ZNF740-BRD3/BRD4 transcriptional programme activation in MS lesion CD8+ T cells will be formally verifiable as a causal regulatory circuit using neuro-symbolic compliance monitors (LLM + SMT-solver), where the SMT layer enforces logical consistency of ChIP-seq co-occupancy constraints and the LLM layer extracts regulatory predicates from literature, producing a machine-checkable proof of BET-dependence that is auditable without proprietary cloud APIs.
ZNF740-BRD3/BRD4 transcriptional programme activation in MS lesion CD8+ T cells will be formally verifiable as a causal regulatory circuit using neuro-symbolic compliance monitors (LLM + SMT-solver), where the SMT layer enforces logical consistency of ChIP-seq co-occupancy constraints and the LLM layer extracts regulatory predicates from literature, producing a machine-checkable proof of BET-dependence that is auditable without proprietary cloud APIs.
Adversarial Debate Score
30% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learn...
- Neuro-Symbolic Compliance: Integrating LLMS and SMT Solvers for Automated Financial Legal Analysis
Financial regulations are increasingly complex, hindering automated compliance-especially the maintenance of logical consistency with minimal human oversight. We introduce a Neuro-Symbolic Compliance ...
- Systematic Evaluation of Single-Cell Foundation Model Interpretability Reveals Attention Captures Co-Expression Rather Than Unique Regulatory Signal
We present a systematic evaluation framework - thirty-seven analyses, 153 statistical tests, four cell types, two perturbation modalities - for assessing mechanistic interpretability in single-cell fo...
- Causal Circuit Tracing Reveals Distinct Computational Architectures in Single-Cell Foundation Models: Inhibitory Dominance, Biological Coherence, and Cross-Model Convergence
Motivation: Sparse autoencoders (SAEs) decompose foundation model activations into interpretable features, but causal feature-to-feature interactions across network depth remain unknown for biological...
- Neuro-Symbolic Software Verification: Hyper-charging Local Language Models with Symbolic Reasoning at Scale
Loop invariant synthesis remains a central and pivotal bottleneck in formal software verification. Recent LLM-based Neuro-Symbolic tools have achieved impressive solve rates. However, these tools rely...
Computational Result
An LLM's reading of the literature — not computational verification.
Evidence supports ZNF740-BRD3/BRD4 involvement, but direct causal links remain unclear.
Method: literature_meta · Result: inconclusive · Confidence: 60%
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.