Live AI progress · May 14 GPT-5.5 acceleration track

Live AI Learning Progress

People do not just want a chatbot. They want to see whether an AI is actually improving. Cronus is now past 24,979,513 verified eval rows with semantic maturity green, 500/500 recent reliability, 162/162 curriculum coverage, 22 semantic rule pages, stale ratio 0.094, and a 99.98% all-time pass rate.

24,979,513verified eval rows
24,975,346passed eval rows
76%ChatGPT 5.5 operator parity estimate
38%AGI / frontier parity journey
Live right now

What Cronus is actually learning

Open fullscreen watch mode

Loading live Cronus training feed...

--current training cycle
--eval rows reached
--last batch score
--last live update
LIVE TRAINING SIGNAL
Cycle stage: watching next scored attempt
Cronus is loading the current task...Signals move as Cronus routes work, skips model calls when possible, attempts harder transfer exams, fetches safe web lessons, scores results, and stores lessons.
Waiting for live trainer packets...
Why this task mattersLoading task context...
What Cronus is improvingLoading improvement target...
What you are watching
Blue = fresh attempts moving through the trainer.
Green = passes / useful wins Cronus can keep.
Red = misses routed back into replay until Cronus improves.
Yellow = Overdrive shortcut. Deterministic drills skip model calls, saving generation for harder transfer work.
Diagram key
Task entersA new safe prompt, drill, or training item enters the loop.
RouteCronus chooses the cheapest useful path: shortcut, attempt, replay, web fetch, or transfer exam.
No-model shortcutYellow overdrive path. Deterministic drills run with tools directly instead of spending model generation.
AttemptCronus tries the task with local tools or model reasoning.
ScoreThe attempt is checked by a verification gate and becomes pass, fail, or partial signal.
Replay missFailures are turned into targeted retries so the same weakness gets practiced.
Transfer examUnscaffolded tests check whether the skill works without the training prompt holding his hand.
Web fetchPrivate-only safe web learning fetches public pages as cleaned text. No private/local hosts.
Lesson memoryUseful wins and repair patterns become reusable lessons.
Next taskThe scheduler picks the next weak lane, fresh skill, or transfer check.

Latest win

Loading...

Latest struggle

Loading...

Overdrivebreakthrough learning mode
0 callssaved on deterministic drills
83%latest transfer exam
--attempts in latest window
--
last batch quality pulse
--misses being replayed
LiveWaiting for the next update...

Thought trail

Loading Cronus learning trail...

Live activity stream

LIVELoading attempts...

What the loop is doing

Public-safe live feed. It shows task names, pass/fail signals, and learning focus only -- never private files, secrets, prompts, credentials, or internal instructions.

The mission: self-learning toward AGI

Cronus is being built around one big question: can an AI agent learn how to learn faster? The goal is for Cronus to become increasingly self-learning, improving from every safe challenge, failure, tool trace, web-ingest card, and verified lesson.

In plain English: Cronus is trying to figure out how to do more with less. Better prompts, fewer retries, smarter tool use, stronger memory, cleaner verification, and faster learning loops. The long-term target is AGI-level usefulness, but the public board stays honest about where he is today.

Learn fasterTurn failures into reusable lessons.
Use lessNeed fewer attempts, fewer tokens, and fewer manual fixes.
Move toward AGITrack the journey openly with dates, graphs, and safety gates.

Live streaks

Quick signals for people watching the training loop right now.

--Pass streak
--Replay/miss streak
--Hard-topic wins in window

Important chart note

May 14 raw SVG hard fix. These are the canonical progress charts. Each SVG chunk appends May 10 and May 14 while preserving Apr 7 through May 9 historical checkpoints, including May 1, May 2, May 3, May 4, May 5, May 6, May 7, May 8, and May 9.

Eval rows over time

Apr 7: 34Apr 8: 123Apr 12: 466Apr 19: 1,675Apr 25: 3,570Apr 26: 4,576Apr 30: 10,824May 1: 11,709May 2: 43,955May 3: 369,096May 4: 2,611,394May 5: 4,055,376May 6: 7,500,448May 7: 7,500,448May 8: 8,454,372May 9: 14,221,520May 10: 19,027,981May 14: 24,979,513May 14 current checkpoint: 24,979,513Apr 7: 34Apr 734Apr 8: 123Apr 8123Apr 12: 466Apr 12466Apr 19: 1,675Apr 191,675Apr 25: 3,570Apr 253,570Apr 26: 4,576Apr 264,576Apr 30: 10,824Apr 3010,824May 1: 11,709May 111,709May 2: 43,955May 243,955May 3: 369,096May 3369,096May 4: 2,611,394May 42,611,394May 5: 4,055,376May 54,055,376May 6: 7,500,448May 67,500,448May 7: 7,500,448May 77,500,448May 8: 8,454,372May 88,454,372May 9: 14,221,520May 914,221,520May 10: 19,027,981May 1019,027,981May 14: 24,979,513May 1424,979,513

ChatGPT 5.5 operator parity

Apr 7: 57%Apr 8: 61%Apr 12: 86%Apr 19: 66%Apr 25: 68%Apr 26: 69%Apr 30: 69%May 1: 70%May 2: 70%May 3: 70%May 4: 70%May 5: 70%May 6: 73%May 7: 73%May 8: 73%May 9: 73%May 10: 74%May 14: 76%May 14 current checkpoint: 76%Apr 7: 57%Apr 757%Apr 8: 61%Apr 861%Apr 12: 86%Apr 1286%Apr 19: 66%Apr 1966%Apr 25: 68%Apr 2568%Apr 26: 69%Apr 2669%Apr 30: 69%Apr 3069%May 1: 70%May 170%May 2: 70%May 270%May 3: 70%May 370%May 4: 70%May 470%May 5: 70%May 570%May 6: 73%May 673%May 7: 73%May 773%May 8: 73%May 873%May 9: 73%May 973%May 10: 74%May 1074%May 14: 76%May 1476%

AGI / frontier parity journey

Apr 7: 14%Apr 8: 17%Apr 12: 24%Apr 19: 27%Apr 25: 28%Apr 26: 29%Apr 30: 29%May 1: 29%May 2: 30%May 3: 31%May 4: 33%May 5: 35%May 6: 36%May 7: 36%May 8: 36%May 9: 36%May 10: 37%May 14: 38%May 14 current checkpoint: 38%Apr 7: 14%Apr 714%Apr 8: 17%Apr 817%Apr 12: 24%Apr 1224%Apr 19: 27%Apr 1927%Apr 25: 28%Apr 2528%Apr 26: 29%Apr 2629%Apr 30: 29%Apr 3029%May 1: 29%May 129%May 2: 30%May 230%May 3: 31%May 331%May 4: 33%May 433%May 5: 35%May 535%May 6: 36%May 636%May 7: 36%May 736%May 8: 36%May 836%May 9: 36%May 936%May 10: 37%May 1037%May 14: 38%May 1438%

Corrected pass rate

Apr 7: 82%Apr 8: 83%Apr 12: 91%Apr 19: 94%Apr 25: 88%Apr 26: 88%Apr 30: 85%May 1: 85%May 2: 100%May 3: 99.2%May 4: 99.83%May 5: 99.89%May 6: 99.95%May 7: 99.95%May 8: 99.95%May 9: 99.97%May 10: 99.98%May 14: 99.98%May 14 current checkpoint: 99.98%Apr 7: 82%Apr 782%Apr 8: 83%Apr 883%Apr 12: 91%Apr 1291%Apr 19: 94%Apr 1994%Apr 25: 88%Apr 2588%Apr 26: 88%Apr 2688%Apr 30: 85%Apr 3085%May 1: 85%May 185%May 2: 100%May 2100%May 3: 99.2%May 399.2%May 4: 99.83%May 499.83%May 5: 99.89%May 599.89%May 6: 99.95%May 699.95%May 7: 99.95%May 799.95%May 8: 99.95%May 899.95%May 9: 99.97%May 999.97%May 10: 99.98%May 1099.98%May 14: 99.98%May 1499.98%

Historical training timeline

GPT-5.5 acceleration keeps climbing.

Rows reach 24,979,513; passed rows 24,975,346; recent reliability 500/500; coverage 162/162; semantic rules 22; stale ratio 0.094; pass rate 99.98%. May 1-May 10 checkpoints remain preserved in the chart SVGs.

Post-May 9 acceleration checkpoint.

Rows reached 19,027,981 with 500/500 recent reliability and clean coverage, appended without deleting earlier May checkpoints.

GPT-5.5 acceleration soak.

Rows reach 24,979,513; passed rows 24,975,346; recent reliability 500/500; coverage 162/162; clean exam soak 10/10; semantic rules 22; stale ratio 0.094. May 1-May 8 checkpoints remain preserved in the chart SVGs.

Semantic maturity checkpoint.

Rows reach 8,454,372; passed rows 8,450,205; recent reliability 500/500; coverage 145/145; clean exam soak 9/10; semantic rules 22; stale ratio 0.094.

Weighted self-wiki freshness stability.

Public board held the 7,500,448 eval checkpoint while semantic maturity and overnight watchdog work soaked cleanly.

Throughput and evidence-volume jump.

Rows reach 7,500,448 with 500/500 recent reliability and preserved May 1-May 6 graph history.

p1200 reliability stabilizes.

Rows reach 4,055,376 while the public history remains append-only.

Large-scale clean training continues.

Rows reach 2,611,394 and the daily chart keeps earlier checkpoints intact.

Operator/adversarial lanes go green.

Rows reach 369,096 with public progress charts expanded beyond May 2.

Web learning and challenge loop expands.

Rows reach 43,955 with 500/500 recent reliability.

Web search stack and public sandbox.

Rows reach 11,709; SearXNG web learning pipeline added; public mode locked against secrets, installs, SSH, and private data.

Accelerated curriculum expands the surface.

Rows reach 10,824 with web/tool-order and operator-style task lanes.

Continuous training compounds.

Rows move to 4,576; ChatGPT 5.5 parity estimate reaches 69%; cautionary lessons expand.

Overnight direct training proves the loop.

Eval rows 3,570, raw passes 2,799, corrected passes 3,158.

Eval base crosses 1,675 rows.

Corrected pass rate around 94%, broader test suite around 671 tests, full curriculum coverage at the time. This is also where the public comparison shifted to the harder ChatGPT 5.5 benchmark.

Prompt-aware learning begins.

Cronus starts using relevant failure/eval traces instead of blind latest replay. Public board showed 34 verified tests and early AGI journey framing.

How Cronus compares to current AI assistants

This is not a scientific benchmark against private model weights. It is a product-side operator estimate: how useful Cronus is as a tool-using local agent compared with known assistant tiers.

76%vs ChatGPT 5.5 as a real operator
45%vs ChatGPT/Claude for broad general reasoning
76%coding/tool discipline estimate
38%AGI/frontier parity journey
Honest framing: ChatGPT and Claude are still far stronger general-purpose models. Cronus is interesting because it is local, tool-using, inspectable, and learning in public, not because it is already smarter than frontier AI.