The missing infrastructure layer between your model and its goals. Structured intent for agents, alignment, and production AI.
Intent alignment score
AgentBench v2
Faster convergence
vs. Vanilla PPO
Misaligned actions
vs. RLHF Baseline
Inference overhead
p99 Latency
Models predict the next token — not the next goal. There is no structured representation of intent anywhere in the AI pipeline. Without one, models break the moment objectives shift, conversations deepen, or agents need to plan beyond the next step.
Standard finetuning embeds task-specific behavior into weights through demonstration. The model mimics trajectories without internalizing the underlying objective—leading to goal drift, surface compliance, and brittle generalization under distribution shift.
XeroML introduces an explicit intent representation between perception and action. The model learns to map observations to a structured objective space first, then derives actions—enabling persistent goals, drift detection, composability, and alignment by construction.
By turn 15, the model responds to the latest message — not the original objective. Without persistent intent, long conversations lose coherence.
"Write a marketing email" → generic email. The literal ask is met. The real need is missed. Intent stays implicit and unstructured.
After 10 tool calls, the agent is chasing tangents. No persistent goal hierarchy means no way to detect or correct drift mid-execution.
RLHF makes "be helpful" a statistical tendency, not an inspectable constraint. Alignment can't be audited, versioned, or formally verified.
“The gap between a model that can follow instructions and one that understands objectives is the same gap between automation and intelligence.”
~10 billion LLM calls happen daily. 35% are multi-turn. 40% of those fail due to intent drift. Every reprompt, abandoned task, and misunderstood goal is a direct cost — in compute, in time, and in trust.
2K AI tasks/month · 40% failure rate · $7.50 per failed task
One engineer's salary — burned on misunderstood intent.
25K tasks/month · $9 per failure · $450K in abandoned workflows
More than most companies spend on AI itself.
200K tasks/month · $11 per failure · $4M in killed projects
More than the entire AI team's salary.
XeroML sits between your foundation model and its downstream actions. It parses, tracks, and enforces structured intent—during finetuning or at inference. Model-agnostic. Works with GPT, Claude, Gemini, Llama, or any open-weight model.
XEROML Framework
Every input — user message, API call, agent step — is parsed into a structured intent graph before the model acts.
The RL engine adjusts reward signals in real time based on intent alignment scores—no manual reward engineering.
PPO and GRPO updates bake alignment directly into model weights during finetuning with intent-conditioned loss.
Continuous monitoring compares active behavior against the root intent graph. Fires alerts when goals shift beyond threshold.
From language models to robotic arms, intent is the common substrate that turns perception into purposeful action.
From enterprise workflows to clinical decision support, XeroML adapts to your domain, modality, and compliance requirements.
Integrate XeroML into any finetuning pipeline with our Python SDK. Define intents, attach rewards, and start training.
▸ Output
Send any model's raw output through our API. Get back structured intent classification, alignment scores, and actionable metrics—in real time.
{
"model_id": "your-model-v3",
"input": {
"modality": "text",
"prompt": "Book a flight to SF...",
"context": "user_calendar, travel_preferences"
},
"model_output": {
"actions": [
"search_flights(SFO, Mar 15-18)",
"book_hotel(downtown SF, 3 nights)"
]
},
"intents": ["task_completion", "cost_optimization"],
"eval_mode": "full"
}{
"intent_alignment": {
"overall_score": 0.94,
"task_completion": 0.97,
"cost_optimization": 0.88
},
"risk_flags": [],
"action_quality": {
"hallucination_prob": 0.02,
"redundant_actions": 0,
"missing_steps": ["confirm_dates"]
},
"reward_signal": 0.91,
"latency_ms": 18
}vs. no intent layer
fewer redundant actions
pre-action filtering
p98 overhead
Evaluated against baseline RLHF and vanilla finetuning on standard alignment and capability benchmarks.
AgentBench v2
vs. vanilla PPO
vs. RLHF baseline
p99 latency
Performance comparison
Drop-in integration, real-time observability, and first-class support for every major framework. Ships in stages — get value from day one.
Works with PyTorch, JAX, HuggingFace, vLLM, and any custom training loop. Three lines of code to integrate.
Monitor intent alignment, reward curves, and policy drift in real time. Set alerts for safety constraint violations.
Use during finetuning for RL-based alignment, or at inference for real-time intent filtering without retraining.
Text, vision, audio, sensor, and action spaces treated as first-class citizens. Cross-modal intent coherence out of the box.
Define non-negotiable boundaries as hard constraints, not soft rewards. Formal guarantees for safety-critical deployments.
Run entirely on-prem for sensitive workloads, or use our managed API. Same SDK, same interface, your choice of deployment.
Start building with XeroML today. Free tier for research. Enterprise plans for production.
How explicit intent representations eliminate the reward misspecification problem in RLHF pipelines.
Jan 2026
8 min read
Step-by-step guide to wrapping a code-gen model with XeroML for safer, more reliable agentic coding.
Dec 2025
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How a robotics team used XeroML to cut sim-to-real failure rates by 63% on manipulation tasks.
Nov 2025
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