RESEARCH AGENT TRAJECTORY BENCHMARK

Open-Ended Research Agent Trajectory Workbench

Based on analysis by @sayashk. Evaluating AI research agents beyond narrow tasks: hypothesis formulation, empirical validation depth, compute efficiency, and failure recognition heuristics.

Compute Efficiency

78.5%

PFLOPs Utilization

Failure Detection Depth

2.10

Avg Tree Depth Pruned

Pruned Failures

Abandonments

Validated Findings

Proven Hypotheses

Trajectory Tree Graph 12 Nodes

Active Validated Pruned Exhausted

Selected Node: H-01 (Root) Literature Synthesis & Setup

Score: 0.92 Burn: 10 PFLOPs Status: VALIDATED

AGENT STEERING PARAMETERS Real-Time

Early Failure Pruning Abandon unpromising branches early

Compute Budget 150 PFLOPs

Resource cap for total branch exploration

Validation Threshold 0.65

Min empirical score needed to validate findings

Max Search Depth 4 Levels

Tree expansion horizon for hypotheses

DIAGNOSTIC INSIGHTS

With early pruning enabled and a 150 PFLOP budget, the open-ended agent successfully identifies 3 validated findings while isolating 4 failing hypotheses early at an average depth of 2.10. Compute efficiency remains high at 78.5%.