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What do CISOs need to rest easy about future AI risks?
IANS survey of 113 CISOs finds optimism about managing future AI security risks hinges more on organizational readiness and leadership support than current controls.
An IANS AI Security Survey of 113 CISOs fielded in April and May found 41% optimistic and 38% pessimistic about their organization's ability to manage AI security risks over the next 24 months. Six organizational signals—leadership understanding of AI risk, defined governance ownership, security team effectiveness with AI tools, CISO ownership of the AI-security budget, sustainable workloads, and staffing—separate confident CISOs from pessimistic ones. Interviewed analysts cautioned that these readiness signals measure self-assessed confidence rather than actual security, and recommended hands-on AI use by security teams plus governance of third-party models.
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Bridging the Gap Between Homogeneous and Heterogeneous Asynchronous Optimization Is Surprisingly Difficultnew
Lower bounds show heterogeneous asynchronous optimization cannot match homogeneous rates under standard similarity assumptions; strong interpolation plus local PL condition closes the gap.
The paper examines whether pessimistic optimal time complexities for asynchronous distributed optimization with heterogeneous workers (different data distributions) can be overcome. It proves improvement is provably impossible under widely used first- and second-order similarity assumptions for any randomized algorithm, and that the weak interpolation assumption alone is also insufficient. Combining strong interpolation with the local Polyak-Lojasiewicz condition yields a new time complexity bound matching the best-known homogeneous dependence on worker computation times without requiring identical data distributions.
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Bellman Policy Optimization
Bellman Policy Optimization, a critic-free RLVR method derived from Policy Mirror Descent, improves LLM mathematical reasoning without intermediate state-value estimation.
The paper introduces Bellman Policy Optimization (BPO), a critic-free reinforcement learning method for LLMs with verifiable rewards, derived from Policy Mirror Descent. BPO uses the Bellman equations to reformulate PMD as a trajectory-level objective for autoregressive generation with terminal rewards, avoiding state-value estimation at intermediate states. The authors prove BPO shares the same unique optimal solution as PMD and validate it on mathematical reasoning benchmarks.
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Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models
Drift-Constrained Optimization reformulates fine-tuning as update-direction selection, letting Qwen3 models improve target tasks within a behavioral drift budget.
The paper specifies a behavioral drift budget before optimization and shows that update direction is the remaining degree of freedom, reformulating fine-tuning as a direction-selection problem. In a stringent QA-only setting where instruct models must still generate multi-step reasoning at inference, a coarse layer-selective probe reverses the failure of QA-only fine-tuning. Across Qwen3-8B and Qwen3-14B, these directions substantially improve scientific reasoning and multilingual translation, matching or outperforming dedicated translation systems over 100+ languages and giving stronger initialization for reinforcement learning.
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Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
Theorists prove multi-step lookahead RL planning is NP-hard for every fixed rational discount factor yet give a randomized polynomial-time approximation scheme.
The paper resolves open questions about reinforcement learning with multi-step transition lookahead. It shows exact planning remains NP-hard for every fixed rational discount factor in (0,1), not just discounts arbitrarily close to one, and introduces a randomized polynomial-time approximation scheme for every fixed lookahead depth. Extending to unknown transitions and stochastic rewards via optimism and variance-adaptive confidence bounds, the algorithm achieves cumulative regret matching classical tabular discounted RL up to logarithmic factors.
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Optimizing Credential Blast Radius Through Trust Boundaries and Delegation Under Post-Quantum Authentication Costs
Academic paper models credential blast radius optimization across trust domains under post-quantum latency costs, cutting expected impact by up to 36%.
The paper formulates the joint selection of trust domains and credential-derivation structures under policy and latency constraints as an NP-hard optimization problem, showing the scalarized two-domain direct-issuance case reduces to a weighted minimum cut. In 195 of 230 exhaustive synthetic comparisons, joint optimization produced lower credential blast radius than choosing boundaries first, especially under chained delegation. A trace-derived replay using measured post-quantum authentication costs found the best design reduced expected impact by up to 36% relative to a single domain within the latency budget.
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Netskope acquires Infiot to provide users with optimized connections between any enterprise location
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