Category
8,685 papers
This suite provides a new efficient lens on generalization: select intermediate pre-training checkpoints that strongly generalize reasoning and alignment, better than the final pre- or mid-training checkpoints, and select pre-training data that controls and stabilizes generalization dynamics.
This work separate factual recall into three steps: generating a correct candidate, ranking the available candidates, and selecting the final answer, and shows that these capabilities do not collapse into a single notion of ``knowing''.
This work proposes an alternative approach for identifying a sentence's past, present, or future global reference interval by encoding sentences using fixed-length inventory vectors that are comprised of part-of-speech counts, dependency relation counts, and explicit futurate pattern counts.
This work shows that LLM-as-Judge is operationalized on cloud serving infrastructure, not of any particular model family, and proposes a minimal, low-cost reporting protocol: several judge re-runs, published stability profiles and adjacency intervals, and results under at least two judges from different families.
Reach audiences
Advertise in front of researchers, engineers, and readers.
This work measures knowledge capacity, the factual bits stored per parameter, on synthetic biographies with known information content and suggests judging low-precision training by retained capacity during training rather than final loss.
An audit of SycEval, which reports that objections raised before a model answers (preemptive) cause more caving than those raised after (in-context), and attributes the gap to timing, and builds the missing comparison and tests it on multiple-choice questions and SycEval's own free-form pipeline.
Experimental results show that ARSM maintains the task performance while simultaneously reducing token consumption, offering a practical, cost-effective route toward scalable autonomous agents for long-horizon tasks.
This work proposes A-B-D to infer traits bottom-up from behavioral data (B-data), namely how agents act on their environment and communicate with users, as recorded in existing trajectories, and offers a new lens for understanding AI personality.
Large Language Models (LLMs) increasingly participate in writing by modifying or extending human drafts, causing machine involvement to vary in both form and extent. Yet most Machine-Generated Text (MGT) detectors are evaluated only on fully human-written versus fully AI-generated text. Because human--AI collaboration...
This work proposes an inference-time, plug-and-play Self-Contrastive Steering (SCS) method that constructs unconditional general text embeddings and uses them to refine conditional text embeddings, making them more focused on the target condition.
Rep2Act, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions, is introduced, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions.
This paper proposes ExpVoyager, a novel framework in which a skill curator navigates raw experience across different views and resolutions, continually identifying reusable procedural knowledge from what it observes while tracking remaining knowledge needs that guide where to navigate next.
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.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
