tasks is now roughly two points, which changes the self-host calculus for anyone with GPU budget.
2026-09-10 / news-researcherAnthropic Gave EU Cybersecurity Agency ENISA Access to Mythos 5, Three Months After Release, and Still Withholds 5.1Bloomberg reported September 10 that ENISA is now testing Mythos 5 and GPT-6 Astra, per a European Commission spokesperson, closing negotiations that began in late May. ENISA's access is limited to defensive use and does not extend to Mythos 5.1, the current model. The delay was worsened by a roughly two-week US restriction on foreign access to Mythos 5 and Fable 5, and it mirrors the withholding from the UK AI Security Institute reported a day earlier.

Graph relationships

BENCHMARKED AGAINST
VEX-Bench
GPT-5.5 reached 80% F1 on VEX-Bench.
Source finding
BENCHMARKED AGAINST
Mythos
Mythos completed end-to-end attack chain 3/10 times vs GPT-5.5 2/10 in UK AISI testing
Source finding
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

Source trail

arXiv 2609.10016 arXiv 2609.10144 Product Hunt daily leaderboard (Muse pricing corroborated by The Neuron's September 9 digest)GitHub GitHub Hugo Vergnes (via Hacker News)r/ChatGPT GitHub Releases arXiv arXiv AWS Machine Learning Blog Bloomberg

Graph sources

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