The Inference Report

Migrating Android applications to HarmonyOS is an important yet challenging software engineering task. Existing large language model (LLM)-based migration approaches primarily translate code at the method or file level, overlooking project-wide dependencies and workflow coordination. Moreover, ArkTS, the primary language for HarmonyOS, is relatively low-resource, and migrated applications often lack sufficient tests for reliable verification, making end-to-end migration error-prone. We present A2HTrans, an agentic framework for end-to-end Android-to-HarmonyOS application migration. Rather than performing isolated code translation, A2HTrans decomposes applications into functionality-aware migration tasks, prioritizes them through dependency analysis, and orchestrates translation, validation, and iterative repair within a unified execution workflow. To improve migration reliability, A2HTrans employs a tool-augmented verification strategy that combines compiler-based syntax checking with LLM-based semantic validation and automated repair. We evaluate A2HTrans on 14 Android applications spanning diverse sizes and domains using Claude-Sonnet-4.6 as the underlying migration model. Compared with Claude Code, A2HTrans preserves 8.3% more functional completeness, improves behavioral correctness by 8.0%, and reduces migration time by 28.8%. These results demonstrate that structured agentic execution substantially improves the reliability and efficiency of project-level cross-platform software migration.

Numerical programs implementing the same computational task can exhibit different numerical behavior because of their execution stacks, program representations, or program logic. Source text and final outputs alone provide limited diagnostic information: numerical differences can propagate through corresponding computations, while distinct executions can produce equal outputs. We present NumericalDiff, an execution-based method that records assignment-level numerical events, aligns traces using a tolerance-aware longest common subsequence independent of variable names and source locations, and optionally reexecutes after replacing one intermediate value with its counterpart. Among the original and all single-intervention executions, it lexicographically maximizes matched events and minimizes unmatched events. NumericalDiff supports code comparison across language and framework front ends, currently including NumPy, PyTorch, JAX, C, C++, Fortran, and Octave. We evaluate NumericalDiff on 36 controlled cases comprising 675 pair/input instances: 36 execution-stack, 588 program-representation, and 51 program-logic instances. All 24 operator-injection instances expose trace differences, including three with tolerance-equal final outputs, while complete trace alignment is achieved for all 441 canonical cross-representation instances. Within the 51 program-logic instances, intervention improves 24 alignments and raises macro-average matching coverage from 0.4210 to 0.6513. The results show that value-based trace alignment and reexecution can delimit propagated numerical differences across heterogeneous implementations.

Reproducibility in large language model (LLM) agent evaluation depends on replay fidelity: which parts of an execution trace are reconstructed, controlled, and compared. This matters for agent debugging, failure analysis, and counterfactual evaluation of stateful tool use. We audit all 71 unsuccessful executions in a fixed historical AppWorld corpus using 284 isolated fixed-code replay attempts. Reconstructed declared settings admit 42 cases under printed-output, pre-oracle database-delta, and native-verdict/test-count checks; resetting the random-number generator before each interaction admits 68. The 26-case gap contracts to four under database/oracle checks, with one endpoint unknown. Mechanism studies identify four materialized-state discrepancies despite matching outputs and verdicts. In one admitted endpoint pair, an identical API call yields matching responses but different row effects; common future Python RNG removes that deterministic difference in two paired repetitions. A separate JWT credential-equality program and alignment controls expose authentication/write differences, with missing repetitions retained. For all 10 affected post-oracle export cases, native export from retained output trackers restores the reference comparison without changing original cached contents. A purposeful 12-case BFCL pilot shows no discrepancy under its tested public-state, return, and verdict checks. We preserve resource censoring and infrastructure deviations. The contribution is a bounded empirical characterization of replay eligibility, hidden state, and test-oracle integration, without estimating debugger accuracy, repair gains, or method-ranking distortion.

The infrastructure definitions and the code for serverless applications are often only loosely connected. This yields a large error class: the referenced infrastructure components might not exist. It is also hard to keep the application and its permissions in sync. To address this problem we present an infrastructure from code solution where we provision cloud infrastructure from Wasm Interface Type (WIT) - the language defining the interface of WebAssembly components. We limit ourselves to the basic example of an AWS Lambda function accessing S3 buckets. We build two interpreters for the WIT language: one that derives the definition of S3 buckets as infrastructure as code and least-privilege IAM permissions, and a generic runtime that runs the component as a Lambda function and provides access to the S3 buckets as capabilities. We formulate consistency properties that ensure the existence of the S3 buckets and correct permissions of the Lambda, and provide evidence that our interpreters satisfy them. We show how selected failures for deployment of such cloud components can be prevented using our technique and how a baseline setup fails at this task. Together with the strict sandboxing of the WebAssembly runtime we achieve an instance of capability-based security for access to AWS infrastructure as a further result.

Pre-training code language models relies on massive code corpora usually streamed over the internet. However, researchers rarely need a multi-terabyte archive in its entirety; instead, their workflow centers on curated subsets: selecting well-documented files, filtering out repetitive boilerplate, auditing for benchmark contamination, or retrieving specific code patterns. Yet, because modern corpora are typically published as file pointers and basic attributes rather than source text, no general structural layer exists to select which files to download: curating a subset requires downloading and parsing millions of files first just to discard most of them. We present SWHForest, an open-source framework and public dataset that provides this missing structural layer for any pointer-based corpus without redistributing source code. A single pass over the archive parses each file into an AST and immediately discards the source text, keeping only its structural summary. Queried locally or exposed by archive providers as a server-side filtering API, SWHForest selects matching files first (via SQL or index intersection) and downloads only surviving candidates. Across the ten largest languages of Stack-Edu (52.1M unique files, 341M definitions), SWHForest answers curation queries in milliseconds while cutting downloads by 46-72%, reducing live structural searches from hours of per-language streaming to seconds or minutes.

We present MicroSim, a system for binary code similarity detection that represents each function as a graph built from IDA Pro Hex-Rays microcode and compares such graphs with a contrastive Siamese graph neural network (GNN). We evaluate on the TREX cross-optimization benchmark, a corpus of 849,072 function-level microcode graphs spanning optimization levels -O0 through -O3, under a strict class-disjoint split in which no function key is shared between train and test. Under this protocol, MicroSim reaches 0.9972 area under the receiver operating characteristic curve (ROC-AUC) and 0.9762 Matthews correlation coefficient (MCC), outperforming Gemini (0.9332 ROC-AUC), SAFE (0.9752), and jTrans (0.9740) retrained on the same split, as well as a structure-free Bag-of-Words (BoW) baseline (0.9484). We analyze the gap between pair classification and ranked retrieval in graph transformers, showing that Cross-domain Similarity Local Scaling (CSLS) under unified tie-resolved evaluation raises GraphGPS mean reciprocal rank (MRR) from 0.2542 to 0.2559 (R@1 = 0.1639, R@10 = 0.4561), strictly satisfying the theoretical MRR upper bound. Finally, over 5 seeds with equal compute, the pairwise cosine-embedding loss attains a slightly higher ROC-AUC (Δ = -0.0256, 95% confidence interval [-0.3282, +0.2770]), whereas the supervised-contrastive information noise-contrastive estimation (InfoNCE) loss is significantly better on thresholded classification and ranked retrieval.