Neurosymbolic Diffusion Models
Neurosymbolic (NeSy) predictors combine neural perception with symbolic
reasoning to solve tasks like visual reasoning. However, standard NeSy
predictors assume conditional independence between the symbols they extract,
thus limiting their ability to model interactions and uncertainty - often
leading to overconfident predictions and poor out-of-distribution
generalisation. To overcome the limitations of the independence assumption, we
introduce neurosymbolic diffusion models (NeSyDMs), a new class of NeSy
predictors that use discrete diffusion to model dependencies between symbols.
Our approach reuses the independence assumption from NeSy predictors at each
step of the diffusion process, enabling scalable learning while capturing
symbol dependencies and uncertainty quantification. Across both synthetic and
real-world benchmarks - including high-dimensional visual path planning and
rule-based autonomous driving - NeSyDMs achieve state-of-the-art accuracy among
NeSy predictors and demonstrate strong calibration.
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Emile van Krieken
claimed_verified
2025-05-21T10:14:44.208Z
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Emile van Krieken
user
HEmile
682da62a975206d1caa8c9da
Pasquale Minervini
claimed_verified
2025-05-21T10:16:57.876Z
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Pasquale Minervini
user
pminervini
682da62a975206d1caa8c9db
Edoardo Ponti
claimed_verified
2025-05-21T10:30:14.820Z
60809ad44ad99100d63ce36a
https://cdn-avatars.huggingface.co/v1/production/uploads/1619040921084-noauth.jpeg
Edoardo Maria Ponti
user
ducdauge
682da62a975206d1caa8c9dc
Antonio Vergari
claimed_verified
2025-05-22T07:17:41.289Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64f6eeb113e91ef83ba82ce8/sJkSu363cEmqHThSBtCOp.jpeg
Antonio Vergari
user
softmarx
Neurosymbolic Diffusion Models
Neurosymbolic (NeSy) predictors combine neural perception with symbolic
reasoning to solve tasks like visual reasoning. However, standard NeSy
predictors assume conditional independence between the symbols they extract,
thus limiting their ability to model interactions and uncertainty - often
leading to overconfident predictions and poor out-of-distribution
generalisation. To overcome the limitations of the independence assumption, we
introduce neurosymbolic diffusion models (NeSyDMs), a new class of NeSy
predictors that use discrete diffusion to model dependencies between symbols.
Our approach reuses the independence assumption from NeSy predictors at each
step of the diffusion process, enabling scalable learning while capturing
symbol dependencies and uncertainty quantification. Across both synthetic and
real-world benchmarks - including high-dimensional visual path planning and
rule-based autonomous driving - NeSyDMs achieve state-of-the-art accuracy among
NeSy predictors and demonstrate strong calibration.
Neurosymbolic diffusion models address limitations of standard neurosymbolic predictors by modeling dependencies between symbols using discrete diffusion, leading to improved accuracy and calibration.
neurosymbolic predictors symbolic reasoning conditional independence discrete diffusion symbol dependencies uncertainty quantification synthetic benchmarks real-world benchmarks high-dimensional visual path planning rule-based autonomous driving state-of-the-art accuracy strong calibration