State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing. However, their different mechanisms raise an important question: how does contextual representation flow across depth differ between these model families and their hybrids? We ex...
Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen et al.· 0 citations
Grammatical Error Correction (GEC) and feedback play a vital role in supporting second language (L2) learners, educators, and examiners. While written GEC is well-established, spoken GEC (SGEC), aiming to provide feedback based on learners' speech, poses additional challenges due to disfluencies, transcription errors,...
Mengjie Qian, Rao Ma, Stefano Bann\`o et al.· 0 citations
Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptat...
Konstantinos Skianis, John Pavlopoulos, A. Seza Do\u{g}ru\"oz· 0 citations
Reward models (RMs) are a key component of reinforcement learning from human feedback, enabling the alignment of large language models with human values such as helpfulness and harmlessness. However, these alignment goals can conflict, leading to interference when optimised jointly. We refer to this conflict as alignme...
Eshaan Tanwar, Pepa Atanasova· 0 citations
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Latent chain-of-thought compresses reasoning through continuous hidden-state recurrence, but existing approaches are difficult to optimize with standard on-policy reinforcement learning (RL) and hard to analyze mechanistically. We introduce SWITCH, a switchable latent reasoning framework that addresses both challenges...
Jiayu Yang, Chao Chen, Shengen Wu et al.· 0 citations
The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a dominant memory bottleneck for efficient deployment. While the established per-channel quantization effectively accommodates intrinsic channel-wise outliers in Key tensors, its ef...
Zunhai Su, Rui Yang, Chao Zhang et al.· 0 citations
Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilities beyond what parametric memory alone supports. Existing skill-based RL methods assume external skills either accumulate as persistent guidance or internalized into the...
Junhao Shen, Teng Zhang, Xiaoyan Zhao et al.· 0 citations
A distilled student can copy its teacher's mistakes along with its skills. On-policy distillation (OPD), used in recent post-training recipes, has the teacher grade every token of the student's own outputs. Which teacher errors does it pass on? We build ErrOPD, a testbed whose teachers are fine-tuned to close a JSON li...
This work proposes TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology that explicitly captures how tool invocations and environmental responses drive the divergence between effective strategies and failure modes.
Under GRPO training, the hardened judge eliminates the reward-collapse failures (false-positive spikes and length collapse) the authors observe in the unhardened baseline on both MATH and GSM8K at ten seeds per condition.
Upperological Signatures is presented for characterizing the structure of embedding spaces and finds that it outperforms individual metrics in downstream performance prediction and reveals novel insights into representational similarity that challenge the global claims of the Platonic Representation Hypothesis.
Florian Rottach, William Rudman, Bastian Rieck et al.· arXiv.org· 5 citations· ⚡1
Do multilingual language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose color representations into two geometric axes: (P1) alignment to perceptual color space, and (P2) cross-lingual symmetry. Analyzing layerwise embeddings across models and...
Pablo Loyola, Edison Marrese-Taylor, Andr\'es Hoyos-Idrobo· 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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