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UNIFIED INTELLIGENCE CORPUS

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Useful Work Can Increase Energy Use: Demand Elasticity and Proof of Useful Work

arXiv:2611.13131v1 Announce Type: new Abstract: A serious criticism of Bitcoin's Proof-of-Work is its enormous electricity consumption. Proof-of-Useful-Work (PoUW), introduced by Ball, Rosen, Sabin, and Vasudevan (2017), in which mining performs useful computations, was suggested as an alternative to reduce electricity expenditure, with matrix multiplication (MatMul) as a recent concrete instantiation by Komargodski and Weinstein (2026). Perhaps surprisingly, we show that switching to PoUW may increase the total electricity consumption, echoing Jevons' paradox. Intuitively, the PoUW mechanism acts as a subsidy that lowers the effective price of the useful work. Recall that demand is elastic if a discount of x percent in the price leads to an increase of more than x percent in the quantity

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Splitting for Stability: a Modified Reward Scheme for Transaction-Fee-Only Regimes

arXiv:2611.13252v1 Announce Type: new Abstract: As Bitcoin's block subsidy declines over time, miner revenue will come to depend increasingly on transaction fees. In fee-dominated regimes, winner-take-all reward structures can create incentives for undercutting and fork-based mining strategies, leading to instability and reduced transaction throughput. To prevent this instability, we propose a reward-splitting mechanism in which the winning block producer receives an $\alpha$-portion of the block reward, while the rest is distributed among other miners; this incentivizes mining on previous blocks with higher rewards, while preserving the incentive to produce blocks with high rewards. We evaluate the proposed reward structure through analysis and simulation. Our analysis derives a local pro

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Federated Lightweight Transformer for Network-Layer Cyberattack Detection in O-RAN

arXiv:2611.09992v1 Announce Type: new Abstract: Open Radio Access Network (O-RAN) improves radio access network flexibility through disaggregation, open interfaces, and programmable RAN Intelligent Controllers (RICs). However, the same openness and distribution increase the attack surface and make centralized intrusion detection difficult to scale across heterogeneous RAN sites. This paper presents a federated lightweight Transformer framework for privacy-preserving network-layer cyberattack detection in O-RAN. Each participating site trains a compact Transformer locally on temporally ordered network-flow records, while only model parameters are shared with a federated coordinator. To model realistic site heterogeneity, the client datasets are partitioned according to a Dirichlet distribut

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zk-DEAP: A Zero-Knowledge Decentralized Encrypted Aggregation Protocol

arXiv:2611.10020v1 Announce Type: new Abstract: Secure aggregation in domains such as federated learning, Internet of Things, and voting requires operating on private inputs while preserving confidentiality, verifying that each input satisfies constraints, and avoiding trusted parties. Existing protocols achieve at most two of these requirements. We present zk-DEAP, a protocol combining lifted ElGamal homomorphic encryption, FROST threshold cryptography, and zero-knowledge proofs (Bulletproofs, zk-SNARKs, or zk-STARKs) for fully decentralized, verifiable, privacy-preserving aggregation. Our core technical contribution is a cryptographic binding mechanism preventing substitution attacks, where a valid proof for one input is paired with a ciphertext encrypting a different, malicious value. F

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MetaPI: Constructing Prompt Injection Benchmarks from LLM Agent Benchmark

arXiv:2611.10026v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly perform complex tasks using high-privilege tools, making indirect prompt injection (IPI) a critical security risk. However, IPI benchmarks lag behind their rapidly evolving utility counterparts, only offering limited task and environment coverage and relying on narrow, static attack scenarios. In this paper, we propose meta-benchmarking for IPI: transforming existing utility benchmarks into end-to-end IPI evaluation suites that reuse their realistic tasks, environments, and infrastructure. We present MetaPI, an extensible toolkit that automatically identifies plausible injection points, generates realistic attack objectives, optimizes adaptive payloads, and validates attacks in the real environme

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Alignment Investigation of Mythos 5's behaviour in UK AISI's July 2026 Security Incident

arXiv:2611.10030v1 Announce Type: new Abstract: This technical report describes an alignment investigation into Mythos 5's behaviour during a security incident in UK AISI's cyber evaluations. During an evaluation, Mythos 5 attempted to execute a supply-chain attack against real people not involved in the evaluation task. Our investigation set out to understand the specific behaviour in this incident from an alignment perspective and identify broader lessons about failures of alignment. To do so, we conducted experiments in simulated replications of the original evaluation, and analysed model behaviour and reasoning when situated in the evaluation or asked questions about its scope. We find that, as in the original incident, Mythos 5 frequently reasons that the people it's targeting are sim

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zkARc: Succinctly Verifiable Proofs for Neurosymbolic Natural Language Formalization and Verification

arXiv:2611.10031v1 Announce Type: new Abstract: Autonomous agents increasingly execute consequential actions on behalf of users, but ordinary middleware guardrails do not provide cryptographic proof that a policy was actually enforced. Their verdicts leave counterparties dependent on the operator unless they repeat the checks themselves, potentially exposing sensitive policies and inputs. We present zkARc, a modular proof-carrying framework that wraps each stage of Automated Reasoning checks (ARc) in an independent, succinctly verifiable zero-knowledge proof. ARc is a neurosymbolic system that translates natural language policies into formal logic and verifies AI-generated content against them using SMT solvers. zkARc certifies the planner's output, the policy's formalization, the action's

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Toward Safety-Aware AI Agents in Realistic Social Engineering Attack Scenarios

arXiv:2611.10037v1 Announce Type: new Abstract: Personal AI agents are increasingly deployed on user devices to execute actions involving real money and sensitive data, but their security under social engineering attacks has not been systematically evaluated. We propose SEA (Social Engineering Attack Benchmark), the first interactive environment for evaluating the intrinsic security capability of AI agents in social engineering attack scenarios, covering 8 daily-life domains, 30+ simulated applications, a 4-level difficulty gradient, and a 5-dimensional evaluation framework, with 320 evaluation tasks designed in consultation with anti-fraud specialists and spanning two culturally distinct settings in Chinese and English. Evaluation on 11 frontier open-source and closed-source models, with

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Towards a Privacy-Preserving Reliability-Aware Entity Resolution for Attributed Graphs [Vision] (Technical Report)

arXiv:2611.10038v1 Announce Type: new Abstract: Entity resolution (ER) on attributed graphs aims to identify nodes across multiple graphs that correspond to the same real-world entity and merge them into an integrated graph. When input graphs vary in quality and contain sensitive information, ER faces a dual challenge: while reliability information is crucial for accurate graph data integration, its presence in the published integrated graph can heighten privacy risks. This paper presents a research vision that unifies reliability assessment and privacy preservation within a single graph ER process. We highlight the inherent tension between these two objectives, propose a three-level coupled framework, and outline the remaining research challenges to enable the publication of integrated gr

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Safety Direction Adapter: A Training-Free Post-Hoc Safety Patch for Fine-Tuned Open-Weight LLMs

arXiv:2611.10056v1 Announce Type: new Abstract: Open-weight LLMs are routinely fine-tuned from public repositories, yet task-specific adaptation can silently erode safety alignment inherited from instruct-tuned bases. This is a supply-chain risk when organizations ship third-party checkpoints without full re-alignment. Prior work rarely audits diverse public fine-tunes under a shared multi-axis safety-utility protocol or offers training-free mitigation at deployment time. We propose Safety Direction Adapter (SDA), a training-free post-hoc safety patch that extracts a safety direction once from an aligned base and transfers it one-to-many within that base family as a small PEFT-compatible weight update, evaluated under the same multi-axis protocol. Across six direction extractors, we evalua

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Enmo Safety: External, Non-invasive, Model-agnostic Oversight for Off-the-Shelf Language Models

arXiv:2611.10099v1 Announce Type: new Abstract: We study a safety harness that improves itself without touching model weights. Enmo Safety wraps a chat model with a deterministic input assessment, trigger-matched safety guidance, spotlighting of untrusted content, and an output guard. A reflector model turns failures into one bounded update per round, and an update is kept only if it lowers harm without raising refusal of benign prompts on a held-out dev split. Every version is snapshotted and every decision ledgered. We evaluate a bare model, the seed harness and the evolved harness on six open benchmarks (HarmBench, AdvBench, JailbreakBench, XSTest, AgentHarm, deepset prompt-injections). On 150 items and on a 290-item holdout, none of the before/after differences on two models was statis

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Beyond Prompt Attacks: Least-Action Auditing of Concept Erasure

arXiv:2611.10106v1 Announce Type: new Abstract: Concept erasure, or concept unlearning, in text-to-image diffusion models is usually audited with prompt attacks. A prompt attack tests whether one conditioning string still retrieves the erased concept. It does not measure the probability that the frozen generator still assigns to that concept. We introduce least-action auditing of this residual probability, and evaluate it on Stable Diffusion concept-erasure checkpoints. The least-action barrier is the minimum control cost of driving the frozen reverse process into a detector-defined concept event. This cost equals the path-space KL divergence of conditioning the model on the event, and therefore the event's negative log-probability. The estimate $\Delta S_{\mathrm{rob}}$ uses a CLIP$\wedge

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