atr0p05/aegis-training-v2.3

AEGIS RevOps Training Dataset v2.3

World-class training data for the AEGIS 3-tier Revenue Operations AI assistant.

v2.3 Improvements (World-Class Release)

New Capabilities

• Computed-Truth Business Math: 600+ examples with verifiable numeric answers (pipeline rollups, win rates, commissions, churn cohorts)
• Adversarial Reasoning: 350+ examples handling contradictory signals, causal traps, and incomplete data
• Noisy Real-World Inputs: 250+ examples with missing values, inconsistent formats, and human shorthand
• Multi-Turn Workflows: 200+ structured conversation flows (pipeline reviews, forecast updates, churn saves)
• Gold Eval Set: 770 held-out examples never used for training

Quality Improvements

• 100% RevOps domain purity (no generic reasoning dilution)
• Deduplication across all splits
• Style compliance validation (Main has structure, Voice is concise, Router outputs intent only)
• Consistent metadata (model_type, category, source, intent)

Dataset Stats

Model	Train	Val	Test	Total
Main (72B)	2,486	292	147	2,925
Voice (7-8B)	1,419	167	84	1,670
Router (0.5B)	2,204	260	130	2,594
Training Total				7,189
Gold Eval				770

Dataset Structure

aegis-training-v2.3/
├── main/
│   ├── train.jsonl
│   ├── val.jsonl
│   └── test.jsonl
├── voice/
│   ├── train.jsonl
│   ├── val.jsonl
│   └── test.jsonl
├── router/
│   ├── train.jsonl
│   ├── val.jsonl
│   └── test.jsonl
└── gold_eval/
    ├── main/eval.jsonl
    ├── voice/eval.jsonl
    └── router/eval.jsonl

Example Schema

Each example contains:

{
  "model_type": "main|voice|router",
  "category": "computed_truth_pipeline|adversarial|...",
  "source": "v23_synthetic|v22_base|...",
  "intent": "voice_simple|crm_lookup|complex_analysis|action_confirm|fallback",
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

Usage

from datasets import load_dataset

# Load training data
main = load_dataset("atr0p05/aegis-training-v2.3", data_dir="main")
voice = load_dataset("atr0p05/aegis-training-v2.3", data_dir="voice")
router = load_dataset("atr0p05/aegis-training-v2.3", data_dir="router")

# Load gold eval (NEVER use for training!)
gold_main = load_dataset("atr0p05/aegis-training-v2.3", data_dir="gold_eval/main")
gold_voice = load_dataset("atr0p05/aegis-training-v2.3", data_dir="gold_eval/voice")
gold_router = load_dataset("atr0p05/aegis-training-v2.3", data_dir="gold_eval/router")

Model Descriptions

Router (0.5B)

• Purpose: Intent classification for query routing
• Intents: voice_simple, crm_lookup, complex_analysis, action_confirm, fallback
• Output: Single intent label
• V2.3 Additions: 500+ adversarial misroute traps, calibration examples

Voice (7-8B)

• Purpose: Fast, conversational responses (1-4 sentences)
• Style: Crisp, offers escalation to Main for complex queries
• V2.3 Additions: 400+ triage/handoff, clarification, noisy handling
• Note: No <think> blocks in training or output

Main (72B)

• Purpose: Complex analysis with chain-of-thought reasoning
• Style: Executive summary → metrics → insights → next steps
• V2.3 Additions: 600+ computed-truth, 350+ adversarial, 200+ multi-turn
• Note: Uses <think> blocks in training; strip at inference

<think> Block Policy

Main model uses internal reasoning blocks:

<think>
[Internal analysis and calculations]
</think>

[User-facing response with executive summary, metrics, recommendations]

At inference: Strip <think>...</think> before displaying to user.

Gold Eval Usage

The gold_eval/ directory contains harder, more realistic prompts for:

• Model selection and comparison
• Regression testing
• Routing policy calibration

CRITICAL: Never include gold_eval in training data.

Categories

Main Categories

• computed_truth_pipeline - Pipeline analysis with verifiable math
• computed_truth_churn - NRR/GRR calculations
• computed_truth_quota - Attainment calculations
• computed_truth_commission - Commission calculations
• computed_truth_winrate - Win rate analysis
• adversarial_contradictory - Contradictory signal analysis
• adversarial_simple - Simple answer traps
• adversarial_incomplete - Incomplete data scenarios
• noisy_input - Messy real-world data handling
• multi_turn_* - Multi-turn workflows

Voice Categories

• triage - Quick lookups and facts
• handoff - Escalation to Main
• action_clarification - High-impact action confirmation
• noisy_handling - Graceful handling of unclear requests

Router Categories

• voice_simple, crm_lookup, complex_analysis, action_confirm, fallback
• adversarial - Misroute trap examples
• calibration - Borderline cases

Version History

Version	Main	Voice	Router	Total	Key Changes
v2.0	~3K	~4K	~2K	9,289	Initial dataset
v2.1	825	1,157	1,734	4,373	Domain filtering
v2.2	2,025	1,307	2,084	5,416	Synthetic expansion
v2.3	2,925	1,670	2,594	7,189	World-class release

Training Recommendations

Phase 1 - Identity Lock-in (2-3 epochs on v2.3)

• Train each model tier on its respective split
• Use gold_eval for early stopping / model selection

Phase 2 - Coverage Expansion (optional)

• Mix with carefully curated additional data
• Monitor for domain drift using gold_eval

License

Apache 2.0