REVIEW 6 cited by
Specifications: The missing link to making the development of LLM systems an engineering discipline
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This capability has been a cornerstone of past technological revolutions, which relied on combining components to create increasingly sophisticated and reliable systems. Cars, airplanes, computers, and software consist of components-such as engines, wheels, CPUs, and libraries-that can be assembled, debugged, and replaced. A key tool for building such reliable and modular systems is specification: the precise description of the expected behavior, inputs, and outputs of each component. However, the generality of LLMs and the inherent ambiguity of natural language make defining specifications for LLM-based components (e.g., agents) both a challenging and urgent problem. In this paper, we discuss the progress the field has made so far-through advances like structured outputs, process supervision, and test-time compute-and outline several future directions for research to enable the development of modular and reliable LLM-based systems through improved specifications.
Forward citations
Cited by 6 Pith papers
-
Why Do Multi-Agent LLM Systems Fail?
The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
-
AssumptionMiner: Extracting, Tracing, and Revising Implicit Assumptions in LLM Code Generation
AssumptionMiner extracts implicit assumptions from LLM-generated code with a cross-model ensemble F1 of 0.816 on a new 180-task benchmark, and enables assumption-guided targeted regeneration.
-
Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptions
Most MCP tool descriptions (97.1%) contain quality smells, and augmenting them improves agent success by a median of 5.85 percentage points at a 67.46% increase in execution steps.
-
Engineering Robustness into Personal Agents with the AI Workflow Store
AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.
-
Engineering Robustness into Personal Agents with the AI Workflow Store
AI agents require pre-engineered reusable workflows stored in a central repository rather than generating plans on the fly to achieve production-grade reliability and security.
-
Engineering Robustness into Personal Agents with the AI Workflow Store
Position paper advocating a shift from on-the-fly AI agent synthesis to reusable hardened workflows in an AI Workflow Store to improve robustness and security.
Discussion (0). Sign in to comment.