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Datasets:
denialkhmbot
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a-s-flc-decisions

Tasks:
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Modalities:
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Formats:
json
Languages:
English
Size:
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Tags:
decision-making
chain-of-thought
asymmetric-reasoning
force-guided
structured-output
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id
stringlengths
8
16
category
stringclasses
17 values
action
stringlengths
3
40
positives
float64
0
8.5
neg_est
float64
1.2
9.5
neg_buf
float64
1.38
11
net
float64
-10.97
7.12
stability
float64
0.8
1
symmetric_reward
float64
-9.5
7.3
asymmetric_reward
float64
-9.65
6.98
buffer_impact
float64
0.15
0.68
num_chains
int64
2
4
travel-001
travel
Fly Singapore to Tokyo with max comfort
8.5
6.2
7.1
1.4
0.9
2.3
2.041
0.259
3
travel-002
travel
Stay local in Bangkok
6.5
4
4.6
1.9
0.9
2.5
2.125
0.375
2
travel-003
travel
Road Trip
8.5
6
6.9
1.6
0.95
2.5
2.013
0.487
2
travel-004
travel
cruise
8.5
4.2
4.83
3.67
0.92
4.3
4.082
0.218
2
travel-005
travel
Budget Airbnbs
8.5
4
4.6
3.9
0.95
4.5
4.26
0.24
2
travel-006
travel
Pay for direct flight
8.5
4
4.6
3.9
0.95
4.5
4.125
0.375
2
travel-007
travel
Buy insurance
8
2
2.3
5.7
0.95
6
5.55
0.45
2
travel-008
travel
Solo travel to Japan
8.5
6
6.9
1.6
0.95
2.5
2.2
0.3
2
travel-009
travel
Save for 3 economy trips
8.5
2
2.3
6.2
0.95
6.5
6.223
0.277
3
travel-010
travel
Rent a car
8.5
6
6.9
1.6
0.9
2.5
2.2
0.3
2
travel-011
travel
Visit in monsoon season
7.5
4
4.6
2.9
0.95
3.5
3.05
0.45
2
travel-012
travel
Airbnb
8.5
2
2.3
6.2
0.95
6.5
6.2
0.3
2
travel-013
travel
Fly via Singapore
8
4
4.6
3.4
0.9
4
3.7
0.3
2
travel-014
travel
Mountain Cabin
8.5
4
4.6
3.9
0.95
4.5
4.275
0.225
2
travel-015
travel
Reschedule trip
8
6
6.9
1.1
0.95
2
1.7
0.3
2
finance-001
finance
High-Yield Savings
8
2
2.3
5.7
0.9
6
5.605
0.395
3
finance-002
finance
Pay off student loan
8.5
2
2.3
6.2
0.9
6.5
6.2
0.3
2
finance-003
finance
Keep 6 months expenses in savings
8
2
2.3
5.7
0.9
6
5.7
0.3
2
finance-004
finance
Refinance Mortgage
8.5
4
4.6
3.9
0.95
4.5
4.05
0.45
2
finance-005
finance
Max out 401k
8.5
2
2.3
6.2
0.99
6.5
6.312
0.188
2
finance-007
finance
Lease
8.2
4.5
5.17
3.03
0.95
3.7
3.422
0.277
2
finance-008
finance
Invest in Index Funds
8.5
2
2.3
6.2
0.95
6.5
6.2
0.3
2
finance-009
finance
Credit Card with 2% Cashback
8.5
1.2
1.38
7.12
0.99
7.3
6.978
0.322
2
finance-010
finance
Term Life Insurance
8.5
2
2.3
6.2
0.95
6.5
6.05
0.45
2
finance-011
finance
Consolidate into personal loan
8
4
4.6
3.4
0.9
4
3.7
0.3
2
finance-012
finance
Invest
8.5
2
2.3
6.2
0.9
6.5
6.312
0.188
2
finance-013
finance
Open 529 college fund
8.5
2
2.3
6.2
0.95
6.5
6.237
0.263
2
finance-014
finance
Max out ESPP
8.5
4
4.6
3.9
0.99
4.5
4.125
0.375
2
finance-015
finance
Pay off debt and invest the rest
8.5
2
2.3
6.2
0.95
6.5
6.2
0.3
2
career-001
career
Remote Startup
8.5
2
2.3
6.2
0.95
6.5
6.312
0.188
2
career-002
career
Attend bootcamp
8.5
6.2
7.1
1.4
0.9
2.3
1.797
0.503
2
career-003
career
Accept Raise
8.5
6
6.9
1.6
0.95
2.5
1.975
0.525
2
career-004
career
Stay for Promotion
8.5
2
2.3
6.2
0.9
6.5
6.312
0.188
2
career-005
career
Freelance full-time
8.5
6
6.9
1.6
0.95
2.5
2.05
0.45
2
career-006
career
Pursue MBA
8.5
6
6.9
1.6
0.9
2.5
2.05
0.45
2
career-008
career
Pivot Product
8
6
6.9
1.1
0.9
2
1.625
0.375
2
career-009
career
Stay as Senior IC
8.5
2
2.3
6.2
0.95
6.5
6.05
0.45
2
career-010
career
Sign the offer as-is
8
2
2.3
5.7
0.9
6
5.55
0.45
2
career-011
career
Contract
8.5
4.2
4.83
3.67
0.92
4.3
4.022
0.277
2
career-012
career
Move abroad
8.5
4
4.6
3.9
0.9
4.5
4.125
0.375
2
career-013
career
Leave for healthier workplace
8
6
6.9
1.1
0.95
2
1.728
0.272
3
career-014
career
Start side business while employed
8.5
4
4.6
3.9
0.95
4.5
4.2
0.3
2
career-015
career
Take demotion to switch industries
8.5
6.2
7.1
1.4
0.9
2.3
2.098
0.202
2
product-001
product
fix onboarding churn
8.2
2.5
2.875
5.325
0.98
5.7
5.441
0.259
3
product-002
product
offer free shipping
8.2
2.5
2.875
5.325
0.92
5.7
5.446
0.254
3
product-003
product
Launch Now
8.5
4
4.6
3.9
0.95
4.5
4.2
0.3
2
product-004
product
Freemium model
8.5
4
4.6
3.9
0.95
4.5
4.237
0.263
2
product-005
product
Use React Native
8
4
4.6
3.4
0.9
4
3.625
0.375
2
product-006
product
Prioritize power users
8.5
2
2.3
6.2
0.9
6.5
6.237
0.263
2
product-007
product
Raise prices 20%
8
4
4.6
3.4
0.9
4
3.7
0.3
2
product-008
product
Integrate Mixpanel
8.2
4.5
5.17
3.03
0.92
3.7
3.422
0.277
2
product-009
product
Open-Source
8.5
4
4.6
3.9
0.95
4.5
4.162
0.338
2
product-010
product
Target enterprise customers
8.5
6
6.9
1.6
0.95
2.5
2.163
0.337
3
product-011
product
Fixing Bugs
8.5
2
2.3
6.2
0.9
6.5
6.2
0.3
2
product-012
product
Pivot to B2B
8.2
4.5
5.17
3.03
0.92
3.7
3.327
0.373
3
product-013
product
Invest in SEO content
8.5
4
4.6
3.9
0.9
4.5
4.125
0.375
2
product-014
product
Dominate one market first
8.5
4.2
4.83
3.67
0.95
4.3
3.978
0.322
2
product-015
product
Hire a designer
8.5
6
6.9
1.6
0.98
2.5
2.125
0.375
2
health-001
health
Home Workout Equipment
8.5
6
6.9
1.6
0.9
2.5
2.2
0.3
2
health-002
health
Intermittent Fasting
8
4
4.6
3.4
0.9
4
3.737
0.263
2
health-003
health
Stick with employer coverage
6.5
4.2
4.83
1.67
0.92
2.3
1.978
0.322
2
health-004
health
Morning workout routine
8.5
2
2.3
6.2
0.9
6.5
6.2
0.3
2
health-005
health
Meal Prep on Sundays
8.5
4
4.6
3.9
0.99
4.5
4.2
0.3
2
health-006
health
Standing Desk
8.5
6
6.9
1.6
0.95
2.5
2.05
0.45
2
health-007
health
Balanced Training
8.5
6
6.9
1.6
0.9
2.5
2.196
0.304
3
health-008
health
Personal Trainer
8.5
6
6.9
1.6
0.9
2.5
2.05
0.45
2
health-009
health
Switch to plant-based diet
8.5
4
4.6
3.9
0.95
4.5
4.162
0.338
2
health-010
health
meditation app
8
1.5
1.72
6.28
0.95
6.5
5.933
0.567
3
education-001
education
Self-taught with projects and certificat
8.5
2
2.3
6.2
0.95
6.5
6.105
0.395
3
education-002
education
Learn Python first
8.5
2
2.3
6.2
0.95
6.5
6.312
0.188
2
education-003
education
hands-on project portfolio
8.5
2
2.3
6.2
0.9
6.5
6.2
0.3
2
education-004
education
Part-time master's
8.5
4
4.6
3.9
0.98
4.5
4.125
0.375
2
education-005
education
Learn AI/ML from scratch
8.5
6
6.9
1.6
0.9
2.5
2.05
0.45
2
education-006
education
MIT OpenCourseWare
8.5
2
2.3
6.2
0.95
6.5
6.05
0.45
2
education-007
education
Study for GMAT
8.5
6
6.9
1.6
0.9
2.5
2.125
0.375
2
education-008
education
Learn Coding
8.5
2
2.3
6.2
0.9
6.5
6.312
0.188
2
education-009
education
Learn Mandarin
8.5
4
4.6
3.9
0.9
4.5
4.312
0.188
2
education-010
education
PhD in Computer Science
8.5
6
6.9
1.6
0.95
2.5
2.2
0.3
2
housing-001
housing
Buy Condo
8.5
4.2
4.83
3.67
0.92
4.3
3.978
0.322
2
housing-002
housing
Renovate current home
8
6
6.9
1.1
0.95
2
1.7
0.3
2
housing-003
housing
Live in the suburbs
6.5
4
4.6
1.9
0.95
2.5
2.163
0.337
3
housing-004
housing
Get a roommate
8
4
4.6
3.4
0.9
4
3.625
0.375
2
housing-005
housing
Fixed-rate mortgage
8
5.5
6.33
1.67
0.95
2.5
2.237
0.263
2
housing-006
housing
Buy in established area
8
6
6.9
1.1
0.9
2
1.7
0.3
2
housing-007
housing
Rent out spare room
7.5
4
4.6
2.9
0.95
3.5
3.125
0.375
2
housing-008
housing
Install Solar Panels
8
6
6.9
1.1
0.92
2
1.625
0.375
2
housing-009
housing
Move to lower cost-of-living city
8.5
4
4.6
3.9
0.9
4.5
4.2
0.3
2
housing-010
housing
15-year mortgage
8.5
4
4.6
3.9
0.95
4.5
4.312
0.188
2
tech-001
technology
Use Go for backend
8.5
4
4.6
3.9
0.95
4.5
4.2
0.3
2
tech-002
technology
MongoDB
8.5
2
2.3
6.2
0.95
6.5
6.2
0.3
2
tech-003
technology
AWS
8.5
2
2.3
6.2
0.95
6.5
6.223
0.277
3
tech-004
technology
Microservices
8
6
6.9
1.1
0.95
2
1.625
0.375
2
tech-005
technology
Buy Linux Laptop
8
2.5
2.875
5.125
0.95
5.5
5.237
0.263
2
tech-006
technology
Use managed RDS
8
4
4.6
3.4
0.9
4
3.625
0.375
2
tech-007
technology
Fine-tune open-source model
8.2
4.5
5.17
3.03
0.92
3.7
3.422
0.277
2
tech-008
technology
Migrate to GraphQL
8.5
6
6.9
1.6
0.95
2.5
1.975
0.525
2
tech-009
technology
Kubernetes
8.5
6
6.9
1.6
0.9
2.5
2.2
0.3
2
tech-010
technology
Build Progressive Web App
8.5
4.2
4.8
3.7
0.9
4.3
3.978
0.322
2
tech-011
technology
TypeScript
8.5
2
2.3
6.2
0.95
6.5
6.312
0.188
2
tech-012
technology
Redis
8.5
2
2.3
6.2
0.9
6.5
6.2
0.3
2
End of preview. Expand in Data Studio

A-S-FLC Decision Dataset

Training data for fine-tuning LLMs on Asymmetric Signed Force-Loop-Chain reasoning.

What is A-S-FLC?

A decision-making framework where:

  • Positives are trusted exactly (known benefits)
  • Negatives are estimated with a conservative buffer proportional to uncertainty
  • Multiple event chains are scored and the highest stable-net path is chosen

This catches "trap" decisions where uncertain downsides are underestimated.

Dataset Details

  • Examples: 806
  • Categories: 2fa_theft, account_takeover, ai_phishing, authority_impersonation, bec, breach_phishing, career, charity_scam, chinese, credit, crisis, daily_life, deepfake_video, deepfake_voice, education, environment, escalation, evil_twin_wifi, fake_app, finance, health, housing, injection, invoice_fraud, khmer, korean, legal, malware, memory, parenting, phishing, pig_butchering, pii_address, pii_dob, pii_email, pii_medical, pii_national_id, pii_passport, product, quishing, relationship, retirement, safe_number, safety, scam, security, sim_swap, smishing, social_engineering, startup, supply_chain, technology, tool, tool_use, travel, urgency_phishing, usb_baiting, verify, vishing
  • Generated by: Llama 3.3 70B via Groq with FG-CoT prompt
  • Format: Each example is a decision query → structured JSON output

Formats

  • asflc_chat_format.jsonl — chat messages format (system/user/assistant)
  • asflc_instruction_format.jsonl — instruction/input/output format (Alpaca-style)

Output Schema 

{
  "chosen_action": "string",
  "breakdown": {
    "positives": "float 0-10",
    "negatives_estimated": "float 0-10",
    "negatives_buffered": "float (with delta buffer)",
    "net": "float",
    "chain_id": "string",
    "events": ["event1", "event2"]
  },
  "all_chains": ["..."],
  "reasoning_steps": ["..."],
  "stability_score": "float 0-1",
  "risk_level": "SAFE | SUSPICIOUS | DANGEROUS (security mode)",
  "threat_type": "string or null",
  "decision_route": "LOCAL | BLOCK | MEMORY_STORE | MEMORY_RETRIEVE | ESCALATE",
  "memory_action": {"op": "store|retrieve|skip", "key": "str", "reason": "str"},
  "knowledge_request": "string or null",
  "escalation_reason": "string or null",
  "source": "small | large_knowledge"
}

Usage 

from datasets import load_dataset
ds = load_dataset("json", data_files="asflc_chat_format.jsonl")

Source

GitHub: denial-web/a-s-flc-llm-enhancer
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