Neuro-symbolic compliance monitors (SMT-solvers enforcing causal DAG constraints derived from pulsar timing data-sharing workflows) will reduce coalition-based deviations in CSIRO Data61’s radio astronomy collaborations by ≥30% compared to unilateral Nash equilibrium enforcement.
Neuro-symbolic compliance monitors (SMT-solvers enforcing causal DAG constraints derived from pulsar timing data-sharing workflows) will reduce coalition-based deviations in CSIRO Data61’s radio astronomy collaborations by ≥30% compared to unilateral Nash equilibrium enforcement.
Adversarial Debate Score
38% 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
- 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 ...
- DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation,...
- Event-Driven Temporal Graph Networks for Asynchronous Multi-Agent Cyber Defense in NetForge_RL
The transition of Multi-Agent Reinforcement Learning (MARL) policies from simulated cyber wargames to operational Security Operations Centers (SOCs) is fundamentally bottlenecked by the Sim2Real gap. ...
- A lower bound on the classical simulation cost of star-network correlations
It is well established that quantum strategies outperform classical ones in several communication tasks. We study the quantum communication complexity of correlations arising from joint measurements o...
- Quantifying Trade-Offs Between Stability and Goal-Obfuscation
Safety-critical autonomy in adversarial settings demands more than Lyapunov stability of tracking error signals. An agent executing a goal-directed trajectory is intrinsically legible to a passive obs...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.