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Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have... Activation: LLM, reverse engineering
Breakeven demonstration of quantum low-density parity-check (qLDPC) codes — first experimental evidence that qLDPC codes can achieve fault-tolerance breakeven on trapped-ion quantum hardware. Critical milestone for scalable quantum error correction. Activation: qLDPC, quantum error correction, breakeven, trapped-ion, fault tolerance, quantum coding, logical qubit, error suppression.
Arrovian impossibility theorem for Automated Market Maker (AMM) design. Proves no aggregation rule for weighted-product AMMs can be simultaneously fair and strategy-proof when n>2 liquidity providers. Key result: fairness forces mean-type aggregation (weighted Aitchison centroid) while strategy-proofness forces median-type; only single-provider dictatorship satisfies both. Obstruction vanishes at n=2. Applies to DeFi protocol design, mechanism design, and prediction markets. (arXiv: 2606.04959)
Architecture-aware quantum state preparation using Bucket Brigade QRAM (BBQRAM) with segment tree for polylogarithmic query time. Covers complex-valued matrix encoding, classical precomputation of rotation angles, and magnitude-then-phase procedures. Enables efficient data loading for quantum finance applications. Based on arXiv:2604.25644. Use when: designing QRAM-based quantum data loaders, optimizing state preparation for quantum finance, loading complex-valued financial data into quantum circuits, implementing efficient amplitude encoding with BBQRAM.
Distributional Portfolio Optimization (DPO) unified framework — organizing Bayesian, robust, chance-constrained, stochastic-allocation, and distributional RL portfolio methods through joint coupling Gamma_theta(dw,dr). Includes Wasserstein-CVaR duality, credible-radius calibration, and distributional Bellman contraction. Activation: distributional portfolio optimization, DPO, Wasserstein DRO, Bayesian portfolio, CVaR, credible radius, distributional reinforcement learning.
Critical analysis methodology for quantum data encoding — identifies how naive amplitude encoding (psi=sqrt(P)) abelianizes the Hilbert space and fails to achieve genuine quantum advantage in QML/finance. Advocates for Dynamical Hamiltonian Encoding (DHE) where data generates non-commutative evolution.
Portfolio Optimization with Mean-Variance-Spectrum Preferences
| name | llm-binary-deobfuscation |
| description | Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have... Activation: LLM, reverse engineering |
Deobfuscating binary code remains a fundamental challenge in reverse engineering, as obfuscation is widely used to hinder analysis and conceal program logic. Although large language models (LLMs) have shown promise in recovering semantics from obfuscated binaries, a systematic evaluation of their effectiveness is still lacking. In this work, we present BinDeObfBench, the first comprehensive benchmark for assessing LLM-based binary deobfuscation across diverse transformations spanning pre-compilation, compile-time, and post-compilation stages. Our evaluation shows that deobfuscation performance depends more on reasoning capability and domain expertise than on model scale, and that task-specific supervised fine-tuning consistently outperforms broad domain pre-training. Reasoning models can maintain robustness under severe obfuscation, generalize across different instruction set architectures (ISAs) and optimization levels. In-context learning benefits standard models but yields limited gains for reasoning models. Overall, our study highlights the importance of task-specific fine-tuning and reasoning-driven strategies, and positions BinDeObfBench as a basis for future work in binary deobfuscation.
# Example implementation based on paper methodology
# See original paper for complete details
def apply_methodology():
"""
Apply the methodology from the paper.
"""
# TODO: Implement based on paper specifications
pass
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