Skip to content

Category

machine learning

17,191 papers

#machine learning Preprint Nov 2026

GatedV: Self-Gated Values for Transformer Language Models

Standard attention forms query (Q), key (K), and value (V) matrices through learned linear projections of input hidden states. Architectures such as GAU and Mega incorporate nonlinear transformations into attention. In this paper, we propose GatedV, a simple attention variant that applies SiLU only to V before aggregat...

Li-Ming Yu, Hong-Yu Zhou · 0 citations
#machine learning Preprint Nov 2026

Seeing Why: Exact Evidence Maps for Quantum and Optical Image Classifiers

When an image classifier decides, we can usually only estimate why. For quantum and all-optical classifiers that act linearly on amplitude-encoded pictures up to the measurement, with no trained classical network before or after them, each class's Born probability above its no-information share, $P_g - b_g$, splits exa...

Teerawat Tongloy, R. Jomtarak · 1 citation
#machine learning Preprint Nov 2026

Write Facts the Way Models Read Them: Lifting the Two-Hop Curse

Legible proposes read-equivalent writing (Legible): one loss term makes the bridge phrase, at the layer where the second hop finishes reading it, match the model's own reading of the new entity, and keeps the same relation's other subjects unchanged.

Zhi-Yun Shi · 0 citations
#machine learning Preprint Open access Nov 2026

How Close to the Theme? Similarity-Label Conditioning for Game Music Generation

Composers develop music from an original idea, controlling how similar each new section is to it, from near repetition to new material. Game music needs this often, as its material repeats over long play. Music models, however, rarely learn to control similarity from real pieces. We further train a pretrained MIDI mode...

Shangxuan Luo, Zixun Guo, Jinjie Shi et al. · 0 citations
#machine learning Preprint Open access Nov 2026

Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA

Reasoning-enabled model derivatives must turn intermediate deliberation into a usable answer within a deployment budget. We study that transition in an Ornith checkpoint adapted to multi-camera driving question answering with verifiable rewards. Paired archived outputs show that, on a selected 400-question development...

T N Rickesh, Pranav Vinodh, Sowmya Kamath S · 0 citations
#machine learning Preprint Open access Nov 2026

Answer Correction with Verifiable Rewards: A Gemma-Family Study of Multi-View Driving QA

Multi-camera driving question answering provides a discrete test of vision-language models, but post-training gains can reflect changed selected answers or improved answer extraction. We investigate this distinction within the Gemma model family using archived, task-linked WaymoQA evaluations. A zero-shot diagnostic on...

T N Rickesh, Pranav Vinodh, Sowmya Kamath S · 0 citations
#machine learning Preprint Open access Nov 2026

No Frames, Just Frequencies: Bandwidth-Efficient Deployment-Time Adaptation for Smart Glasses

Deep neural networks deployed on smart glasses must adapt to changes in the distribution of incoming data while adhering to strict privacy and device constraints. We present No Frames, Just Frequencies, a bandwidth-efficient source-assisted adaptation framework that characterizes distribution shifts using compact frequ...

Francesco Corti, Moritz Becker, Olga Saukh · 0 citations
#machine learning Preprint Open access Nov 2026

Near Minimax Rates for Quantile Distributional Reinforcement Learning

In this paper, we study quantile distributional policy evaluation, where the return distribution under a given policy is approximated by the quantile Bellman fixed point $\bm\eta_m$. We investigate the statistical accuracy of estimating $\bm\eta_m$ across quantile resolutions under Wasserstein distances. Assuming acces...

Zijie Cheng, Zhihua Zhang · 0 citations
#machine learning Preprint Open access Nov 2026

When Do Repetition Loops Lock? Truncation Sampling, Bounded Penalties, and a Blind Spot in DRY

Language models sometimes repeat verbatim until the generation limit. We study the exit rather than the entrance: given that a span has already been repeated $n$ times, when is escape still possible? Along the loop the context is deterministic, so the probability that a loop survives $m$ more repetitions is, under any...

Yichuan Chen, Xin Wang · 0 citations
#machine learning Preprint Open access Nov 2026

SPELUNKA: A Framework for Finding Unanticipated Associations in Vision-Language Models

Vision-Language Models (VLMs) are the backbone of many state-of-the-art multimodal systems, but often encode spurious and harmful associations from their training data. While prior work focuses on removing known biases from VLMs, methods for discovering unanticipated associations for retrieval tasks remain underexplore...

Cassandra Parent, Isha Agarwal, Kumail Alhamoud et al. · 0 citations
#machine learning Preprint Open access Nov 2026

Radial Basis Function Networks as Projection Heads in Self-Supervised Learning

Self-supervised learning (SSL) typically relies on a backbone encoder followed by a small multilayer perceptron (MLP) projection head, which is conventionally discarded after training, while backbone quality is assessed via costly linear probing on labeled data. We argue that this approach including discarding the proj...

Andreas Schliebitz, Heiko Tapken, Martin Atzmueller · 0 citations
#machine learning Preprint Open access Nov 2026

Agent trajectories as programs: fingerprinting and programming coding-agent behavior

Benchmark scores tell you what an agent got right; they do not tell you how it got there. In this work, we introduce methods for comparing agents procedurally in different contexts, where the model, tasks, and approaches vary. We compare ten agents and find that they are identifiable by their behavioral habits, which w...

Hamidah Oderinwale · 0 citations

From tech blogs

See all →
Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.