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7,777 papers
Standard accuracy flatters all ten models the authors evaluate and reorders their ranking, and prompting fixes that look effective largely vanish under paired scoring.
The ability to represent oneself and others as agents with knowledge, intentions, and belief states that guide their behavior - Theory of Mind - is a human universal that enables us to navigate - and manipulate - the social world. It is supported by our ability to form mental models of ourselves and others. Its ubiquit...
Large language models (LLMs) frequently exhibit performance biases against regional dialects of low-resource languages. However, frameworks to quantify these disparities remain scarce. We propose a two-phase framework to evaluate dialectal bias, operationalized as comprehension degradation relative to standard Bengali,...
A three-stage evaluation pipeline is designed that progressively reduces textual guidance to systematically evaluate LALMs' in-context learning ability in the audio modality, uncovering a consistent asymmetry across LALMs.
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IndexRAG is presented, a novel approach that shifts cross-document reasoning from online inference to offline indexing and achieves the best average performance among all evaluated methods, including graph-based baselines such as HippoRAG2 and FastGraphRAG, while relying on a flat vector index.
Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic and extra kernels after the LM head. We present FlashSampling, an exact sampling primitive that fuses sampling into the LM-head matmul and never materializes the logits tensor in H...
SalamahBench is introduced, a unified benchmark of 8{,}270 human-verified harmful prompts across ML Commons hazard categories, each rendered in Modern Standard Arabic (MSA) and five regional Arabic varieties and five regional Arabic varieties, for a total of 49{,}620 paired instances.
This study investigates the diverse characteristics of nouns, focusing on both semantic and morphosyntactic distinctions, and proves the model's representational completeness in learning and prediction and presents a unified framework for understanding the complexities of morphological markings across languages.
This study provides new mechanistic insights into how LLMs internalize OOD challenges, and designs Sparsity-Guided Curriculum In-Context Learning (SG-ICL), a strategy that explicitly uses representation sparsity to schedule few-shot demonstrations, leading to considerable performance enhancements.
A fully automated annotation framework for generating precise molecular descriptions at scale, such that the original molecule can be unambiguously reconstructed from the description alone and the proposed framework and dataset provide a reliable foundation for molecule--language alignment.
Time-Annealed Perturbation Sampling (TAPS) is proposed, a training-free inference strategy that samples nearby conditioning trajectories through time-aware, manifold-constrained perturbations, and consistently improves semantic and lexical diversity across open-ended and instruction-following generation tasks.
Evaluations across nine languages reveal that many low-resource languages suffer large performance drops when tested on digit instantiations different from those in the original test set, and it is found that models robustness in HRL setting do not necessarily translate to LRL.
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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