arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.
arXiv:2509. 24148v3 Announce Type: replace-cross Abstract: Test-Driven Development (TDD) is a widely adopted practice that requires developers to create and execute tests alongside implementation.
arXiv:2607. 19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases.
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a behavioral oracle, even frontier models solve fewer than 1% of instances.
CCTU is a new benchmark designed to evaluate large language models (LLMs) on their ability to use tools under complex constraints. It includes 200 test cases that average seven constraint types and 4,700‑token prompts, covering resource, behavior, toolset, and response dimensions. An executable validation module performs step‑level checks, and nine state‑of‑the‑art LLMs were tested, revealing that none exceed a 20% task completion rate when strict constraints are enforced, with frequent violations and limited self‑refinement.
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv:2604.16625v2 Announce Type: replace-cross Abstract: Recent large language model (LLM) agents have shown promise in using execution feedback for test-time adaptation. However, robust self-improv...
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
The paper investigates how well large language models specify the software environments needed to run AI-generated code. Using a new agent protocol and a three‑layer dependency framework, the authors evaluate three coding agents across four languages and fifty tasks, finding that dependency specifications are often inconsistent, redundant, or incomplete. Agreement on dependency sets is as low as 7% for identical tasks, and newer agents show no improvement, indicating that environment specification remains a significant, unaddressed challenge in code generation.
arXiv:2610.16859v2 Announce Type: replace Abstract: For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task mu...
arXiv:2610.14224v1 Announce Type: new Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a pr...