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7,776 papers
SALMONN-duo is proposed, an adaptive dual-system voice agent inspired by dual-process theories of cognition that separates real-time interaction from deliberative computation by pairing an always-on, fast-thinking full-duplex speech LLM with a powerful asynchronous slow-thinking LLM agent.
MAS-OPD is presented, where Role-Advantage Specialization defines the role advantage as the difference between the teacher signals under target and non-target role conditions, and Privileged Attribution for Coordination attributes an interaction conflict to its source and supplies it to the teacher alone as privileged...
USA is proposed, which converts per-parameter update magnitudes measured during a brief warm-up into per-coordinate perturbation radii, reducing curvature precisely on the coordinates that carry most of the merging displacement.
Among closely related North Germanic languages, the quantification of language distance has traditionally relied on qualitative methods, lacking a unified multi-dimensional computational framework. Multilingual pretrained models based on the Transformer architecture can map texts from different languages into a shared...
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An de-identified corpus of 33 cognitive assessment conversations with 8,250 utterances annotated for three speaker roles and 56 dialogue acts is presented, showing that broad conversational intent is easier to recognize than fine-grained communicative function.
This work investigates whether existing MU algorithms and evaluation tools are suitable for Automatic Speech Recognition (ASR), and shows that both sequential and simultaneous unlearning yield worse privacy and utility than single-subject unlearning.
A two-stage framework that first reconstructs source text from a hidden activation and then evaluates the resulting text with a separate frozen question-answering model, yielding an explicit and inspectable intermediate readout is introduced, enabling compact cross-model inverters to surpass donor-matched question-cond...
Opera is presented, a verbal critic framework that treats each correction as a persistent note, followed until the diagnosed problem is resolved, and achieves the highest mean resolve rate among competitive critic baselines on all three benchmarks.
Conditional Progressive Pruning (CPP) is proposed, a lightweight pruning framework that fully exploits multi-round MAD and is the first to fully outperform consistency methods.
Findings show why decision systems need joint evaluation of accuracy, confidence calibration, and probability coherence in the workflow used by an application.
SlopBench asks which models produce the stiff, repetitive prose readers call AI slop, a question detectors leave open once they have classified a text as machine-written. We evaluated eighteen models on 112 hand-written tasks in email, social posts, essays, and workplace chat, sampling each model on each task up to ten...
This work introduces NSV-Shift, a contrastive benchmark for evaluating whether speech-to-speech models can understand non-speech vocalizations (NSVs) and adapt their responses accordingly, and evaluates five models on NSV perception, emotion understanding, and response adaptation.
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.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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