BluesFL uses block-level instruction-oriented slicing with LLMs to localize 24 bugs at Top-1 in a 19K-line RISC-V processor, a 242.9% gain over prior SOTA of 7 bugs.
Fault localization for hardware design code with time-aware program spectrum
8 Pith papers cite this work. Polarity classification is still indexing.
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Clover fixes 96.8% of bugs on an RTL-repair benchmark using stochastic tree-of-thoughts and neural-symbolic agents, outperforming traditional and LLM baselines by 94% and 63% respectively with 87.5% pass@1.
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
Quantum circuits show high average condition (97.56%) and decision (97.63%) coverage but lower path coverage (71.84%), with probabilistic versions adding confidence levels (averages 88.87%, 88.65%, 37.18%); mutation testing reveals weak or no correlation between structural coverage and fault finding
Pecker reconstructs causal chains in sequential hardware designs via temporal backtracking with Estimated Minimal Propagation Cycles and trace pruning, localizing 51%/80%/85% of bugs in top-1/3/5 ranks on benchmarks.
ApproxHDC extends the HPVM-HDC compiler to automate search for high-impact software and hardware approximations in HDC workloads across heterogeneous backends.
citing papers explorer
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Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
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Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
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Probabilistic Condition, Decision and Path Coverage of Circuit-based Quantum Programs
Quantum circuits show high average condition (97.56%) and decision (97.63%) coverage but lower path coverage (71.84%), with probabilistic versions adding confidence levels (averages 88.87%, 88.65%, 37.18%); mutation testing reveals weak or no correlation between structural coverage and fault finding