ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation
Quick Answer
This paper shows that ChromaFlow, a tool-augmented autonomous reasoning framework, reveals that increased orchestration does not enhance performance in GAIA 2023 Level-1 tasks, yielding a drop from 54.72% to 50.94% accuracy.
Quick Take
The study emphasizes the necessity for bounded planner escalation and deterministic extraction for reliable .
Key Points
- ChromaFlow achieved 54.72% accuracy on GAIA 2023 Level-1 with a frozen baseline.
- Expanded orchestration configuration resulted in a performance drop to 50.94%.
- Operational noise increased with more aggressive orchestration, including more timeouts and tool failures.
- Two randomized evaluations showed unstable diagnostic gains with 12/20 and 11/20 correct answers.
- The report advocates for first-order requirements in autonomous agent evaluation.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 14102v1 Announce Type: new Abstract: Autonomous language-model agents increasingly combine planning, , document processing, browsing, code execution, and verification loops. These capabilities make agent systems more useful, but they also introduce operational failure modes that are not visible from final accuracy alone. This report presents ChromaFlow, a tool-augmented autonomous reasoning framework built around planner-directed execution, specialized tool use, and telemetry-driven evaluation.
We analyze ChromaFlow on GAIA 2023 Level-1 validation tasks under clean evaluation constraints. A frozen full Level-1 baseline achieved 29/53 correct answers, or 54. 72%. A later recovery configuration with expanded orchestration achieved 27/53 correct answers, or 50. 94%, while increasing tracebacks, timeout events, tool-failure mentions, token-line calls, and campaign-log cost estimates.
Two randomized 20-task smoke evaluations produced 12/20 and 11/20 correct answers, showing that small diagnostic gains can be unstable across samples. The central result is therefore a negative ablation: more aggressive orchestration did not improve full-set performance and increased operational noise. The report argues that bounded planner escalation, deterministic extraction, evidence reconciliation, and explicit run gates should be treated as first-order requirements for reliable autonomous .
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.