Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents
Insights Generator enhances LLM diagnostics with a multi-agent system, improving performance by 30.4pp.
Key Findings
Methodology
The study introduces Insights Generator (IG), a multi-agent system for analyzing large execution trace corpora. IG identifies and validates behavioral patterns through a hypothesis-generation and validation loop. The Scout agent generates initial hypotheses, while the Investigator agent validates them at corpus scale, ensuring reliability of findings.
Key Results
- Human experts using IG reports improved benchmark performance by 30.4pp, significantly higher than the unmodified baseline.
- IG's detection coverage is comparable to competing methods, but domain experts rated its depth and evidence quality as leading.
- IG's scout-investigator architecture produces findings with comparable detection coverage to other methods.
Significance
The study addresses the manual nature of LLM agent diagnostics by systematizing corpus-level diagnostics. IG not only improves diagnostic efficiency and accuracy but also provides more reliable behavioral insights for downstream applications, with significant academic and industrial impact.
Technical Contribution
IG's main technical contribution lies in its innovative scout-investigator architecture, capable of hypothesis validation at large corpus scales. Unlike existing methods, IG separates hypothesis generation and validation processes, providing deeper behavioral pattern analysis.
Novelty
IG is the first to introduce a multi-agent system in LLM diagnostics, differing from traditional single-trajectory debugging methods by enabling pattern discovery and validation at the corpus level.
Limitations
- IG may face computational resource limitations when handling extremely large corpora.
- In certain specific failure modes, IG may require more complex hypothesis generation strategies.
Future Work
Future work could explore IG's application in different domains, particularly optimizing its performance on ultra-large corpora. Further research on automating the hypothesis generation process is also a key direction.
AI Executive Summary
Traditional methods for diagnosing failures in LLM agents rely on manual inspection of a small subset of execution traces, making it difficult to identify patterns across trace populations. The Insights Generator (IG) introduces a multi-agent system that systematically analyzes corpus-level traces to generate evidence-backed insights reports. IG's scout-investigator architecture enhances the efficiency of hypothesis generation and validation, allowing for the identification of behavioral patterns at large corpus scales. Experimental results show that human experts using IG reports improved performance by 30.4pp over the unmodified baseline. IG's detection coverage is comparable to other methods, but its depth and evidence quality are rated as leading by domain experts. This study not only improves diagnostic efficiency and accuracy but also provides more reliable behavioral insights for downstream applications, with significant academic and industrial impact. Future work could explore IG's application in different domains, particularly optimizing its performance on ultra-large corpora. Further research on automating the hypothesis generation process is also a key direction.
Deep Analysis
Background
As LLM agents become more prevalent across various fields, diagnosing failures has become a critical issue. Traditional single-trajectory debugging methods struggle to handle complex patterns in large corpora, especially those that emerge across trace populations. Existing methods often rely on manual inspection, which is inefficient and lacks scalability.
Core Problem
LLM agents generate execution traces with numerous reasoning steps and tool calls, making it difficult to identify systematic patterns across traces manually. This is particularly challenging for errors and performance differences that do not significantly impact aggregate metrics.
Innovation
IG introduces a multi-agent system with a scout-investigator architecture that separates hypothesis generation from validation. The Scout agent generates initial hypotheses, while the Investigator agent validates them at corpus scale. This architecture allows IG to identify and validate behavioral patterns in large corpora.
Methodology
- �� IG uses Scout agents to generate initial hypotheses.
- �� Investigator agents validate hypotheses at corpus scale.
- �� A Python layer processes data, ensuring analysis reliability.
- �� Iterative rounds refine the hypothesis and validation process.
Experiments
Experiments were conducted on the SpreadsheetBench and HLE benchmarks, involving 250 to 400 tasks. A combination of human expert and automated evaluations verified IG's effectiveness in report quality and downstream performance improvement.
Results
IG performed exceptionally in benchmark tests, with human experts using IG reports achieving a 30.4pp improvement. IG's detection coverage is comparable to other methods, but its depth and evidence quality are rated as leading.
Applications
IG can enhance LLM agent diagnostics, especially in scenarios requiring large corpus analysis. Its insights reports can be used to optimize agent performance and reliability.
Limitations & Outlook
IG may face computational resource limitations when handling extremely large corpora. Additionally, in certain specific failure modes, IG may require more complex hypothesis generation strategies.
Plain Language Accessible to non-experts
Imagine a large library with thousands of books. Traditional methods are like a person trying to understand the entire library by reading a few books. IG is like a team, with each member responsible for different books, then pooling information to uncover hidden themes and patterns. This approach is not only more efficient but also capable of discovering details that individuals might miss.
ELI14 Explained like you're 14
Imagine you're playing a complex video game with lots of tasks and challenges. Traditional methods are like trying to understand the whole game by watching a few tasks. IG is like a team, with each member tackling different tasks, then pooling information to uncover hidden rules and tricks. This approach is not only more efficient but also capable of discovering details that individuals might miss.
Glossary
LLM (Large Language Model)
An AI model capable of processing and generating natural language text, commonly used for tasks like conversation and translation.
Core technology for generating execution traces.
Scout Agent
An agent responsible for initial hypothesis generation using LLM-driven summarization and extraction tools.
Key role in IG for generating hypotheses.
Investigator Agent
An agent responsible for validating hypotheses at corpus scale using statistical analysis tools.
Key role in IG for hypothesis validation.
Corpus-Level Analysis
An analysis method focusing on patterns and trends across an entire corpus rather than individual instances.
Core analysis method of IG.
Hypothesis Generation
The process of identifying potential patterns and proposing hypotheses for validation.
First step in IG's analysis process.
Open Questions Unanswered questions from this research
- 1 How to optimize IG's performance on ultra-large corpora?
- 2 How to automate the hypothesis generation process to improve efficiency?
Applications
Immediate Applications
Diagnostics Optimization
IG can enhance LLM agent diagnostics, especially in scenarios requiring large corpus analysis.
Long-term Vision
Intelligent Agent Optimization
Insights from IG reports can further optimize intelligent agent performance and reliability, promoting broader applications.
Abstract
Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterate. This process misses patterns that only emerge across trace populations and does not scale to production corpora where individual traces span tens of thousands of tokens. We formalize the problem of corpus-level trace diagnostics. Given a corpus of execution traces, the goal is to produce grounded natural-language insights that characterize systematic behavioral patterns across trace groups, each linked to supporting evidence. We present the Insights Generator (IG), a multi-agent system that answers diagnostic questions by proposing and testing hypotheses across the trace corpus to produce an evidence-backed insights report. We evaluate IG across qualitative and objective dimensions, spanning rubric-based report assessment and downstream performance improvements achieved by implementing IG insights. Human experts using IG reports improve scaffold performance by 30.4pp over the unmodified baseline scaffold, and coding agents leveraging IG-derived insights show consistent and stable gains. Across benchmarks, IG's scout-investigator architecture produces findings comparable in detection coverage to competing approaches, while domain experts rated IG reports as leading depth and evidence quality.