arXiv AI
Jul 7
The agent creates, we validate: A Lightweight Framework for Agentic Artifact Generation
arXiv:2607. 02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.
TAG: A Lightweight Framework for Test-Driven Agentic Artifact Generation
arXiv:2607. 02615v2 Announce Type: replace-cross Abstract: Generating structured artifacts with Large Language Models - e.
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arXiv AI
Jul 14
TENET: One Step Toward Test-Driven Development for Repository-Level Code Generation
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 AI
Jul 17
StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.
arXiv AI
Jul 21
AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows
arXiv:2607. 16345v1 Announce Type: cross Abstract: Modern agentic systems increasingly rely on skills: installable packages of natural language and code that teach an LLM agent to perform a domain task.
arXiv AI
Jul 13
The Patchwork Problem in LLM-Generated Code
arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.
arXiv AI
Jun 11
When Generic Prompt Improvements Hurt: Evaluation-Driven Iteration for LLM Applications
arXiv:2601. 22025v2 Announce Type: replace-cross Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically variable, and sensitive to prompt and model changes.
arXiv Machine Learning
Jul 10
TTHE: Test-Time Harness Evolution
arXiv:2607. 08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures.
arXiv AI
Sep 21
Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
arXiv:2605.06445v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, productio...
arXiv AI
Aug 18
TDD-Agent: Test-Driven Reasoning for Code Generation
arXiv:2608. 16742v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging.
arXiv AI
Aug 5
LiveEvalBench: Toward Open-World Evaluation for Web Generation
arXiv:2608. 03689v1 Announce Type: new Abstract: Large language models are increasingly capable of synthesizing executable frontend projects, yet existing benchmarks still treat web generation as a static evaluation problem.
arXiv Computation and Language
Aug 28
Agent Seer: Synthesizing Scenarios from Specification Understanding
Agent Seer is a pipeline that automatically synthesizes realistic evaluation scenarios for AI agents that use external tools, using only the tool’s specification (function names, natural‑language descriptions, and typed parameter schemas). Starting from a single Model Context Protocol (MCP) specification, it enriches raw schemas, generates graded scenarios with synthetic tool outputs, and expands them into mock‑data‑grounded multi‑turn dialogues that demonstrate strong tool‑calling correctness and conversational coherence. Across seven diverse MCP specifications, the pipeline achieves high quality, with parameter‑schema complexity emerging as the main driver of quality variation and argument‑value accuracy identified as the dominant failure mode.
arXiv AI
Jun 11
ClawEnvKit: Automatic Environment Generation for Claw-Like Agents
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.