arXiv AI
Deleting Code Without Losing Knowledge: Regenerative Software for Python Libraries
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arXiv AI
Jun 16
User as Code: Executable Memory for Personalized Agents
arXiv:2606. 16707v1 Announce Type: new Abstract: A personalized AI agent needs a user memory: a persistent model of who the user is, built across many conversations and consulted on each new one.
arXiv AI
Sep 7
The History Is the Detector: Executing CVE Patch History, End-to-End
The paper introduces BUGSTONE‑E2E, a framework that converts vulnerability history into executable detection rules and validates them. It mines reusable rules from fixing commits, organizes them by CWE and language, and applies a funnel‑shaped pipeline that starts with lightweight analysis and culminates in LLM‑guided inspection, runtime verification, and patch generation. Using 19,325 high‑severity CVEs, the system identified 2,710 fixing commits, created 1,033 detection rules across 56 CWE families, and produced runtime evidence for 644 findings in 14 programs.
arXiv AI
Oct 7
Harness Engineering for Software Engineering via Modular Executable Dev-Primitives
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, e...
arXiv AI
Sep 7
When LLM Decompilers Recompile More and Preserve Less
The paper examines how large‑language‑model (LLM) decompilers, which produce clean, idiomatic C code, are currently evaluated mainly on recompilability and passing shipped tests. It shows that these metrics can mask significant behavioral differences: a decompiled function may recompile and pass all tests yet diverge on other inputs or lose disclosed vulnerabilities. To address this, the authors propose Decompile‑Diverge, a behavioral oracle that synthesizes drivers, fuzzes inputs, and compares the decompiled code’s behavior to the original, revealing divergences in up to 13% of cases and exposing gaps in current evaluation suites.
arXiv AI
Aug 12
Persistent Recursive Worlds Enable Autonomous Software Evolution
arXiv:2608. 10450v1 Announce Type: cross Abstract: Complex software systems develop over timescales that exceed the lifespan of any individual coding agent.
arXiv AI
Sep 24
Escaping Python Dependency Hell: A Hybrid Replay-and-Repair Pipeline for Python Dependency Resolution
The paper introduces PLLM+, a hybrid pipeline for resolving Python dependency conflicts that combines deterministic steps—such as static AST inference, replaying known successful configurations from a solutions database, and live PyPI validation—with an LLM-based repair fallback. Evaluated on the HG2.9K benchmark of 2,891 failing snippets, PLLM+ successfully fixes 1,500 cases, outperforming the baseline PLLM and reducing average runtime from 368.7 to 71.8 seconds per snippet. The majority of fixes (1,495) come from replaying existing configurations, while the LLM fallback contributes only five additional solutions.