ArbiGraph: Arbitrarily Scalable Verifiable Task Graphs for Evaluating Context Management

2026-07-22 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

ARBIGRAPH is a new benchmark generator designed to evaluate how tool-assisted language agents manage context across extended reasoning workflows. It represents tasks as natural-language problems with executable Python solvers, composing them through typed intermediate states like scalar and list values. This design enables controllable task graphs where length, dependency structure, distractor count, and value type can be varied while ensuring exact automatic verification. The benchmark is instantiated with math, GSM-style word problems, and Python-tracing task categories. Initial evaluations using a Qwen3.5-27B tool-assisted agent across four topologies revealed high accuracy on isolated tasks but a substantial degradation, up to 33.3%, on more complex dependent math tasks, highlighting context management failures not apparent from single-task evaluations. The code, datasets, and results are publicly available.

Key takeaway

For Machine Learning Engineers evaluating tool-assisted language agents, you should move beyond single-task benchmarks. ARBIGRAPH demonstrates that agents like Qwen3.5-27B show significant accuracy drops, up to 33.3%, on complex, dependent reasoning tasks due to context management issues. Incorporate multi-step, verifiable task graph evaluations into your testing protocols to accurately assess an agent's ability to retain, update, and compose context across extended workflows, revealing critical performance gaps.

Key insights

ARBIGRAPH reveals tool-assisted agents struggle with context management in complex, dependent reasoning tasks, despite high single-task accuracy.

Principles

• Context management failures are hidden by single-task evaluations.
• Task graphs expose agent limitations in extended reasoning.
• Varying task graph parameters enables precise evaluation.

Method

ARBIGRAPH generates verifiable task graphs by composing natural-language problems with Python solvers via typed intermediate states, allowing control over graph length, dependencies, and distractor count.

In practice

• Evaluate agents on multi-step, dependent reasoning tasks.
• Use Python solvers for automatic task verification.
• Vary task graph topologies to stress-test context retention.

Topics

• ARBIGRAPH
• Tool-assisted Agents
• Context Management
• Task Graphs
• Benchmark Generation
• Qwen3.5-27B

Code references

• pavelgolikov/ArbiGraph

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.