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1,065 papers
A benchmark score is a joint property of the model, the evaluation harness, the elicitation budget, the sampled population, and contamination status. Leaderboards publish the model and the score, so capability and leakage stay observationally equivalent. Existing taxonomies classify contamination for automated detectio...
This work proposes a secure multi-party computation framework that enables the training and inference of CellCnn entirely on secret-shared data, and preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline.
CallosumNet is a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure that reconstructs subgraphs using biologically-inspired virtual edges and restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer.
This work formalizes resulting Hessian collection as a partially symmetric decomposition to establish conditions for local identifiability and stability to exploit vector-output stencil reuse to reduce the structural query cost by a factor of 16.
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Five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics are presented, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
Securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.
SRE-Bench is introduced, the first realistic, contamination-free RE benchmark, and results indicate that strong source-code security capabilities do not yet transfer to binary analysis, highlighting RE as an important frontier for agentic cybersecurity and SRE-Bench as a rigorous testbed to measure progress.
This work presents a security-oriented framework for risk identification, evaluation, and mitigation in a multi-agent GIS system while maintaining adaptability to broader agentic architectures.
A trust-aware post-routing framework is proposed that reweights clients using returned-evidence feedback, including retrieval relevance, profile consistency, and cross-client agreement, and online experiments show that it suppresses persistent hijacking over recurring queries and transfers to a learned neural router.
BEACON is an LLM-driven framework for cross-source CTI knowledge graph construction that constructs and releases two human-annotated datasets from 34 sources and outperforms all baselines by at least 23% and 9%, respectively.
CamoDocs is proposed, a poisoning attack that avoids direct query inclusion by camouflaging adversarial documents among benign content, and shows that erasure-heavy clustering defenses such as TrustRAG can reduce ASR, but only with substantial utility drops on retrieval-dependent benchmarks such as NeoQA.
This work forms sector-targeted CTI dissemination as a multilabel classification problem, leveraging deep field knowledge of CTI structures and sector-specific threat patterns, and applies BERT, a transformer-based model, to automate the mapping of CTI events to sectors.
Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
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