arXiv:2602.22269v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this work, we aim to understand how these techniques are implemented in quantum-assisted FL. Quantum Secure Aggregation (QSA) has been proposed, offering information-theoretic privacy by encoding client updates into the global phase of multipartite entangled states. Existing QSA protocols, […]

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arXiv:2601.15296v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance, yet existing decoding strategies either explore blindly (random sampling) or redundantly (independent multi-sampling). We propose Entropy-Tree, a tree-based decoding method that exploits entropy as a signal for branching decisions–expanding the search tree only at positions where the model exhibits genuine uncertainty. Entropy-Tree shows superior accuracy and calibration in reasoning tasks: it achieves better pass@k than Multi-chain across multiple models and datasets, and its predictive entropy demonstrates […]

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arXiv:2603.23530v1 Announce Type: new Abstract: Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behaviour through a prospective memory inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2-21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade […]

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arXiv:2602.07186v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems improve LLM reasoning through iterative deliberation, but remain vulnerable to debate collapse, a failure type where final agent decisions are compromised on erroneous reasoning. Existing methods lack principled mechanisms to detect or prevent such failures. To address this gap, we first propose a hierarchical metric that quantifies behavioral uncertainty at three levels: intra-agent (individual reasoning uncertainty), inter-agent (interactive uncertainty), and system-level (output uncertainty). Empirical analysis across several benchmarks reveals […]

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Model-based reinforcement learning (MBRL) achieves high sample efficiency by simulating future trajectories with learned dynamics and reward models. However, its effectiveness is severely compromised in sparse reward settings. The core limitation lies in the standard paradigm of regressing ground-truth scalar rewards: in sparse environments, this yields a flat, gradient-free landscape that fails to provide directional guidance for planning. To address this challenge, we propose Shaping Landscapes with Optimistic Potential Estimates (SLOPE), a novel framework that shifts reward modeling […]

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TLS 1.3 zero-round-trip-time (0-RTT) resumption reduces reconnection latency by allowing clients to transmit early application data using pre-shared keys (PSK) derived from previously established session tickets. This mechanism is pivotal for latency-sensitive web services, API gateways, and IoT applications. However, the cryptographic foundations of current session tickets—symmetric keys derived from classical X25519 key exchange—are fundamentally vulnerable to Harvest-Now-Decrypt-Later (HNDL) quantum attacks: an adversary capturing session ticket exchanges today can retroactively decrypt PSKs and all 0-RTT early data once […]

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Businesses have always had a level of uncertainty to contend with, but 2026 is looking to bring additional challenges that need to be prepared for. Although a lot of businesses already have risk management strategies in place, many are still struggling to deal with any unforeseen issues that arise. Over the next two years, 36% of organizations say they plan to increase their investment in risk management, but what exactly needs to be done to prepare for the […]

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We study the Inverse Contextual Bandit (ICB) problem, in which a learner seeks to optimize a policy while an observer, who cannot access the learner’s rewards and only observes actions, aims to recover the underlying problem parameters. During the learning process, the learner’s behavior naturally transitions from exploration to exploitation, resulting in non-stationary action data that poses significant challenges for the observer. To address this issue, we propose a simple and effective framework called Two-Phase Suffix Imitation. The […]

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