arXiv AI
Constraint Decay: The Fragility of LLM Agents in Backend Code Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 20
Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios
arXiv:2604. 06742v2 Announce Type: replace-cross Abstract: The evolution of Large Language Models (LLMs) has catalyzed a paradigm shift towards intent-driven software development, where autonomous agents are expected to design and deliver complete, runnable software systems from scratch.
Hugging Face Trending Papers
Jul 13
BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code. This shift raises a central evaluation question: can an agentic LLM generate an end-to-end software artifact that is both deployable and behaviorally correct under execution?
arXiv AI
Jul 3
A Unified Framework for the Evaluation of LLM Agentic Capabilities
arXiv:2605. 27898v2 Announce Type: replace Abstract: As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential.
arXiv AI
Jul 14
BackendForge: Benchmarking Agentic End-to-End Code Generation with Backend Services
arXiv:2607. 11042v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in agentic coding settings, where they can inspect files, execute commands, run tests, observe failures, and iteratively revise code.
arXiv AI
Jul 22
Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
arXiv AI
Jun 6
ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer
arXiv:2606. 05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance.