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9,049 papers
Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. We intr...
A course intelligence platform deployed across more than 100 universities and serving over 10,000 instructors in China is presented, suggesting that AI-assisted evaluation requires not only capable models but also structured domain knowledge and transparent criteria.
Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues like hallucination, error propagation, and limited context length. To...
A novel defense method called Eraser is proposed, which mainly includes three goals: unlearning harmful knowledge, retaining general knowledge, and maintaining safety alignment, which can significantly reduce the jailbreaking success rate for various attacks without compromising the general capabilities of the model.
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Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This survey argues that topic-independence is not a property of a stylistic feature but of the feature together with its encoding, its scoring rule, and the evaluation split, so the...
The natural language generation domain has witnessed great success thanks to Transformer models. Although they have achieved state-of-the-art generative quality, they often neglect generative diversity. Prior attempts to tackle this issue suffer from either low model capacity or over-complicated architectures. Some rec...
This work introduces Harness-Zero, which enables harness distillation through agent-as-harness, and shows that for frontier LLMs using the same evolved harness, agent-as-harness outperforms code-as-harness.
LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewa...
SLICEChat is introduced, a slide-level MLLM that integrates progressive token pruning within a hybrid Mamba--Transformer slide encoder, which demonstrates accurate and computationally efficient multimodal reasoning over gigapixel WSIs.
Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components such as claims and premises. While research on this subtask remains relatively limited compared to...
HyperQ is introduced, which adds token-conditioned quantum residual branches to a frozen masked-diffusion language model, and support token-conditioned circuit emission as a tractable architectural approach to quantum-augmented language modelling.
The results show where domain specialization and lightweight adaptation offer practical value, and where transformer accuracy justifies higher inference costs, providing an empirical basis for building knowledge systems under resource constraints.
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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.
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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.
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