Offloaded inference for real-world physical AI robotics
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads.
The post Offloaded inference for real-world physical A...
"As a Language Model...": Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It
arXiv:2609.25021v1 Announce Type: new
Abstract: Large Language Models (LLMs) tend to add disclaimers like "I'm just an AI" when asked about something related to themselves. The self-reports from such responses are used in debates about AI safety or self-knowledge of the models, yet what drives them...
Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach
arXiv:2609.25082v1 Announce Type: new
Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-frontier candidate beyond purely classical approaches. Hybrid quantum-classical models operationalize ...
The Probabilistic Structure of Large Language Models
arXiv:2609.25134v1 Announce Type: new
Abstract: This paper presents a probabilistic perspective on large language models (LLMs), developed with the aim of bringing together, in a single self-contained account, tools that are usually treated separately across the literature. LLMs are described throu...
An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Prediction
arXiv:2609.25088v1 Announce Type: new
Abstract: Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these objectives as com- peting, where performant models sacrifice transparency, while interpretable models ac...
Ovis-Embedding: Pushing the Frontiers of Universal Omni-Modal Embeddings
arXiv:2609.25165v1 Announce Type: new
Abstract: In this report, we introduce \textbf{Ovis-Embedding}, a state-of-the-art omni-modal embedding family built on native integration of text, image, video, and audio. Instead of assembling separate modality towers, Ovis-Embedding uses a shared multimodal ...
A new AI control system lets MIT’s tiny flying robot move with insect-like agility, boosting its speed by about 450 percent and allowing it to pull off 10 somersaults in 11 seconds. The technology could eventually enable miniature robots to search earthquake rubble and navigate dangerous spaces that...
PRQuant: Permutation Residual Quantization for Low-Overhead Inference
arXiv:2609.22106v1 Announce Type: new
Abstract: Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rotation, or residual-based approaches, may mitigate this problem, they often introduce new accuracy bott...
arXiv:2609.22107v1 Announce Type: new
Abstract: Making prediction with multimodal data is widely used in diverse scenarios. Existing multimodal fusion models, once deployed, can only handle predefined modalities (e.g., vision, text and audio) and single tasks, making it difficult to quickly adapt t...
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
arXiv:2609.22113v1 Announce Type: new
Abstract: Persistent low retention and completion rates in medications for opioid use disorder (MOUD) have driven the use of machine learning (ML) models to predict retention and identify patients at risk of premature discontinuation. However, the fairness of t...
Didactic knowledge or Clinical Cases? How Data Types Shape Medical Large Language Models
arXiv:2609.22161v1 Announce Type: new
Abstract: Medical large language models are commonly trained on mixtures of didactic data (e.g., textbooks) and clinical data (e.g., patient records), yet how these data types differentially shape model capabilities remains unclear. We address this issue with t...
PAANI : On Device Visual Evidence Fusion and Explainable Guidance for River Robot Simulation
arXiv:2609.22353v1 Announce Type: new
Abstract: Mobile river monitoring robots must interpret obstacles and water boundaries that geographic waypoints alone cannot describe. On resource constrained platforms, converting imperfect visual predictions into timely and inspectable guidance is a distinct...
Learning 3D biophysical cell properties from 2D images and cell-population statistics
arXiv:2609.22410v1 Announce Type: new
Abstract: Inferring 3D cellular properties from 2D microscopy is difficult when a reference instrument reports only population statistics rather than labels for individual cells. Here we develop a population-supervised framework that maps single 2D red-cell ima...
Can AI Be Slowed Down? Stanford HAI Experts Weigh the Risks, Rules and Race Ahead
In a new series called Prompt Response, Stanford’s Surya Ganguli, Diyi Yang and Rob Reich examined emergent agent behavior, recursive self-improvement, independent evaluation and whether a kill switch can make advanced AI safer.
Improving synthesis prediction of small molecules at scale with RetroChimera
Custom-made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules.
The post Improving synth...
BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence
arXiv:2609.20886v1 Announce Type: new
Abstract: Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau. In traditional BI workflows, users need to prepare data by (1) identifying relevant tables, (2) ...
Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding
arXiv:2609.20888v1 Announce Type: new
Abstract: Massive KV caches can cause severe memory-bandwidth bottlenecks during long-context decoding. Sparse attention methods mitigate this via selective loading, but that comes at a cost: rigid heuristics drop necessary context, leading to quality degradati...
Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
arXiv:2609.20906v1 Announce Type: new
Abstract: Quasars are luminous objects in the universe that exhibit stochastic brightness variations encoding information about the supermassive black holes powering them, and modeling these variations from ground-based survey data time series, known as light c...
RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models
arXiv:2609.20971v1 Announce Type: new
Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation begins. Sparse block selection can reduce this cost, but a block centroid may hide a highly releva...
Attention-Aware Routing: Coupling Routing and Attention in MoEs
arXiv:2609.20974v1 Announce Type: new
Abstract: In Mixture-of-Experts language models, the router typically selects and weights experts based on the token's hidden state, utilizing limited contextual information. We propose Attention-Aware Routing (AAR), which augments the router with temporal and ...
CaLR: Causal Latent Revision for Robust Diffusion Reasoning
arXiv:2609.20981v1 Announce Type: new
Abstract: Autoregressive (AR) models suffer from local greediness, while diffusion language models (DLMs) often lack the strict causal structure required for reasoning. To combine the advantages and overcome the drawbacks of the dual, we propose Causal Latent R...
Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing
arXiv:2609.21096v1 Announce Type: new
Abstract: In this work, we examine the topology of information flow patterns within attention graphs to effectively distinguish hallucinated from non-hallucinated responses. We analyze the Forman-Ricci curvature to identify structural patterns indicating inform...
Layer-wise Curriculum Learning for Efficient LLM Compression
arXiv:2609.19213v1 Announce Type: new
Abstract: In this paper, we introduce layer-wise curriculum learning for efficient LLM compression. The proposed method facilitates the knowledge transfer from the teacher model to the student model, utilizing a curriculum learning approach that begins with eas...