Are your LLM code benchmarks truly rejecting wrong-complexity options and interactive-protocol violations, or are they passing under-specified unit assessments? A group of researchers from UCSD, NYU, College of Washington, Princeton College, Canyon Crest Academy, OpenAI, UC Berkeley, MIT, College of Waterloo, and Sentient Labs introduce AutoCode, a brand new AI framework that lets LLMs create and confirm aggressive programming issues, mirroring the workflow of human drawback setters. AutoCode reframes analysis for code-reasoning fashions by treating drawback setting (not solely drawback fixing) because the goal process. The system trains LLMs to supply competition-grade statements, check information, and verdict logic that match official on-line judges at excessive charges. On a 7,538-problem benchmark constructed from prior datasets, AutoCode achieves 91.1% consistency with official judgments (FPR 3.7%, FNR 14.1%). On a separate, tougher 720 current Codeforces issues (together with interactive duties), the total framework studies 98.7% consistency, 1.3% FPR, 1.2% FNR.
Why drawback setting issues for analysis?
Public code benchmarks typically depend on under-specified assessments that permit wrong-complexity or shortcut options move. That inflates scores and pollutes reinforcement indicators (rewarding fragile techniques). AutoCode’s validator-first strategy and adversarial check era goal to cut back false positives (FPR)—incorrect packages that move—and false negatives (FNR)—appropriate packages rejected as a consequence of malformed inputs.
The core loop: Validator → Generator → Checker
AutoCode runs a closed loop that mirrors human contest workflows, however every step is chosen from LLM-generated candidates utilizing focused in-framework assessments.
1) Validator (decrease FNR by imposing enter legality)
The system first asks an LLM to synthesize 40 analysis inputs—10 legitimate and 30 near-valid unlawful (e.g., off-by-one boundary violations). It then prompts the LLM for three candidate validator packages and selects the one which finest classifies these instances. This prevents “appropriate” options from crashing on malformed information.
2) Generator (cut back FPR by adversarial protection)
Three complementary methods produce check instances:
• Small-data exhaustion for boundary protection,
• Randomized + excessive instances (overflows, precision, hash-collisions),
• TLE-inducing constructions to interrupt wrong-complexity options.
Invalid instances are filtered by the chosen validator; then instances are deduplicated and bucket-balanced earlier than sampling.
3) Checker (verdict logic)
The checker compares contestant outputs with the reference answer beneath complicated guidelines. AutoCode once more generates 40 checker situations and three candidate checker packages, retains solely situations with validator-approved inputs, and selects the perfect checker by accuracy in opposition to the 40 labeled situations.
4) Interactor (for interactive issues)
For duties that require dialogue with the decide, AutoCode introduces a mutant-based interactor: it makes small logical edits (“mutants”) to the reference answer, selects interactors that settle for the true answer however reject the mutants, maximizing discrimination. This addresses a niche in earlier public datasets that prevented interactives.
Twin verification permits new issues (not simply assessments for current ones)
AutoCode can generate novel drawback variants ranging from a random “seed” Codeforces drawback (two options: an environment friendly reference and an easier brute-force baseline. An issue is accepted provided that the reference output matches brute pressure throughout the generated check suite (the brute pressure might TLE on giant instances however serves as floor fact on small/exhaustive instances). This dual-verification protocol filters ~27% of error-prone objects, lifting reference-solution correctness from 86% → 94% earlier than human evaluate.
Human consultants then grade the survivors on solvability, answer correctness, high quality, novelty, problem. After filtering, 61.6% are usable for mannequin coaching, 76.3% for human coaching, and 3.2% are ICPC/IOI-level issues. Problem sometimes will increase relative to the seed, and problem achieve correlates with perceived high quality.
Understanding the outcomes
Present issues (7,538 whole; 195,988 human submissions). AutoCode: 91.1% consistency, 3.7% FPR, 14.1% FNR, vs 72.9–81.0% consistency for prior mills (CodeContests, CodeContests+, TACO, HardTests).
Latest Codeforces issues (720, unfiltered; contains interactives). AutoCode: 98.7% consistency, 1.3% FPR, 1.2% FNR. Ablations present all three generator methods and immediate optimization contribute: eradicating immediate optimization drops consistency to 98.0% and greater than doubles FNR to 2.9%.
Key Takeaways
- AutoCode {couples} a Validator–Generator–Checker (+Interactor) loop with twin verification (reference vs. brute-force) to construct contest-grade check suites and new issues.
- On held-out issues, AutoCode’s check suites attain ~99% consistency with official judges, surpassing prior mills like HardTests (
- For current Codeforces duties (together with interactives), the total framework studies ~98.7% consistency with ~1.3% FPR and ~1.2% FNR.
- The mutant-based interactor reliably accepts the true answer whereas rejecting mutated variants, enhancing analysis for interactive issues.
- Human consultants fee a large fraction of AutoCode-generated objects as training-usable and a non-trivial share as contest-quality, aligning with the LiveCodeBench Professional program’s goals.
AutoCode is a sensible repair for present code benchmarks. It facilities drawback setting and makes use of a closed-loop Validator–Generator–Checker (+Interactor) pipeline with twin verification (reference vs. brute-force). This construction reduces false positives/negatives and yields judge-aligned consistency (≈99% on held-out issues; 98.7% on current Codeforces, together with interactives). The strategy standardizes constraint legality, adversarial protection, and protocol-aware judging, which makes downstream RL reward indicators cleaner. Its placement beneath LiveCodeBench Professional matches a hallucination-resistant analysis program that emphasizes expert-checked rigor.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.
