Sink-aware deployment may identify important first-token attention heads before a model is quantized, then reuse that map at the edge. We test when this shortcut is safe for 4-bit NF4 weight-only post-training quantization (PTQ). Our Sink Topology Consistency (STC) metrics separate global rank preservation, top-$k$ set...
This work introduces OmicsBench, the first reasoning benchmark for multi-omics sequences, and tool-augmented on-policy distillation (TA-OPD), a post-training method to align sequence prediction with evidence-grounded biological reasoning.
Jie Ying, Zhe-Fan Wang, Zi-Hong Chen et al.· 0 citations
CTRL is introduced, a framework that decouples semantic reasoning from quantitative prediction, and is explicitly designed to improve robustness under non-stationary temporal dynamics and distribution shift, while remaining competitive on highly stationary time series where adaptive correction provides limited addition...
Minkyoung Kim, D. Ji, Yo-Han Lee et al.· Annual Meeting of the Associ...· 1 citation
Similarity-based retrieval is the dominant rule for conditioning large language models (LLMs) in in-context learning, retrieval-augmented generation, and prompt-based time series forecasting. The rule concentrates on near-duplicate candidates, an issue that has motivated diversity-aware retrieval but remains unexamined...
D. Ji, Minkyoung Kim, Dongkuk Kim et al.· 0 citations
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Mixture-of-Experts (MoE) models are increasingly deployed alongside Speculative Decoding (SD) to accelerate inference, but combining the two is challenging. SD improves the inference speed of dense models by verifying groups of tokens in parallel. However, the inference speedup for SD with MoEs depends heavily on the n...
Kumari Nishu, Han-Byul Kim, Santosh Chilkunda et al.· 0 citations
Results show that persistent strategy experience provides effective guidance for automated fine-tuning on unseen tasks, and propose Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning search cumulative.
Haoran Zhao, Wei Du, Dingwen Yang et al.· 0 citations
CAMFT is proposed, a Conflict-Aware Mergeable Fine-Tuning method that makes task adaptation both efficient and mergeaware and treats mergeability as a property shaped during fine-tuning, rather than only a problem to be solved after fine-tuning.
Jin-Gang Zhou, Hai-Yang Guo, Yuan-Pei Ma et al.· 1 citation
This study asks whether authoritative metadata and execution feedback improve the reproducibility of Eurostat answers produced by a coding agent, and isolates what execution feedback actually contributes.
Toollery is presented, a training-free candidate-compression framework for scalable LLM skill/tool selection that keeps online selection bounded to a compact top-$k$ candidate set and improves recall over ordinary specification retrieval.
INT8 is broadly safe for clinical deployment, while INT4 safety must be assessed per-model and per-task, and that safety alignment is determined primarily by instruction tuning rather than clinical domain adaptation.
EvoRank is presented, an open autonomous ranking engineer: an LLM-guided evolutionary loop that discovers complete Learning-to-Rank pipelines (features, models, losses, ensembles) for multi-objective e-commerce search and is released as a headroom gate that predicts, before any LLM spend, whether the loop will pay off.
Computer-use agents (CUAs) have made rapid progress in completing complex tasks through graphical user interfaces, yet post-training centered on task success alone does not induce reliable safety behavior. A reliable CUA must condition its execution on risk: it should complete ordinary benign tasks, avoid environmental...
Zeng Kang, Zhen-Yun Yin, Yang Zhang et al.· 0 citations
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.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
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.
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