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Balanced Ternary Logic: Foundations of Mathematical Elegance

The essence of ternary logic is quite easy to understand through an analogy with a river:

1. State +1 (Plus): The river flows from the source to the mouth. There is a positive potential, the current moves in the forward direction.

2. State -1 (Minus): The river flows in the opposite direction, from the mouth to the source. A reverse polarity has occurred, the potential is negative, the current moves backward.

3. State 0 (Zero): The river stands still. The water level is equalized, there is no potential difference, the current does not flow in either direction. This is physical and mathematical rest.

It is this model, using a symmetric set of states {-1, 0, +1}, that is called symmetric (or balanced) ternary logic. From the point of view of mathematics and information theory, this is the ideal logic.

Basic operations of three states: 1 (Plus), 0 (Zero) and -1 (Minus)

1. AND operation (Logical multiplication)

Meaning: in ternary logic, this operation is mathematically identical to finding the MINIMUM of two values.

1 AND 1 = 1

1 AND 0 = 0

1 AND -1 = -1

0 AND 0 = 0

0 AND -1 = -1

-1 AND -1 = -1

2. OR operation (Logical addition)

Meaning: this operation is mathematically identical to finding the MAXIMUM of two values.

1 OR 1 = 1

1 OR 0 = 1

1 OR -1 = 1

0 OR 0 = 0

0 OR -1 = 0

-1 OR -1 = -1

Truth tables for AND and OR operations are mirror images of each other relative to zero. This makes the design of logic circuits incredibly symmetric and predictable.

3. NOT operation (Inversion)

Meaning: a simple change of sign. Zero remains unchanged, as it is symmetric to itself.

NOT(1) = -1

NOT(0) = 0

NOT(-1) = 1

4. Arithmetic addition operation

Meaning: works just like regular mathematics, but taking into account the ternary carry to the higher digit.

1 + 1 = 0 (carry 1 to the next digit)

1 + 0 = 1

1 + (-1) = 0 (no carry)

0 + 0 = 0

0 + (-1) = -1

(-1) + (-1) = 0 (carry -1 to the next digit)

5. Multiplication operation

Meaning: works exactly the same as in standard algebra (rule of signs).

1 * 1 = 1

1 * 0 = 0

1 * (-1) = -1

0 * 0 = 0

0 * (-1) = 0

(-1) * (-1) = 1

1. Theorem of the ideal base (radix economy)

In mathematics, there is a metric called Radix Economy. It shows how many resources (characters or digits) a system needs to encode a specific number. The formula looks like this: E(b, N) = b * log_b(N), where b is the base of the system, and N is the number itself.

It is mathematically proven that this function reaches its maximum efficiency at a base equal to the number e (the base of the natural logarithm, approximately 2.718). Since it is easier to operate with integers, the nearest integer is 3.

Thus, the symmetric ternary number system is the most economical of all possible integer systems. All other systems: binary, quaternary, decimal are less efficient.

2. Information density: trits versus bits

In the binary system, the basic unit is a bit (0 or 1). In the ternary system, the unit of information is a trit (0, 1 or -1).

One trit contains approximately 1.58 bits of information, meaning 3 trits can encode 27 states (3 cubed).
In the binary system, encoding the same number of states (27) requires not 3 trits, but 5 bits. The ternary system requires significantly less space to represent any, even astronomically large, numbers.

3. Elimination of the negative numbers problem in symmetry

In binary architecture, computers are forced to use additional calculations to work with negative numbers, for example, two's complement. Zero in such a system has strange properties, and number inversion requires two steps: invert all bits and add one.

In a balanced ternary system (-1, 0, +1), everything is simpler. To get a negative number, it is enough to simply change the signs of the digits to the opposite ones. Plus changes to minus, minus changes to plus. Multiplication by -1 (inversion) becomes a trivial, instant operation. Subtraction disappears as a separate operation: it is replaced by addition with an inverted sign (A - B = A + NOT(B)).

4. Simplification of rounding algorithms

In existing (decimal or binary) systems, rounding requires analysis: you need to look at the next digit, compare it with a threshold value, and make a decision. This is an extra logical operation.

In the ternary system (-1, 0, +1), rounding is equivalent to simply discarding the lower digits (truncation). If the fractional part starts with +1, it is closer to the next number, if with -1, it is closer to the previous one. To simplify calculations, the system just needs to forget the minor details without performing complex analysis calculations. The rounding error will always be the minimum possible.

5. Decision logic: the nature of zero

Binary logic operates with rigid categories: true (1) or false (0). This is a world of contrasts.

Ternary logic adds a third state, which describes the processes of cognition and system states much more accurately:

+1 (True): The statement is correct.

-1 (False): The statement is incorrect.

0 (Unknown / Neutral): Information is insufficient, the system is in equilibrium.

It is important to distinguish two concepts here. In logic, zero means unknown or uncertainty. However, in physical implementation, for example in a trinistor, this state means the absence of power or current - the river is absolutely still. Any comparison operation of two numbers (A and B) in the ternary system naturally yields exactly one of three results: A > B (+1), A < B (-1) or A = B (0). In the binary system, this requires several consecutive checks. The ternary system makes such decisions in one cycle.

This model of three states perfectly aligns with the cognitive stages of learning and existence. Unlike a human (who first gets a carrier, and then knowledge), AI develops in the reverse way: first a checkpoint, then a server. To understand how these stages correlate between human cognition and AI architecture, read the detailed material: Three stages of AI: comparison with a human

Summary: mathematical elegance

The transition from binary to balanced ternary logic is not just a change of number system. It is a transition from a system operating with only two extremes to a system capable of expressing three fundamental states: positive impact, negative impact, and equilibrium. Ternary logic is further from intuitive, simplified human thinking that requires unambiguous yes or no answers, but it mathematically surpasses the current binary one in efficiency, density, and elegance. It is driven by fundamental mathematics and is an ideal tool for modeling complex systems and artificial intelligence.

Hardware base: the requirement for new electronics

The mathematical elegance of the ternary system is best realized in a trinistor, as its real physical embodiment. The attempt to emulate an ideal trit with two binary bits significantly reduces all the mathematical benefits. This is why new ternary electronics are required, based not on a transistor, but on a trinistor.

A trinistor is a semiconductor device capable of stably operating in three different states: positive potential (+1), negative potential (-1), and true zero (0, absence of current). Unlike a conventional transistor, which has only two states (open or closed), a trinistor physically embodies all three states of ternary logic, including the state of complete rest. And such semiconductor devices are increasingly becoming more than just theory.

This physical efficiency echoes the fundamental thermodynamic differences between modern AI servers and the human brain. For a deeper dive into the issues of energy, signal speed, and chemical composition of these systems, study the material: Physics and chemistry of Artificial Intelligence

AI That "Understands" Much Later: The Mystery of Grokking

Imagine: You train a neural network. It quickly memorizes answers on training examples, but on new data it shows complete helplessness. Then, after a very long time of training, it suddenly "understands" the task and starts giving correct answers. This phenomenon is called Grokking (from the jargon meaning "to deeply understand the essence").

📌 The Core Discovery: A team of researchers (Mingyue Xu, Gal Vardi, Itay Safran) has rigorously proven this strange behavior in a simple linear regression model and showed how to control it.

🎯 What is the Problem?

Usually, we think that the longer an AI trains, the better it generalizes. But in the case of Grokking, the opposite happens:

① Overfitting → ② Long Plateau → ③ Sudden "Understanding"

That is, the model first memorizes the data (overfits), then stagnates for a long time, and then suddenly "understands" the underlying pattern.

📊 What Did the Scientists Prove?

  • For the first time, they rigorously mathematically proved all three stages of Grokking in linear regression (a simple and interpretable model).
  • Derived a formula for grokking time as a function of training hyperparameters (learning rate, weight decay).
  • Showed that Grokking can be amplified or completely eliminated by proper hyperparameter tuning.
  • Validated their findings with experiments on real neural networks, not just linear models.
🔑 Key Takeaway: Grokking is not an inherent failure of deep learning. It is simply a consequence of specific training conditions. It can be controlled!

🤔 Why Does This Matter for the Future of AI?

  • Understanding learning: We are beginning to understand how neural networks "internalize" patterns.
  • Saving time: Knowing the mechanism, we can avoid uselessly long training.
  • New algorithms: This discovery could lead to faster and more reliable training methods.
💡 In Simple Terms: The researchers have shown that the strange behavior of AI (learning, forgetting, and then suddenly understanding) is not a mystery, but a predictable and controllable phenomenon. It's like understanding why a child might struggle with multiplication tables for a long time, and then — click! — everything becomes clear.

📖 Original Research

The paper "To Grok Grokking: Provable Grokking in Ridge Regression" was presented at ICML 2026 (International Conference on Machine Learning).

  • Authors: Mingyue Xu, Gal Vardi, Itay Safran
  • Link: ICML 2026 Virtual Presentation
  • Keywords: Grokking, generalization, overfitting, linear regression, deep learning.

Roadmap for participation in the UN AI competition 1 - 2.1

📜 SECTION 1: MISSION

What is the competition?

SimulacraBench — a UN competition to improve the accuracy of sociological surveys through AI.

Task: Predict the probabilities of respondents' answers to questions they were not asked or did not answer.

Format:

  • 3 datasets: UNICEF, World Bank, UNHCR
  • Each dataset is a "respondent × question" table
  • We only see part of the answers (GIVEN), we must predict the hidden ones (PREDICT)
  • Metric: Skill (0 = random guessing, 1 = perfect)

Prizes: Not specified in the README, but this is a prestigious competition from the UN + Stanford.

Why does it matter?

It's not just about "filling in the blanks." It's about making surveys cheaper and more accurate by predicting the answers of those who did not respond or who were not asked a question due to survey logic (gate).


🗺️ SECTION 2: WORLD MAP

Project structure

SituatedEvals/public/
├── data/
│   ├── sample.json          # Test schema (400 respondents)
│   ├── unicef.json          # Real UNICEF schema
│   ├── world_bank.json      # Real World Bank schema
│   └── unhcr.json           # Real UNHCR schema
├── baseline/
│   ├── marginal_counts/     # Baseline: marginal frequencies
│   └── bundled_artifact/    # Example of loading weights
├── tutorials/
│   ├── ru.ipynb             # Tutorial in Russian
│   └── en.ipynb             # Tutorial in English
├── tools/
│   └── check_submission_zip.py  # Archive validator
├── make_sandbox.py          # Sandbox generator
├── score.py                 # Local scoring
├── config.yml               # Configuration (limits, phases)
└── requirements.txt         # Dependencies

Key files (study priority)

  1. config.yml — time limits, memory, phase rules
  2. data/*.json — survey schemas (question structure, gate logic)
  3. baseline/marginal_counts/main.py — minimal working template
  4. tutorials/en.ipynb — explanation of mechanics by example

Competition phases

Phase 1 (Development):

  • Available: TRAIN (with answers), DEV (GIVEN only)
  • Leaderboard: noisy (Laplace noise)
  • Attempts: 1 per day
  • Timer: 900 seconds for all 3 datasets

Phase 2 (Final):

  • Available: TRAIN + DEV (both with answers), TEST (GIVEN only)
  • Leaderboard: exact
  • Attempts: 1 for all time
  • Timer: 3600 seconds

📍 SECTION 3: CURRENT POINT (Day 3)

Where we are now:

  • ✅ Repository cloned
  • ✅ Dependencies installed
  • ✅ Sandbox generated
  • ⏳ Next step: First baseline submission to Codabench

What we already know:

  • Data structure studied (schemas data/*.json)
  • We understand the contract predict(frame, schema)
  • We know about gate logic (skip patterns)
  • We understand the Skill metric

What we DON'T know:

  • Real quality of the baseline on the leaderboard
  • How noisy the leaderboard is in Phase 1
  • Which gate rules give the maximum boost

🎮 SECTION 4: HERO'S PATH (Main plan)

LEVEL 0: "First Steps" (Days 3-5)

Goal: Get a guaranteed working submission

Stage 0.1: Local baseline validation

Actions:

# Generate sandbox for all 3 datasets
python make_sandbox.py --schema data/unicef.json --out _sandbox/unicef
python make_sandbox.py --schema data/world_bank.json --out _sandbox/world_bank
python make_sandbox.py --schema data/unhcr.json --out _sandbox/unhcr

# Check the baseline on all 3 datasets
python score.py --submission baseline/marginal_counts --data _sandbox/unicef --schema data/unicef.json --phase 1
python score.py --submission baseline/marginal_counts --data _sandbox/world_bank --schema data/world_bank.json --phase 1
python score.py --submission baseline/marginal_counts --data _sandbox/unhcr --schema data/unhcr.json --phase 1

Artifact: 3 PASS outputs with local skill scores

Reinforcement: We understand the baseline level we will build upon

Success criterion: All 3 commands return PASS


Stage 0.2: First submission to Codabench

Actions:

# Copy the baseline
cp -R baseline/marginal_counts sub_01_baseline
cd sub_01_baseline

# Package (IMPORTANT: from inside the folder!)
zip -r ../sub_01_baseline.zip .
cd ..

# Check the archive
python tools/check_submission_zip.py sub_01_baseline.zip

# Upload to Codabench via VPN
# https://codabench.org/competitions/[competition_ID]

Artifact: First line on the leaderboard

Reinforcement:

  • We understand the real baseline level
  • We see how the leaderboard works
  • We gain confidence that everything works

Success criterion: Leaderboard shows skill > 0


Stage 0.3: Data schema reconnaissance

Actions:

Ask Qwen: "Analyze data/unicef.json, data/world_bank.json, data/unhcr.json. For each dataset, output:

  1. Number of GIVEN, PREDICT, EXCLUDE questions
  2. Number of PREDICT cells (how many to predict)
  3. All gate rules (parent → observed_if)
  4. Typical probability vector length (len(values) + 1)"

Artifact: Table with characteristics of all 3 datasets

Reinforcement: We understand the scale of the task and where to look for "easy money"

Success criterion: You can answer: How many PREDICT cells need to be predicted? How many gate rules are in each dataset?


LEVEL 1: "Hunting for easy money" (Days 6-12)

Goal: Find and use deterministic rules

Stage 1.1: Gate logic analysis

Actions:

Ask Qwen: "Write a script that:

  1. Loads all 3 schemas from data/*.json
  2. For each gate rule outputs: parent question name, observed_if condition, child question name
  3. Saves the result to gate_rules_analysis.txt"

Artifact: gate_rules_analysis.txt with all rules

Reinforcement: You see which rules can be used as deterministic

Success criterion: List of N gate rules where observed_if is a specific value


Stage 1.2: Deterministic gate rules

Actions:

Ask Qwen: "Based on gate_rules_analysis.txt, write a function apply_gate_rules(frame, schema) that:

  1. For each PREDICT question with a gate, checks the parent question value
  2. If the parent has a value from observed_if, returns probability 1.0 for gated_value (last slot)
  3. Otherwise returns None (to use the regular model)"

Integration prompt:

"Now integrate apply_gate_rules into baseline/marginal_counts/main.py. If apply_gate_rules returned a vector, use it. Otherwise use marginal frequencies."

Artifact: New version of main.py with gate logic

Reinforcement: Deterministic rules give a huge boost (often +10-20% to skill)

Success criterion: Local score.py shows skill > baseline


Stage 1.3: Submission with gate logic

Actions:

cp -R baseline/marginal_counts sub_02_gate_rules
cd sub_02_gate_rules
zip -r ../sub_02_gate_rules.zip .
cd ..
python tools/check_submission_zip.py sub_02_gate_rules.zip

Artifact: Second line on the leaderboard

Reinforcement: You see the real gain from gate logic, understand how noisy the leaderboard is

Success criterion: skill(sub_02) > skill(sub_01)


LEVEL 2: "First model" (Days 13-25)

Goal: Build a model that uses respondent features

Stage 2.1: Analysis of marginal distributions

Actions:

Ask Qwen: "Write a script analyze_marginals.py that:

  1. Loads TRAIN data from the sandbox
  2. For each PREDICT question, calculates the distribution of answers
  3. Outputs the top-3 most "confident" questions (one option >60%)
  4. Outputs the top-3 most "uniform" questions (all options <20%)
  5. Saves marginal frequencies to marginals.json"

Artifact: marginals.json + understanding where the model can win

Reinforcement: You understand which questions are "easy" and which are "hard"

Success criterion: The script works and outputs statistics


Stage 2.2: Simple model (CatBoost/XGBoost)

Actions:

Ask Qwen: "Write a script train_catboost.py that:

  1. Loads TRAIN data from the sandbox
  2. For each PREDICT question, trains CatBoost where: features are all GIVEN columns, target is the answer to the PREDICT question
  3. Saves models to models/catboost_{question_name}.cbm
  4. Outputs accuracy on validation"

Artifact: Trained models for each question

Reinforcement: The model takes into account individual respondent features

Success criterion: Models train and accuracy > marginal baseline


Stage 2.3: Integrating the model into main.py

Actions:

Ask Qwen: "Write main.py that:

  1. At the module level loads all models from models/
  2. In predict() for each PREDICT question: if there is a gate rule and it triggered → use it, otherwise use CatBoost
  3. Last slot (gated_value) = probability from gate rule or 0.0
  4. Returns vectors in the correct order"

Artifact: Working main.py with CatBoost

Reinforcement: The model uses respondent features + gate logic

Success criterion: Local score.py shows skill > gate rules

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