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7,776 papers
GeoOutageBench provides a design principle and foundation for assessing LLM-KG systems that support real-world infrastructure resilience analysis and provides user-configurable evaluation of three important, highly coherent yet less studied tasks.
This work develops a conception of understanding fit for polyphonic AI that centres on sound circuitry that is reliably and correctly recruited and in control of outputs, and can do the work of guiding trust in AI.
This work quantifies the compression cost of boundaries while separating it from the prediction quality of the resulting token units, and introduces boundary licences, which limit the vocabulary entries permitted to cross cuts and admit the same form of certificate.
This work presents a neurosymbolic router that classifies each incoming query and dispatches it to the cheapest correct solver, sending structured tasks to deterministic engines and reserving the small language model (SLM) for open-ended word problems.
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A Wizard-of-Oz study design and supporting real-time system for collecting multimodal behavioral data from native Chinese speakers reacting to social norm violations by German learners.
This paper presents AuditBench, a new benchmark dataset for evaluating the capabilities of LLMs at investigating security-related system audit logs. We design and use this benchmark to explore the performance of LLMs on four log-investigation tasks that incident response teams commonly perform, ranging from triaging al...
This work formalizes agent attribution as a cooperative game, parameterized by the coalition distribution, intervention protocol, and target metric, which provides a principled approach for cost-effective MAS attribution and intervention.
The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deployment. While most videos can be screened through fast pattern recognition, a small subset requires deeper reasoning over temporally complex content and nuanced policy con...
As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is essential. We evaluate LLM-as-a-judge marking across four settings spanning three assessment formats - structured questions, written essays, and scientific plots - comparing GPT-...
ChEmbed is introduced, the first purpose-built family of domain-adapted text embedding models engineered for chemical literature retrieval, and represents a practical, lightweight, and reproducible embedding solution that effectively improves chemical literature retrieval.
The reachability problem in symmetric vector addition systems with states (VASS), where transitions are invariant under a group of permutations of coordinates, is investigated and the gain in complexity when the group arises as a combination of the trivial and symmetric groups is estimated.
D-STT, a simple yet effective defense algorithm that identifies and explicitly decodes safety trigger tokens of the given safety-aligned LLM to activate the model's learned safety patterns, effectively preserves model usability by introducing minimum intervention in the decoding process.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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