torchmodal 0.1.0

Differentiable Modal Logic for PyTorch — Modal Logical Neural Networks (MLNNs)

torchmodal

Differentiable Modal Logic for PyTorch

A PyTorch library implementing Modal Logical Neural Networks (MLNNs) — the first framework enabling differentiable reasoning over necessity and possibility by integrating neural networks with Kripke semantics from modal logic.

Installation

pip install -e .

Quick Start

import torch
import torchmodal
from torchmodal import nn, KripkeModel

# Create a 3-world Kripke model with learnable accessibility
model = KripkeModel(
    num_worlds=3,
    accessibility=nn.LearnableAccessibility(3, init_bias=-2.0),
    tau=0.1,
)

# Add propositions
model.add_proposition("safe", learnable=True)
model.add_proposition("online", learnable=False)

# Evaluate modal operators
A = model.get_accessibility()
box_safe = model.necessity("safe", A)       # □(safe) — necessarily safe
dia_online = model.possibility("online", A)  # ♢(online) — possibly online

# Compute contradiction loss
loss = model.contradiction_loss()

Architecture

torchmodal/
├── __init__.py          # Public API
├── functional.py        # Stateless functional operators (like torch.nn.functional)
├── nn/
│   ├── operators.py     # Softmin, Softmax, ConvPool modules
│   ├── connectives.py   # Negation, Conjunction, Disjunction, Implication
│   ├── modal.py         # Necessity (□), Possibility (♢) neurons
│   └── accessibility.py # Fixed, Learnable, Metric accessibility relations
├── kripke.py            # KripkeModel, Proposition
├── losses.py            # ContradictionLoss, ModalLoss, SparsityLoss, CrystallizationLoss
├── inference.py         # Upward-downward bound propagation
├── systems.py           # EpistemicOperator, DoxasticOperator, TemporalOperator, MultiAgentKripke
└── utils.py             # Temperature annealing, accessibility builders, decoding

Core Concepts

Differentiable Kripke Semantics

A Kripke model M = ⟨W, R, V⟩ is realized as differentiable tensors:

• W (Worlds): A finite set of possible worlds — agents, time steps, or contexts
• R (Accessibility): A relation determining which worlds can "see" each other
• V (Valuation): Truth bounds [L, U] ⊆ [0, 1] for each proposition in each world

Modal Operators

Operator	Symbol	Semantics	Implementation
Necessity	□	True in all accessible worlds	softmin over weighted implications
Possibility	♢	True in some accessible world	softmax over weighted conjunctions
Knowledge	K_a	Agent a knows ϕ	□ restricted to agent's row
Belief	B_a	Agent a believes ϕ	□ with non-reflexive access
Globally	G	ϕ at all future times	□ over temporal accessibility
Finally	F	ϕ at some future time	♢ over temporal accessibility

Accessibility Relations

# Fixed (deductive mode): enforce known rules
R = torchmodal.build_sudoku_accessibility(3)
access = nn.FixedAccessibility(R)

# Learnable direct matrix (small worlds)
access = nn.LearnableAccessibility(num_worlds=7, init_bias=-2.0)

# Metric learning (scales to 20,000+ worlds)
access = nn.MetricAccessibility(num_worlds=10000, embed_dim=64)

Loss Functions

# Combined modal loss: L_total = L_task + β * L_contra
criterion = torchmodal.ModalLoss(beta=0.3)
loss = criterion(task_loss, model.all_bounds())

# Sparsity regularization on accessibility
sparse_loss = torchmodal.SparsityLoss(lambda_sparse=0.05)

# Crystallization for SAT mode (forces crisp 0/1 assignments)
crystal_loss = torchmodal.CrystallizationLoss()

Examples

All examples are self-contained scripts in examples/ and can be run directly:

python examples/sudoku.py

Example	Modal Logic	Description
sudoku.py	□, CSP	4x4 Sudoku via modal contradiction + crystallization
temporal_epistemic.py	K, G, F, K∘G	Learns epistemic accessibility to resolve contradictions
epistemic_trust.py	K_a	Trust learning from promise-keeping behavior
doxastic_belief.py	B_a	Belief calibration and hallucination detection
temporal_causal.py	□(cause → crash)	Root cause analysis in event traces
deontic_boundary.py	O, P	Normative boundary learning (spoofing detection)
trust_erosion.py	Temporal + Deontic	Retroactive lie detection collapses trust
dialect_classification.py	□, ♢ thresholds	OOD detection — 89% Neutral recall trained only on AmE/BrE
axiom_ablation.py	T, 4, B axioms	Effect of reflexivity/transitivity/symmetry on structure learning
scalability_ring.py	□, ♢	Ring structure recovery with tau/top-k/learnable ablation

Epistemic Trust Learning (CaSiNo / Diplomacy)

from torchmodal import MultiAgentKripke

# 7 agents (Diplomacy powers), 3 time steps
kripke = MultiAgentKripke(
    num_agents=7,
    num_steps=3,
    learnable_epistemic=True,
    init_bias=-2.0,
)

# Evaluate "agent knows claim is consistent over time"
K_G_claim = kripke.K_G(claim_bounds)

# Learn trust from contradiction minimization
A = kripke.get_epistemic_accessibility()

Sudoku as Constraint Satisfaction

import torchmodal
from torchmodal import KripkeModel, nn

# 81 worlds (cells), fixed Sudoku accessibility
R = torchmodal.build_sudoku_accessibility(3)
model = KripkeModel(
    num_worlds=81,
    accessibility=nn.FixedAccessibility(R),
)

# 9 propositions (digits)
for d in range(1, 10):
    model.add_proposition(f"d{d}", learnable=True)

# Train with contradiction loss + crystallization
contra_loss = torchmodal.ContradictionLoss(squared=True)
crystal_loss = torchmodal.CrystallizationLoss()

POS Tagging with Grammatical Guardrails

from torchmodal import nn, functional as F

# 3-world structure: Real, Pessimistic, Exploratory
box = nn.Necessity(tau=0.1)
access = nn.LearnableAccessibility(3)

# Enforce axiom: □¬(DET_i ∧ VERB_{i+1})
A = access()
det_bounds = ...   # from proposer network
verb_bounds = ...
conj = F.conjunction(det_bounds, verb_bounds)
neg_conj = F.negation(conj)
box_constraint = box(neg_conj, A)  # must be high (true)

Formula Graph Inference

from torchmodal import FormulaGraph, upward_downward

graph = FormulaGraph()
graph.add_atomic("p")
graph.add_atomic("q")
graph.add_conjunction("p_and_q", "p", "q")
graph.add_necessity("box_p_and_q", "p_and_q")

# Initialize bounds
bounds = {
    "p": torch.tensor([[0.8, 1.0], [0.3, 0.5], [0.9, 1.0]]),
    "q": torch.tensor([[0.7, 0.9], [0.6, 0.8], [0.4, 0.6]]),
    "p_and_q": torch.tensor([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]),
    "box_p_and_q": torch.tensor([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]]),
}

# Run inference
A = torch.eye(3)  # reflexive accessibility
tightened = upward_downward(graph, bounds, A, tau=0.1)

Two Learning Modes

Mode	Fixed	Learned	Use Case
Deductive	Accessibility R	Propositions V	POS guardrails, Sudoku, OOD detection
Inductive	Propositions V	Accessibility A_θ	Trust learning, social structure discovery

Citation

If you use torchmodal in your research, please cite:

@misc{sulc2025modallogicalneuralnetworks,
  title={Modal Logical Neural Networks},
  author={Antonin Sulc},
  year={2025},
  eprint={2512.03491},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2512.03491},
}

License

MIT