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3,317 papers
Despite its compact active-parameter budget, Turing-20B-A2B achieves, at the base-model stage, overall general capability exceeding Qwen3-8B Base and approaching Qwen3.5-9B Base, while maintaining strong long-context performance and favorable prefill-latency scaling.
Results show that medical knowledge updating depends not only on the update algorithm, but also on how knowledge is structured as supervision, and suggest that EMQ gives the most stable external transfer and retention among same-budget SFT variants.
LiteSearch-VL is a low-compute recipe for Qwen3-VL-2B and Qwen3-VL-4B that uses only released OpenSearch-VL trajectories, parameter-efficient LoRA adapters, and synthetic step-level preferences, identifying answer verification, not search depth, as the next bottleneck for small multimodal agents.
Ternary conversion is unsuitable as a drop-in general replacement yet remains valuable as a compact substrate for task-specific models.
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Pro-Router is proposed, a token-aware progressive model routing method with adaptive edge-cloud collaboration for efficient multimodal LLM inference and achieves the highest routing accuracy and improves routing speed by more than 10x.
Three building blocks toward a complete, privacy-preserving browser agent, a verified repair instrument pairing a trilingual seeded-violation benchmark with an audit-inject-verify loop, and a dual-condition protocol that measures harm as carefully as benefit are contributed.
While multi-agent LLM pipelines show real promise for legacy code specifically, since their capacity for autonomous context reconstruction and iterative, feedback-driven refinement directly addresses the sparse documentation and thin test coverage that define legacy environments, the literature to date has been validat...
This study presents the first causally grounded structured methodology for embedding LLMs within the scientific method in SE, designed around the epistemological demands of empirical SE research, establishing a basis for rigorous AI-assisted research.
The findings suggest that AI-powered anomaly detection significantly strengthens observability and security in cloud-based applications, enabling proactive threat mitigation and operational optimization in increasingly complex distributed environments.
The results raise concerns that LLM-assisted evaluation may under-select proposals that human reviewers identify as highly novel, potentially reflecting the statistical logic of next-token prediction trained on past scientific outputs.
A constrained formulation in which the tools that carry class discriminative signal form a mandatory core placed outside the policy’s reach, while the remaining choice minimizes the measured invocation cost, which is subjected to an explicit relative accuracy floor.
The rapid advancement of large language models (LLMs) offers transformative potential for secondary physics education. However, theoretical and practical frameworks for responsible, pedagogically sound LLM integration remain underdeveloped. This paper proposes a structured, theory-grounded framework for embedding LLM...
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A Blog post by IBM Research on Hugging Face
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