Parisa Kordjamshidi
Michigan State University
Natural language processing, multimodal reasoning, and neuro-symbolic methods for integrating structured knowledge with neural models.
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SymTrust
Symbolic Methods for Trustworthy and Verifiable Reasoning
A workshop on making AI reasoning more explicit, interpretable, and verifiable.
Workshop question
How can symbolic and structured methods help us improve and verify model reasoning and build systems whose behavior is more reliable, understandable, and trustworthy?
Abstract
About the workshop
Correct answers do not necessarily come from reliable reasoning. Foundation models can produce convincing explanations that fail to faithfully reveal what influenced their outputs, or succeed only under a particular wording or representation.
SymTrust: Symbolic Methods for Trustworthy and Verifiable Reasoning explores how rules, constraints, programs, graphs, knowledge bases, proofs, tables, and diagrams can make reasoning easier to inspect, evaluate, and verify.
We welcome researchers from NLP, trustworthy AI, symbolic and neuro-symbolic reasoning, multimodal reasoning, interpretability, formal verification, and related fields.
Topics
Topics of interest
We welcome archival and non-archival work across methods, theory, datasets, benchmarks, evaluations, applications, position papers, and analyses of model failures.
- Trustworthiness, reliability, and verification of model reasoning
- Faithfulness, validity, plausibility, and consistency of reasoning traces and explanations
- Explainability and interpretability of structured, symbolic, and neural reasoning systems
- Formal, symbolic, and structured reasoning methods, including neuro-symbolic and solver-augmented approaches
- Multimodal reasoning across language, vision, spatial, diagrammatic, and symbolic representations
- Grounding, representation alignment, and reasoning across different modalities and representations
- Robustness and generalization across paraphrases, prompts, representations, domains, and distribution shifts
- Uncertainty, calibration, confidence, and appropriate abstention in reasoning systems
- Metrics, benchmarks, and human evaluation for trustworthy and interpretable reasoning
- Applications, failure analyses, and negative results in high-stakes or real-world reasoning settings
Organizers
Organizing committee
Shreya Rajpal
Michigan State University
Trustworthy AI, explanation evaluation, neuro-symbolic reasoning, and reasoning across language and structured or visual representations.
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