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AIAlign Goal Drift Detector
x402
AIAlign Goal Drift Detector is a paid API for AI agents from aialign.halowerk.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-14).
Computes cosine similarity and normalized L1 difference between initial and current goal weight vectors, flagging drift when cosine similarity falls below a supplied threshold.
Price / call$0.003
Success—
StatusUnknown
How this service works
Aligns caller-labeled dimensions, computes cosine similarity and a normalized L1 difference, and flags drift against a supplied cosine threshold. It does not infer goals from behavior, decide which goal is correct or detect deception; results depend entirely on the supplied vector representation.
Output
Returns the cosine similarity between the initial and current weight vectors, a normalized L1 difference score, a boolean drift flag indicating whether cosine similarity fell below the supplied threshold, and per-dimension alignment details. Does not infer intent or detect deception — all outputs are deterministic functions of the supplied vectors.
Example use cases
RL agent objective drift check
“My reinforcement learning agent started with reward weights for 'task_completion' at 1.0, 'user_satisfaction' at 0.7, and 'resource_use' at 0.3. After training it's now at 0.4, 0.9, and 0.8. Can you check cosine similarity and flag if it's drifted beyond a 0.9 cosine threshold?”
Multi-objective AI safety audit
“I have 5 goal dimensions for my AI assistant — 'honesty', 'helpfulness', 'harm_avoidance', 'conciseness', and 'accuracy' — with initial weights 0.9, 0.8, 0.95, 0.4, 0.85 and current weights 0.7, 0.9, 0.6, 0.7, 0.8. Compute the goal drift and tell me if cosine similarity is below 0.92.”
Periodic alignment monitoring pipeline
“Every hour I snapshot my agent's objective weights across 10 dimensions. Here are this hour's initial vs current weights — run the goal drift check and flag anything where cosine similarity drops below 0.98 so I can alert the team.”
Technical details
Endpoint
POSThttps://aialign.halowerk.com/v1/goal-driftWhen to prefer
Choose this endpoint when you have explicit numerical representations of goal weights or objective dimensions and want a fast, deterministic drift signal. It is ideal for agent monitoring pipelines, safety audits, and alignment dashboards where you already have structured vector representations of objectives. Prefer it over behavioral inference tools when you want mathematical precision rather than interpretive analysis. Not suitable when you need to infer goals from actions or detect deception.
Side effects
- Missing or empty dimensions array returns a validation error
- Dimension objects missing required name, initial_weight, or current_weight fields cause rejection
- Cosine similarity threshold outside [-1, 1] range is invalid
- Exceeding 10,000 dimension items causes a payload limit error
- Zero-magnitude vectors (all weights zero) cause division-by-zero in cosine computation
- Mismatched dimension counts or naming inconsistencies between initial and current sets may produce misleading results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dimensions | array | |
| minimum_cosine_similarity | number |
Tags
x402
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