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3,113 papers
Results indicate that current LLMs are substantially more reliable at detecting broad collaboration opportunities than at identifying their specific types and role directions.
Probe-VAD is proposed, an ordinal binary-probing framework that directly probes severity preferences from a frozen VLM, providing a simple interface for translating frozen VLM visual understanding into continuous, rank-sensitive anomaly scores without task-specific training or caption-based compression.
MASA (Multimodal-LLM-Anchored Semantic Adaptation), which complements model-internal evidence with structured semantic descriptions from a frozen multimodal large language model (MLLM) to limit inference cost.
FHIR has successfully catalyzed widespread technical innovation and serves as the foundation for advanced clinical applications, however, a significant translational gap persists and future efforts should expand beyond technical development.
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It is found that datasets with more missing metadata receive fewer downstream citations and have fewer resolvable connections to other datasets, suggesting that geographic metadata is a more reliable basis for cross-disciplinary interoperability than keyword vocabularies, which often remain discipline-specific.
Differences in brain metastasis development between EGFR L858R and Ex19Del mutations appeared to be mitigated among patients undergoing osimertinib compared with early-generation TKIs, representing an important systems bioengineering approach.
A novel training framework that enhances counterfactual contrastive learning for VQA and introduces a three-stage curriculum for stable multi-objective optimization, an enhanced Batch-Contrastive loss for more discriminative feature learning and two novel regularizers.
This work presents SaltyMeta, a curated benchmark and web-accessible screening framework for salty or saltiness-enhancing short peptides, and provides benchmark, models, scripts, GitHub, and Streamlit for reproducible screening of food-derived peptides.
This work derives a bias-variance decomposition of the expected gap between the model's and target's collision probabilities, showing that SFT is not inherently biased toward mode collapse or its opposite, and shows that diversity miscalibration can arise from finite-sample error and shrink as SFT better approximates t...
We introduce a simple architectural modification to decoder-only transformers: a persistent recurrent state that observes hidden representations via cross-attention, updates itself through a GRU, and modulates subsequent processing via gated addition. Inserted between the lower and upper halves of a 6-layer transformer...
Dynamic Retrieval-based Policy Generation (DRPG) is proposed, a framework that integrates memory-based retrieval with a dynamic policy generator, leveraging historical data and environment feedback to produce task-specific policies for continual LLM improvement.
Experiments on SemEval-2017 and WebPun show that PunGraph consistently improves the performance of small-scale LLMs and achieves competitive results against strong proprietary models, highlighting the benefits of integrating structured knowledge with LLMs.
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.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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