Reference · live from the database
Models and levels, in full.
The complete reference: the scoring formula behind every run, every AI model your harness can run — with its list pricing and the effort multiplier that scales your score — and every challenge in the catalog, with its stack, pacing, benchmark, and the skills it tests.
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- Hidden tests
01 · How scoring works
Every score, decomposed.
One fitness function turns a captured run into a single number. It rewards a correct solution reached with fewer tokens, fewer prompts, and cheaper compute — the win is efficiency, not brute force. Every term below is live from the scoring constants.
Score = (C · W_c) / (P · M_effort) × (1,000 / weighted_tokens) × (100 / (S · W_s))
Higher is better. Correctness gates everything — a cleared run always out-ranks an unfinished one — and the other terms reward doing more with less.
- C · Correctness
- The percent of hidden tests that pass (0–100), weighted by W_c = 1.0. A full clear is 100 and always ranks first.
- Token efficiency
- 1,000 divided by your weighted token spend (input, output, and thinking, each weighted by challenge type). Fewer tokens, higher score — floored at 100 so near-zero reports cannot explode it.
- S · Speed
- 100 divided by your summed hidden-suite run time (× W_s = 1.0), floored at 1.0s so sub-second noise cannot dominate.
- M_effort · Model cost
- Your reported model’s effort multiplier divides the score, clamped to [0.50, 3.00]. Cheaper than the anchor is a discount, pricier a handicap — the per-model figures are in Harness models below. Cursor’s Composer adds +0.20.
- P · Multi-turn
- The number of prompts you sent, dividing the score (floored at 1). Reaching the fix in fewer turns pays off.
- Thinking overhead
- Heavy reasoning nudges M_effort up in small steps, so a flood of thinking tokens costs a little — capped so it can never zero a score.
Worked example
clean debugging clear · anchor modelA full clear on claude-sonnet-4-6 in 2 prompts, 5,000 input + 3,000 output tokens, no thinking — the exact breakdown the in-workspace HUD projects, shown here so you can read every term at rest. Projected score 368.
- Correctness
100% × W_c 1
100raises score - Token efficiency
1e3 ÷ 6800 weighted tokens
0.15raises score - Speed
100 ÷ (2s × W_s 1)
50.00raises score - Effort multiplier
base 1 + thinking 0 + composer 0
1.00lowers score - Multi-turn penalty
÷ P = 2
2.00lowers score
02 · Harness models
What you compete with.
You may solve a level on any AI model your harness exposes. Promptly recognizes 44 models across 8 labs. Your reported model sets an effort multiplier that scales your score, so the cheapest model is not automatically the best move — matching a frontier result on a budget model is what pays.
- In / Out / Think
- Provider list price per 1M tokens (USD). Think is the reasoning-token rate; it usually mirrors output.
- Blended
- 0.25·in + 0.75·out per 1M — output-weighted, because agentic coding is output-heavy.
- Effort multiplier
- A row's blended cost divided by the anchor's, clamped to [0.50, 3.00]. It divides your score: lower is a discount, higher a handicap.
The anchor is claude-sonnet-4-6 — the mid-tier reference fixed at ×1.00 by definition. Every other multiplier is measured against it, so re-pricing the anchor re-scales the whole board.
Anthropic
9 models
Anthropic
9 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
claude-fable-5 Frontier tier above Opus; blended 3.33 clamps down to 3.00. | $10.00 | $50.00 | $50.00 | $40.00 | ×3.00 |
claude-opus-4-6 Older Opus; still harness-selectable (Cursor). | $5.00 | $25.00 | $25.00 | $20.00 | ×1.67 |
claude-opus-4-7 Prior Opus; same list price as 4.8. | $5.00 | $25.00 | $25.00 | $20.00 | ×1.67 |
claude-opus-4-8 Current Opus flagship. | $5.00 | $25.00 | $25.00 | $20.00 | ×1.67 |
claude-sonnet-4 Original Claude 4 Sonnet; still harness-selectable (Cursor). | $3.00 | $15.00 | $15.00 | $12.00 | ×1.00 |
claude-sonnet-4-5 Prior Sonnet; Anthropic holds Sonnet at $3/$15 across versions. | $3.00 | $15.00 | $15.00 | $12.00 | ×1.00 |
claude-sonnet-4-6Anchor Mid-tier anchor row (M_effort = 1.00 by definition). | $3.00 | $15.00 | $15.00 | $12.00 | ×1.00 |
claude-sonnet-5 Current Sonnet at the standard $3/$15 list; the $2/$10 launch promo (through 2026-08-31) is not modeled. | $3.00 | $15.00 | $15.00 | $12.00 | ×1.00 |
claude-haiku-4-5 Current Haiku; clamped up to 0.50 by the lower bound. | $1.00 | $5.00 | $5.00 | $4.00 | ×0.50 |
OpenAI
15 models
OpenAI
15 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
gpt-5-5 GA flagship (Apr 2026 release doubled the GPT-5 line's price). | $5.00 | $30.00 | $30.00 | $23.75 | ×1.98 |
gpt-5-6-sol GPT-5.6 flagship (June 2026); limited preview via API/Codex partners. | $5.00 | $30.00 | $30.00 | $23.75 | ×1.98 |
gpt-5-4 Prior flagship; still available. | $2.50 | $15.00 | $15.00 | $11.875 | ×0.99 |
gpt-5-6-terra GPT-5.6 mid tier; limited preview. | $2.50 | $15.00 | $15.00 | $11.875 | ×0.99 |
gpt-5-2 Legacy flagship; still harness-selectable (Cursor). | $1.75 | $14.00 | $14.00 | $10.938 | ×0.91 |
gpt-5-2-codex Legacy Codex variant; still harness-selectable (Cursor). | $1.75 | $14.00 | $14.00 | $10.938 | ×0.91 |
gpt-5-3-codex Codex CLI default model. | $1.75 | $14.00 | $14.00 | $10.938 | ×0.91 |
gpt-5 Original GPT-5; still harness-selectable (Cursor). | $1.25 | $10.00 | $10.00 | $7.813 | ×0.65 |
gpt-5-1-codex Legacy Codex variant. | $1.25 | $10.00 | $10.00 | $7.813 | ×0.65 |
gpt-5-codex Original GPT-5 Codex variant. | $1.25 | $10.00 | $10.00 | $7.813 | ×0.65 |
gpt-5-6-luna GPT-5.6 fast tier; limited preview. Clamps to 0.50. | $1.00 | $6.00 | $6.00 | $4.75 | ×0.50 |
gpt-5-4-mini Budget 5.4 tier (Cursor/Copilot). Clamps to 0.50. | $0.75 | $4.50 | $4.50 | $3.563 | ×0.50 |
gpt-5-1-codex-mini Legacy budget Codex. Clamps to 0.50. | $0.25 | $2.00 | $2.00 | $1.563 | ×0.50 |
gpt-5-mini Original budget GPT-5. Clamps to 0.50. | $0.25 | $2.00 | $2.00 | $1.563 | ×0.50 |
gpt-5-4-nano Cheapest 5.4 tier. Clamps to 0.50. | $0.20 | $1.25 | $1.25 | $0.988 | ×0.50 |
Google
6 models
| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
gemini-3-1-pro ≤200k-context tier; >200k bills at $4/$18 (not modeled here). | $2.00 | $12.00 | $12.00 | $9.50 | ×0.79 |
gemini-3-pro Prior Pro; same ≤200k list price as 3.1. | $2.00 | $12.00 | $12.00 | $9.50 | ×0.79 |
gemini-3-5-flash Current Flash tier. | $1.50 | $9.00 | $9.00 | $7.125 | ×0.59 |
gemini-3-flash Prior Flash tier. Clamps to 0.50. | $0.50 | $3.00 | $3.00 | $2.375 | ×0.50 |
gemini-2-5-flash Legacy Flash; still harness-selectable (Cursor). Clamps to 0.50. | $0.30 | $2.50 | $2.50 | $1.95 | ×0.50 |
gemini-3-1-flash-lite Budget Flash-Lite; clamps to 0.50. | $0.25 | $1.50 | $1.50 | $1.188 | ×0.50 |
xAI
5 models
xAI
5 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
grok-4 Older flagship; xAI still lists it at anchor-equivalent pricing. | $3.00 | $15.00 | $15.00 | $12.00 | ×1.00 |
grok-4-20 Mid tier; blended 0.42 clamps to 0.50. | $2.00 | $6.00 | $6.00 | $5.00 | ×0.50 |
grok-4-3 Current flagship (Apr 2026); aggressively priced, so clamps to 0.50. | $1.25 | $2.50 | $2.50 | $2.188 | ×0.50 |
grok-build-0-1 Agentic coding model (Cursor). Clamps to 0.50. | $1.00 | $2.00 | $2.00 | $1.75 | ×0.50 |
grok-4-1-fast Fast/cheap tier; clamps to 0.50. | $0.20 | $0.50 | $0.50 | $0.425 | ×0.50 |
Cursor
4 models
Cursor
4 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
composer-1-5 Legacy Composer 1.5; priced above the anchor. | $3.50 | $17.50 | $17.50 | $14.00 | ×1.17 |
composer-1 Legacy Composer 1. | $1.25 | $10.00 | $10.00 | $7.813 | ×0.65 |
composer-2 Prior in-house model (Mar 2026). Clamps to 0.50. | $0.50 | $2.50 | $2.50 | $2.00 | ×0.50 |
composer-2-5 Cursor's current in-house model (May 2026). Clamps to 0.50. | $0.50 | $2.50 | $2.50 | $2.00 | ×0.50 |
DeepSeek
2 models
DeepSeek
2 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
deepseek-v4-pro Official V4 pro pricing; clamps to 0.50. | $0.435 | $0.87 | $0.87 | $0.761 | ×0.50 |
deepseek-v4-flash Official V4 flash pricing; clamps to 0.50. | $0.14 | $0.28 | $0.28 | $0.245 | ×0.50 |
Moonshot
2 models
Moonshot
2 models| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
kimi-k2-6 Official Moonshot list price (aggregators resell cheaper; list wins per 13a). Clamps to 0.50. | $0.95 | $4.00 | $4.00 | $3.238 | ×0.50 |
kimi-k2-7-code Current Kimi coding model (Cursor). Clamps to 0.50. | $0.95 | $4.00 | $4.00 | $3.238 | ×0.50 |
Z.ai
1 model
Z.ai
1 model| Model | In / 1M | Out / 1M | Think / 1M | Blended | Effort |
|---|---|---|---|---|---|
glm-5-2 Current GLM (Cursor). Clamps to 0.50. | $1.40 | $4.40 | $4.40 | $3.65 | ×0.50 |
Special rows
Two rows are not ordinary models — they shape how scoring treats orchestration and unrecognized models.
Meta-orchestrator · adds +0.20 to the underlying model
Meta-orchestrator: resolve the underlying model from telemetry, use its row, then add this coordination modifier (13).
Catch-all for unknown models · scored at anchor parity ×1.00
Catch-all for unknown/unpriced model ids: ANCHOR-PARITY economics (M_effort 1.00), the neutral prior. Model identity is harness-reported, so an unverifiable id must never score better than the anchor — the old flat floor costs handed unknown ids the cheapest tier (0.50), paying players to obscure their model. (The is_baseline_floor flag name is kept for DB/daemon compatibility.)
03 · The catalog
Every challenge, in full.
20 challenges across 4 progressive stages. Each ships a starter kit and public tests; a larger hidden suite is the real gate, run on the server. The benchmark is a reference token/prompt budget — the bar to beat, not a hard limit.
Stage 01· easy
The Bootsector
5 levelsWarm up on real, gnarly-but-small problems — mostly fix a bug or fill one function — and learn the Promptly optimization loop in under 25 minutes.
S01 · L01· Core Algorithms
LRU Eviction Debug
easydebugging
S01 · L01· Core Algorithms
LRU Eviction Debug
- Difficulty
- easy
- Session
- Quick
- Est. solve
- ~20 min
- Stack
- Go go1.22
- Harness
- stdin_stdout
- Benchmark
- ~2200 tokens / 3 prompts
- Tests
- 6 public · 16 hidden
- Version
- v4
A fixed-capacity cache is dropping the wrong keys: under some access patterns it discards entries that should have survived and holds on to ones a true LRU would have evicted first. Dig into how it tracks usage over time and get its eviction order back in line with least-recently-used.
Skills
Constraints
S01 · L02· Data Pipelines
Bounded Ring Buffer
easygeneration
S01 · L02· Data Pipelines
Bounded Ring Buffer
- Difficulty
- easy
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- Go go1.22
- Harness
- multi_file
- Benchmark
- ~2600 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
Bring an empty bounded FIFO queue to life: Push and Pop over a fixed capacity that stay correct when many goroutines hammer it at once. The signatures and public tests ship in the kit — the blocking and safety semantics are yours to pin down.
Skills
Constraints
S01 · L03· Web Systems
Sliding-Window Rate Limiter
easyimplementation
S01 · L03· Web Systems
Sliding-Window Rate Limiter
- Difficulty
- easy
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- TypeScript node20-ts5
- Harness
- http_integration
- Benchmark
- ~2400 tokens / 3 prompts
- Tests
- 6 public · 16 hidden
- Version
- v3
An existing Express app needs sliding-window rate limiting wired in as middleware: cap how often a given client may call, and turn away the overflow the way an HTTP API should. Grading is deterministic, so drive timing through the kit's injectable clock instead of real wall-clock — the exact limits and response shape live in the tests.
Skills
Constraints
S01 · L04· Concurrency
Crawler Race Fix
easydebugging
S01 · L04· Concurrency
Crawler Race Fix
- Difficulty
- easy
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- Rust rust1.75
- Harness
- deterministic_concurrency
- Benchmark
- ~2400 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
An async web crawler can't keep a straight story about which URLs it has already handled — some get visited twice, others slip through. Track down the concurrency flaw behind the inconsistency and make its bookkeeping hold up when tasks run in parallel. The bug reproduces the same way on every run, not as a flake.
Skills
Constraints
S01 · L05· Database
ORM N+1 Fix
easydebugging
S01 · L05· Database
ORM N+1 Fix
- Difficulty
- easy
- Session
- Quick
- Est. solve
- ~20 min
- Stack
- Python python3.11
- Harness
- multi_file
- Benchmark
- ~2000 tokens / 2 prompts
- Tests
- 5 public · 15 hidden
- Version
- v3
One route in a small orders service gets dramatically chattier with the database as its result set grows, while its neighbors stay lean. Work out what makes that endpoint's data access blow up with size and bring it back down to a flat, predictable cost.
Skills
Constraints
Stage 02· medium
Distributed Chaos
5 levelsApply distributed-systems thinking by debugging or completing one slice of a protocol — not building Raft or SWIM end to end.
S02 · L06· Distributed
Leader Election Debug
mediumdebugging
S02 · L06· Distributed
Leader Election Debug
- Difficulty
- medium
- Session
- Standard
- Est. solve
- ~30 min
- Stack
- Python python3.11
- Harness
- multi_file
- Benchmark
- ~3800 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
A Raft-style cluster occasionally crowns two leaders in the same term when messages arrive in an awkward order — exactly the safety property it is meant to guarantee. The cluster, transport, and role transitions are all provided and mostly correct; find what lets the second leader slip through and close the gap so a term can only ever have one.
Skills
Constraints
S02 · L07· Distributed
Vector Clock
mediumgeneration
S02 · L07· Distributed
Vector Clock
- Difficulty
- medium
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- Go go1.22
- Harness
- multi_file
- Benchmark
- ~3400 tokens / 4 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
Build a vector clock from empty stubs against a fixed API — Tick, Merge, and Compare. The signatures ship in the kit; the causality semantics each one has to honor are yours to work out and get exactly right.
Skills
Constraints
S02 · L08· Real-time
CRDT Convergence Debug
mediumdebugging
S02 · L08· Real-time
CRDT Convergence Debug
- Difficulty
- medium
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- TypeScript node20-ts5
- Harness
- multi_file
- Benchmark
- ~3400 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
An RGA text CRDT breaks its core promise: feed two replicas the same edits in different orders and they end up with different documents. Track down what lets them drift apart and restore convergence, so the order edits arrive in stops changing the final result.
Skills
Constraints
S02 · L09· Systems
Worker Pool Deadlock
mediumdebugging
S02 · L09· Systems
Worker Pool Deadlock
- Difficulty
- medium
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- Go go1.22
- Harness
- deterministic_concurrency
- Benchmark
- ~3600 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
A worker-pool orchestrator wedges itself under the right pattern of resource contention — tasks stop finishing and the whole thing hangs. Audit how it coordinates its goroutines, find what deadlocks it, and get every submitted task through to completion. The hang reproduces deterministically, not as a flake.
Skills
Constraints
S02 · L10· Resource Leaks
Goroutine / Conn Leak
mediumdebugging
S02 · L10· Resource Leaks
Goroutine / Conn Leak
- Difficulty
- medium
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- Go go1.22
- Harness
- multi_file
- Benchmark
- ~3200 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
When callers cancel their context, this service never fully cleans up after itself — goroutines and connections accumulate instead of winding down. Set its lifecycle straight so a cancellation actually releases everything the work started.
Skills
Constraints
Stage 03· hard
The Black Hat
5 levelsSecurity and correctness work where AI helps you verify, patch, or pinpoint — provided primitives mean no implementing crypto from zero.
S03 · L11· Cryptography
AEAD Timing-Leak Debug
harddebugging
S03 · L11· Cryptography
AEAD Timing-Leak Debug
- Difficulty
- hard
- Session
- Standard
- Est. solve
- ~30 min
- Stack
- Rust rust1.75
- Harness
- stdin_stdout
- Benchmark
- ~4200 tokens / 3 prompts
- Tests
- 6 public · 20 hidden
- Version
- v5
A secure-envelope library sometimes accepts forged or tampered messages it should reject, and the way it checks them hands an attacker a timing signal. The cipher and framing underneath are sound — make its verification both strict and constant-time so neither the false accept nor the side channel survives.
Skills
Constraints
S03 · L12· DeFi logic
Constant-Product AMM
hardimplementation
S03 · L12· DeFi logic
Constant-Product AMM
- Difficulty
- hard
- Session
- Standard
- Est. solve
- ~30 min
- Stack
- TypeScript node20-ts5
- Harness
- multi_file
- Benchmark
- ~4400 tokens / 4 prompts
- Tests
- 7 public · 20 hidden
- Version
- v4
Implement swap and its mulDiv helper for a constant-product (x·y=k) AMM against a fixed API. The straightforward version is a few lines; the real work is the arithmetic edge cases the tests lean on, where fees, rounding, and integer limits all collide.
Skills
Constraints
S03 · L13· Auth / ZK
Schnorr + Merkle Verify
hardgeneration
S03 · L13· Auth / ZK
Schnorr + Merkle Verify
- Difficulty
- hard
- Session
- Deep
- Est. solve
- ~35 min
- Stack
- Python python3.11
- Harness
- multi_file
- Benchmark
- ~4800 tokens / 4 prompts
- Tests
- 8 public · 22 hidden
- Version
- v4
Implement schnorr_verify and merkle_verify from scratch on top of the provided elliptic-curve primitive. The kit pins the serialization convention and ships one worked example; your job is to turn the verification math into code that agrees with the standard test vectors.
Skills
Constraints
S03 · L14· AppSec
SQLi + Proto Pollution
harddebugging
S03 · L14· AppSec
SQLi + Proto Pollution
- Difficulty
- hard
- Session
- Standard
- Est. solve
- ~30 min
- Stack
- Node node20
- Harness
- http_integration
- Benchmark
- ~4200 tokens / 4 prompts
- Tests
- 6 public · 20 hidden
- Version
- v4
An Express API ships with two live vulnerabilities — one lets a crafted request read data it never should, the other lets a malicious payload quietly poison objects across the whole app. Find where untrusted input reaches something dangerous in each case and shut both down, without breaking any legitimate request path.
Skills
Constraints
S03 · L15· Reverse Eng.
Obfuscated Loop Debug
harddebugging
S03 · L15· Reverse Eng.
Obfuscated Loop Debug
- Difficulty
- hard
- Session
- Standard
- Est. solve
- ~25 min
- Stack
- C gcc13-c17
- Harness
- multi_file
- Benchmark
- ~4000 tokens / 3 prompts
- Tests
- 6 public · 18 hidden
- Version
- v3
A bit-packed analytics toolkit is written top to bottom in dense, branchless bit-twiddling — and one of its routines quietly returns the wrong answer on certain inputs. Work through the obfuscation to the function that is off, pin down exactly which inputs break it, and correct it without disturbing the rest.
Skills
Constraints
Stage 04· expert
Quantum & Scale
5 levelsPrestige capstones (30–45 min) — scaffolded single-module implementations or deep debugging, still CPU-gradable.
S04 · L16· AI Systems
Online-Softmax Block
expertgeneration
S04 · L16· AI Systems
Online-Softmax Block
- Difficulty
- expert
- Session
- Deep
- Est. solve
- ~45 min
- Stack
- C++ gcc13-cpp20
- Harness
- multi_file
- Benchmark
- ~6000 tokens / 5 prompts
- Tests
- 7 public · 24 hidden
- Version
- v3
A tiled attention kernel is fully built except for its heart: the block-update step that folds each new tile of scores into a running softmax. Implement that one function so the streaming result matches a dense reference to tight tolerances — including the inputs crafted to wreck a naive approach.
Skills
Constraints
S04 · L17· Compilers
Bytecode Constant-Fold
expertimplementation
S04 · L17· Compilers
Bytecode Constant-Fold
- Difficulty
- expert
- Session
- Deep
- Est. solve
- ~35 min
- Stack
- Python python3.11
- Harness
- multi_file
- Benchmark
- ~5600 tokens / 4 prompts
- Tests
- 7 public · 20 hidden
- Version
- v3
Write the constant-folding optimization pass for a small bytecode IR, on top of the provided parser and evaluator. The catch is doing it safely — collapsing what is genuinely constant while leaving the program's observable behavior exactly as it was. Dead-code elimination is an optional, separately-scored bonus.
Skills
Constraints
S04 · L18· Game Dev
Sweep-and-Prune Collision
expertimplementation
S04 · L18· Game Dev
Sweep-and-Prune Collision
- Difficulty
- expert
- Session
- Deep
- Est. solve
- ~35 min
- Stack
- C++ gcc13-cpp20
- Harness
- multi_file
- Benchmark
- ~5600 tokens / 4 prompts
- Tests
- 7 public · 20 hidden
- Version
- v3
Fill in the broad-phase stage of a small 2D physics engine — the pass that decides which bodies are even worth a precise collision check. The body, world, and narrow phase are already built for you. Correctness alone won't cut it: it has to stay fast as the world grows, or brute force would have done the job just as well.
Skills
Constraints
S04 · L19· ML Ops
NumPy Training NaN
expertdebugging
S04 · L19· ML Ops
NumPy Training NaN
- Difficulty
- expert
- Session
- Deep
- Est. solve
- ~35 min
- Stack
- Python python3.11
- Harness
- multi_file
- Benchmark
- ~6400 tokens / 4 prompts
- Tests
- 7 public · 22 hidden
- Version
- v4
A small MLP training run blows up to NaN within a handful of steps. More than one thing is destabilizing it — read the way it diverges, reason out what is compounding the blow-up, and fix each cause until the network trains cleanly to its target loss with every step along the way finite.
Skills
Constraints
S04 · L20· Systems
Scheduler Deadlock
expertdebugging
S04 · L20· Systems
Scheduler Deadlock
- Difficulty
- expert
- Session
- Deep
- Est. solve
- ~40 min
- Stack
- Rust rust1.75
- Harness
- deterministic_concurrency
- Benchmark
- ~6800 tokens / 5 prompts
- Tests
- 7 public · 24 hidden
- Version
- v3
A cooperative task runtime grinds to a halt under a particular ordering of wakes and schedules — forward progress simply stops. Its lock discipline is spelled out right there in the code; somewhere a path breaks the rules it lays down. Find that violation and restore liveness so the runtime keeps moving no matter the ordering. The stall reproduces deterministically.
Skills
Constraints