GraphIR, an architecture-aware intermediate representation that supplements executable programs with a mutation-aligned candidate state, achieves the best overall search performance while maintaining comparable model size and favorable end-to-end NAS efficiency when integrated into OpenEvolve.
Abstract
Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs. However, code-level flexibility does not provide the architecture state needed for effective mutation: LLMs must infer tensor dependencies, editable components, and compatibility constraints from implementation details. To address this representation mismatch, we propose GraphIR, an architecture-aware intermediate representation that supplements executable programs with a mutation-aligned candidate state. GraphIR organizes each candidate through three complementary views: a computation skeleton describing tensor flow, a mutation surface exposing editable modules and operations, and a validity envelope capturing interface contracts, propagated shapes, and downstream dependencies. To evaluate our method, we construct NAS-Dependency, a 120-question benchmark covering six complementary dependency-reasoning dimensions. The diagnostic shows that GraphIR is particularly effective at identifying exact producer occurrences, tracing dependency propagation, and diagnosing interface and failure risks. Across six downstream benchmarks including CLRS, GraphIR achieves the best overall search performance while maintaining comparable model size and favorable end-to-end NAS efficiency when integrated into OpenEvolve. These results show that a mutation-oriented architecture state provides an effective interface between executable neural programs and LLM-guided architecture evolution.
Most detectors of AI-generated code answer a binary question: human or machine. We study the multi-class problem, naming the generator family behind a program, and the open-set problem that follows from it, in which the generator was never seen during training. Our detector, GraphCSE, represents a program as a heterogeneous code graph with four edge relations and fuses structural (syntax-type) and semantic (token) node channels through a learned per-node gate before relation-aware graph attention. On a controlled synthetic corpus of 2,500 Python programs spanning a human class built from eight style archetypes and four generator-family proxies, GraphCSE reaches 92.1 ± 0.6% macro-F1, the strongest learned model we test and 11.2 points above a GCN on identical graphs, though character n-gram TF-IDF remains slightly ahead in-distribution (93.4 ± 0.5%). The paper's central findings concern unseen generators. First, a negative one: on single programs, all five rejection rules we test (closed-set argmax, centroid and k-NN distance, energy, maximum softmax probability) fail on held-out families, with mean AUROC no better than 0.65: a tight unseen style inside the human envelope is indistinguishable from an unusual human. Second, a constructive one: judging a set of programs from the same source by a two-sided anomaly test on embedding statistics, calibrated only on human data, restores model-agnostic detection, reaching 86.4-97.3% mean detection at ten programs per source (AUROC up to 0.972) in the graph embeddings versus 53.6% in raw TF-IDF space. Provenance of unseen generators is a property of collections, not snippets.
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