Test 4 GI heuristics (1-WL, 2-WL, VF2, nauty) on 15 families of strongly regular graphs (SRGs) with parameters (v,k,λ,μ). 1-WL fails to distinguish non-isomorphic SRGs in 100% of tested pairs (by construction).
Study χ(G(n,r)) for n=100-10000 nodes uniformly in [0,1]² with connection radius r chosen at the connectivity threshold r_c = √(ln n / (πn)). Empirically: χ concentrates around c·√(n/ln n) with c=1.
Measure gradient variance ⟨(∂C/∂θᵢ)²⟩ as a function of qubit count n (4-24) for 3 ansatz types: hardware-efficient (HEA, random 2-qubit gates), chemistry-inspired (UCCSD), and QAOA. HEA: variance decays as 2^{-2n} (barren plateau at n≈12).
Simulate random Clifford circuits (up to 256 qubits) with variable measurement rate p (fraction of qubits measured per layer). Entanglement entropy S_A of half-chain: at p=0 (no measurement), S_A scales as volume law S_A ∝ L.
Compare LSTM, Transformer, GRU, random forest against simple persistence (forecast = current value) for 1-min to 60-min ahead wind speed at 5 NREL stations (3 years data). Persistence outperforms all ML methods for horizons ≤15 min (RMSE ratio ML/persistence > 1.
Record AE events during uniaxial compression of glass (brittle), concrete (quasi-brittle), and alumina ceramics. Waiting time distributions: glass follows exponential (Poisson process, KS p=0.
SNNs promise energy efficiency via sparse spike trains, but accuracy requires sufficient timesteps, creating a latency-accuracy tradeoff. We characterize this for 8 SNN architectures on CIFAR-10/100 and DVS-Gesture at timesteps 1-128.
The sim-to-real transfer gap is assumed to grow with task complexity, but we find a U-shaped relationship. Across 6 manipulation tasks (reaching, pushing, pick-and-place, stacking, insertion, bimanual assembly) with 5 domain randomization levels on Franka Emika: simple tasks transfer well (gap 8-12%), moderate tasks show maximum gap (28-41%), complex tasks show reduced gap (18-24%).
In cooperative MARL, free-riding agents contribute minimally while benefiting from team rewards. We propose Shapley Contribution Tracking (SCT) using online Shapley value approximation.
Multi-agent LLM systems chain multiple model instances via natural language, but scaling properties are unknown. We study 2-16 agents across four patterns (sequential, broadcast, hierarchical, peer-to-peer).
Fault-tolerant LLM training requires periodic checkpointing. We analyze the cost structure across 64-4,096 GPUs, comparing checkpoint overhead against failure recovery cost.