arXiv:2603.24775v1 Announce Type: new Abstract: AI agents increasingly call tools via the Model Context Protocol (MCP) and delegate to other agents via Agent-to-Agent (A2A), yet neither protocol verifies agent identity. A scan of approximately 2,000 MCP servers found all lacked authentication. In our survey, we did not identify a prior implemented protocol that jointly combines public-key verifiable delegation, holder-side attenuation, expressive chained policy, transport bindings across MCP/A2A/HTTP, and provenance-oriented completion records. We introduce Invocation-Bound Capability Tokens (IBCTs), a […]

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arXiv:2603.12469v1 Announce Type: new Abstract: Generating precise diagnostic reports from High-Resolution Computed Tomography (HRCT) is critical for clinical workflow, yet it remains a formidable challenge due to the high pathological diversity and spatial sparsity within 3D volumes. While Video Language Models (VideoLMs) have demonstrated remarkable spatio-temporal reasoning in general domains, their adaptability to domain-specific, high-volume medical interpretation remains underexplored. In this work, we present AbSteering, an abnormality-centric framework that steers VideoLMs toward precise HRCT report generation. Specifically, AbSteering […]

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arXiv:2601.22355v1 Announce Type: new Abstract: We study the problem of quantifying how far an empirical distribution deviates from Gaussianity under the framework of optimal transport. By exploiting the cone geometry of the relative translation invariant quadratic Wasserstein space, we introduce two novel geometric quantities, the relative Wasserstein angle and the orthogonal projection distance, which provide meaningful measures of non-Gaussianity. We prove that the filling cone generated by any two rays in this space is flat, ensuring that angles, […]

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arXiv:2601.12302v1 Announce Type: new Abstract: Batch codes are of potential use for load balancing and private information retrieval in distributed data storage systems. Recently, a special case of batch codes, termed functional batch codes, was proposed in the literature. In functional batch codes, users can query linear combinations of the information symbols, and not only the information symbols themselves, as is the case for standard batch codes. In this work, we consider linear functional batch codes with the […]

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arXiv:2602.20333v1 Announce Type: new Abstract: We present DMCD (DataMap Causal Discovery), a two-phase causal discovery framework that integrates LLM-based semantic drafting from variable metadata with statistical validation on observational data. In Phase I, a large language model proposes a sparse draft DAG, serving as a semantically informed prior over the space of possible causal structures. In Phase II, this draft is audited and refined via conditional independence testing, with detected discrepancies guiding targeted edge revisions. We evaluate our […]

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Blockchain technology is no longer just the backbone of cryptocurrencies; it has become a powerful tool for real businesses. In a world still reeling from supply-chain disruptions and data breaches, blockchain promises to reinvent trust in multi-party systems. As Deloitte notes, “using blockchain can improve both supply chain transparency and traceability” while cutting costs. Kevin Werbach of Wharton puts it succinctly: “Blockchain is not the end of trust. Blockchain is a new structure of trust, a ‘new architecture […]

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The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired content. In response, machine unlearning (MU) — commonly referred to as concept erasure (CE) in diffusion models — has been introduced to remove specific knowledge from model parameters meanwhile preserving innocent knowledge. Despite recent advancements, existing unlearning methods often suffer from excessive and indiscriminate removal, which leads to substantial degradation in the quality of innocent generations. To preserve model […]

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arXiv:2603.20265v1 Announce Type: new Abstract: Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal dumping sites has created a need for efficient detection of waste hotspots. In this work, we introduce JCAS-MARL, a resource-aware multi-agent reinforcement learning (MARL) framework for joint communication and sensing (JCAS)-enabled UAV networks. Within this framework, […]

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Gozzip turns your social graph into your storage network. Instead of relying on servers that decide what gets stored and served, the protocol creates bilateral storage pacts between peers within your Web of Trust. Data lives on your device and your trusted contacts’ devices, verified by cryptographic challenge-response. The architecture formalizes how human communities have always preserved information: reciprocal obligation along trust edges, bounded by Dunbar layers, propagated through gossip. Built on Nostr primitives, bridges to ActivityPub and […]

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