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8,685 papers
It is shown that harness-aware post-training not only improves in-distribution performance but also enables agents to robustly adapt to OOD settings, highlighting the importance of harness-aware post-training under such shifts.
A post-hoc certification framework forparse autoencoders that provides a practical way to determine whether an SAE representation preserves enough of a frozen LM's predictive behavior to support certification of the original model.
Drop-by-Drop is proposed, a novel multi-bitwidth post-training quantization framework enabling inference-time precision control over LLM weights from a single quantized model, and it is proved that Gaussian sources are successively refinable under a weighted mean squared error distortion motivated by LLM loss functions...
Claw-SWE-Bench is introduced, a unified benchmark that enables researchers to systematically assess the capabilities and efficiency of general-purpose harnesses on software engineering tasks and guide the development of more capable and efficient harnesses.
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EntmaxKV is introduced, an entmax-native sparse decoding framework that exploits sparsity before KV pages are loaded, and a Gaussian-aware entmax selector that estimates the entmax threshold from lightweight page statistics, adapting the selected budget to the score distribution.
Matryoshka Representation Learning (MRL) is a widely adopted approach for training text encoders so they provide useful text representations at various sizes, available by simply truncating the resulting vectors at sizes pre-determined at training time. Recent works have shown that randomly truncating text embeddings h...
This work introduces MulTaBench, a benchmark of 40 datasets, split equally between image-tabular and text-tabular tasks, designed to enable the research of novel architectures which incorporate joint modeling and target-aware representations, paving the way for the development of novel Multimodal Tabular Foundation Mod...
Targeted data selection seeks training examples from a candidate pool that improve downstream task performance. While trajectory-based selectors effectively guide this process, existing approaches typically construct reference states by warming up on the candidate pool itself, thereby inheriting pool-dependent distribu...
Flux Attention is introduced, a context-aware framework that dynamically optimizes attention computation at the layer level by integrating a lightweight Layer Router into frozen pretrained LLMs, which adaptively routes each layer to FA or SA based on the input context.
When a model solves a problem on one attempt and fails it on the next, what separates the two is rarely the final answer token; it is the trajectory that reached it. Contrastive activation steering leaves that signal unused: CAA, SADI, RepE and ITI build their direction from experimenter-supplied text, recorded while t...
As frontier AI systems are pretrained on web-scale data, test set contamination has become a critical concern for accurately assessing their capabilities. While research has thoroughly investigated the impact of test set contamination on discriminative evaluations like multiple-choice question-answering, comparatively...
This work presents AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows and is the only automatically collected and the largest open dataset for data-driven scientific discovery.
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
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