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8,054 papers
With this ensemble, frequency matching with a public name list recovers zero identities by alignment across the four corpora; the names it got right were ones the detector missed, left in clear text.
This work proposes Threader, a memory system that shifts the focus from memory construction to efficient, structure-aware access over raw interactions, and preserves them as first-class memory, organizes them into topic-coherent segments via lightweight incremental segmentation, and enables accurate retrieval through m...
CoLMbo-SV, a speaker language model that combines strong speaker discrimination with structured, acoustically grounded comparison reports, substantially advance audio-language speaker verification, bring its accuracy toward that of dedicated speaker encoders while adding checkable acoustic reporting, and establish an e...
MULTISPEECHQA, a large-scale, synthetically generated and human-verified dataset comprising 9200 hours of 10.8 million spoken question-answer pairs in 23 typologically diverse languages, is presented and shows that high-quality synthetic datasets offer a cheap solution to improving the multilingual capabilities of SLMs...
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Test-time scaling and post-training have improved LLM performance in coding and mathematical reasoning, but their effectiveness for individual stance prediction remains unclear. We study this question by predicting a person's stance in a new discussion from their history. We evaluate widely used test-time scaling strat...
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.
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.
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
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
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