We approach a cognitive simulation perspective on episodic and semantic memory in multiple-choice question answering by incorporating text from individual text corpora (ITC) into retrieval-augmented generation and DoRA fine-tuning. We web-crawl the search histories of 515 participants who answered 36 multiple-choice kn...
Christoph Wigbels, Ali Abusaleh, M. T. Jansen et al.· 0 citations
This work presents a data-centric pipeline that constructs complementary corpora by mining open-source reasoning traces, distilling financial instruction data, and generating knowledge-graph-guided question-answer pairs from financial educational material and shows that retention-aware adaptation can improve financial...
Zhirayr Hayrapetyan, Andrei Kalmykov, Denis V. Kokosinskii et al.· 0 citations
BuzzASR is introduced, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages, which achieve state-of-the-art CER among open-source systems on 27 of 102 languages on the combined FLEURS and Common Voice test set.
Shivam Singh, Aditya Yadavalli, Catherine Arnett et al.· 0 citations
Speculative decoding is critical for accelerating LLM inference. However, the speedup is fragile: drafters are typically trained against a narrow distribution for a single target model, and their acceptance rate collapses under workload shifts. This is a striking inversion of modern LLM development, where target models...
Fengxiang Bie, Yu-Qing Jian, Yi-Fan Yu et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
StochBench is introduced, a Lean 4 benchmark of 450 graduate stochastic-processes problems at varying abstraction levels, each paired with its natural-language source that better represents domain-specific applied mathematics while remaining challenging for advanced provers.
Idan Davidovich, Debargha Ganguly, Vikash Singh et al.· 0 citations
This work addresses leakage through a learned obfuscate-and-recover scheme that protects participants' private datasets while still allowing an independently deployable model to be trained on the server side, making split-based federated LLM fine-tuning practically viable.
Heng Jin, Chao-Yu Zhang, He-Xuan Yu et al.· 1 citation
Show-Harness, an Embodied Harness that enables VLMs to"play" robots through a compact semantic interface linking intent to action, and develops GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to"play"robots across embodime...
Yan-Zhe Chen, Ze-Chen Bai, Zhi-Jun Cao et al.· 26 citations· ⚡2
This work proposes Forgetting Only What Matters via Unlearning Layers (FOM-UL), a layer-level unlearning framework that selects transformer layers using a forget-to-retain significance score and provides an empirical path toward quantization-resilient unlearning.
Ravi Ranjan, O. Kotevska, Agoritsa Polyzou· 2 citations
Penn Treebank and a 10k-vocabulary stress test confirm that geodesic decoding transfers across corpora and larger |V|, while hyperbolic curvature helps selectively, and characterize boundary collapse in naive hyperbolic recurrence and show how Mobius stabilization restores trainability.
It is concluded that LLM document auditing degrades not gracefully but deceptively, and the harness such systems require: bounded batch sizes, direct content injection, and mechanical verification of every reported finding against source text is outlined.
K. Parekh, Sanjana Pendyala Ravinder, Sana Mhapsekar et al.· 0 citations
When training data are limited, increasing parameter count is not the only way to improve language-model performance. A small parameter set, when repeatedly applied, can also deliver comparable performance. We study Looped GPT-BERT in the BabyLM 2026 Strict-small setting, combining GPT-BERT's masked next-token and caus...
Tingshuo Fan, Hong-Tao Mu, Tian-Yun Zhou et al.· 0 citations
In-vehicle assistants must translate natural-language requests into accurate vehicle function calls under strict memory and latency constraints, making small language models (SLMs) attractive for on-device deployment. For such models, a key design choice is how the available function surface is presented. Two approache...
Hamed Jafarzadeh Asl, Yuanhao Yu, Vahid Partovi Nia· 0 citations
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.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
