Conference Information

NESY 2026: International Conference on Neurosymbolic Learning and Reasoning

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Submission Date:
2026-06-09
Notification Date:
2026-07-16
Conference Date:
2026-09-01
Location:
Lisbon, Portugal
Years:
20
ICORE: C   Viewed: 988   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

52.5 / 100
Ranked #1,142 of 5,693 conferences · Top 21%
Academic recognition (35%)
58
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
82
Community attention (10%)
5
Public record completeness (15%)
35

Inputs used: Listed as ICORE C · Editions on record: 20 · Researchers who opened this page in the past 24 months: 1

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-02

Call For Papers

NESY 2026 (International Conference on Neurosymbolic Learning and Reasoning) is a ICORE C conference held in Lisbon, Portugal on 2026-09-01. The paper submission deadline is 2026-06-09. Acceptance notifications are sent on 2026-07-16.

Subject Areas The NeSy conference invites theoretical, experimental and applied submissions on the integration of neural networks and symbolic AI. We invite papers on all topics related to Neurosymbolic AI, including but not limited to: Informed or physics-aware Machine Learning; Addressing knowledge representation and reasoning tasks using neural networks; Code generation and knowledge engineering with neural networks, including with LLMs; Studying and improving LLM reasoning with Neurosymbolic methods; Neurosymbolic cognitive modelling; Languages for Neurosymbolic AI; Embedding methods for structured information; Circuits and knowledge compilation for Neurosymbolic AI; Specification and verification of machine/deep learning systems; Neurosymbolic AI frameworks; Neurosymbolic methods for reinforcement, causal, structure, transfer, meta, multitask, continual, or relational - learning, and graph neural networks; Applications of Neurosymbolic AI.
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