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Entropy-Regularized Rank-Masked Policy Optimization for Test-Time Reinforcement Learning in Code Generation
Researchers propose ERPO, enabling test-time reinforcement learning for code generation via probe-executed consensus rewards, rank masking, and entropy regularization.
The paper introduces probe-driven test-time reinforcement learning (TTRL) for code generation, where output-free probe inputs are constructed from problem statements and candidate programs are executed on them to compute a Probe Consensus Reward (PCR). Because PCR can be gamed through spurious consensus, the authors propose Entropy-Regularized Rank-Masked Policy Optimization (ERPO), which turns low-PCR outcomes into conservative negative updates via rank masking and constrains policy drift with an entropy ceiling. On coding benchmarks, ERPO substantially improves pass@1 and pass@k in both in-domain adaptation and zero-shot transfer.
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Trust-Aware Adaptive Disclosure for Inference Privacy Preservation in Multi-Agent Networks
Researchers propose a trust-aware privacy framework for multi-agent systems that adapts message disclosure to reduce goal inference attacks.
The cs.CR paper addresses privacy-preserving consensus in networked multi-agent systems where observing adversaries attempt to infer each agent's hidden goal from its messages. A Trust-Aware Privacy Control framework uses a trust-dependent stochastic policy to adapt information release, trading off consensus performance and privacy. Experiments show reduced adversarial goal inference accuracy versus representative baselines while maintaining competitive consensus utility, with relevance to deployments such as healthcare management and smart grids.
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CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search
CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.
The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.
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Decentralized network congestion control for DAG-based distributed ledger system
Researchers propose node-specific variable proof-of-work to curb transaction spamming in DAG-based distributed ledgers, proving a Nash equilibrium enforces prescribed node behavior.
The paper proposes a variable, behavior-based node-specific proof-of-work model for DAG-based distributed ledger networks, where congestion is mainly driven by transaction spamming rather than user growth or token launches. The model grants equal opportunity to stakeholders regardless of computational resources and penalizes nodes issuing more than a prescribed number of transactions. System behavior is modeled as a non-cooperative game over finite network resources, and the authors prove existence of a Nash equilibrium enforcing the prescribed behavior.
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AI is Working in the SOC. So Why are Security Executives More Worried Than Ever?
An Omdia survey of 500 security leaders commissioned by Rapid7 reports 97% positive AI outcomes in the SOC despite heightened executive concerns.
Omdia, commissioned by Rapid7, surveyed 500 security professionals and found 97% reporting positive outcomes from AI in security operations. 98% said AI reduces alert fatigue and 95% said it helps address staffing shortages. Despite this consensus, the report highlights a growing confidence gap and elevated concerns among security executives about where operations are headed.
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