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arXiv:2609.14833v1 Announce Type: new Abstract: World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal genera

applicationsarxiv-cs-ai

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Model Releases

One Size Does Not Fit All: Setting Inference Depth from the Questions a Deployment Actually Asks

DGX agent

arXiv:2609.14144v1 Announce Type: new Abstract: A transformer language model is trained to respond to any prompt, but each deployment asks only a narrow range of questions: a support assistant sees de

model-releasesarxiv-cs-cl

15 Sept 2026

One Spectrum, Two Resources: Data-Memory Scaling in Autoregressive Prediction

arXiv:2609.13500v1 Announce Type: cross Abstract: How much learned memory is needed to benefit from more data? We show that the two resources are governed by one predictive-energy spectrum in a positi

Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and ca

safetyarxiv-cs-lg

Online Language Adaptive Sampling for Better Distributed Cross-lingual Gains

arXiv:2609.14969v1 Announce Type: cross Abstract: Realignment is a promising approach for improving the cross-lingual transfer ability of multilingual language models, particularly for extremely low-r

Online local learning for generative thermodynamic computing

arXiv:2609.15439v1 Announce Type: cross Abstract: Generative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at ea

researcharxiv-cs-lg

Agents

Oops, Not Now: PEARL, a RAG-Based Support Agent for Gameplay and What Players Want from AI Help

arXiv:2609.13718v1 Announce Type: cross Abstract: AI-powered gameplay support agents hold promise for game-based learning, yet grounding generative models in structured game data remains an open chall

agentsarxiv-cs-ai

Open-UniMo: Towards Unified Motion-Language Understanding and Generation in the Open World

arXiv:2609.14615v1 Announce Type: cross Abstract: Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world envi

Agents

OpenAl4S: Code as Action, Science as Sessions

arXiv:2609.15096v1 Announce Type: new Abstract: AI co-scientists could accelerate computational research, but over a long-running study the workflow also has to stay inspectable, resumable and reprodu

agentsarxiv-cs-ai

Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

arXiv:2609.13514v1 Announce Type: new Abstract: Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when groun

safetyarxiv-cs-lg

Tutorials

Optimal Learning Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay

arXiv:2602.06797v3 Announce Type: replace-cross Abstract: We study optimal learning rate (LR) schedules under the functional scaling law (FSL) framework (Li et al., 2025), which decomposes training dy

tutorialsarxiv-cs-lg

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based fr

Optimizing Sparse Outcomes Through Dense Behavioral Signals via Value-Guided Preference Distillation

arXiv:2609.14648v1 Announce Type: new Abstract: Aligning multi-turn dialogue agents is usually framed as matching turn-level human preferences, yet direct optimization of long-term outcomes is often i

safetyarxiv-cs-cl

Option-Aware Retrieval and Task-Specific VLM Adaptation for Medical VQA

arXiv:2609.15530v1 Announce Type: new Abstract: We describe our submission to the MedReason 2026 challenge, covering multiple-choice (MCQ) and open-ended (OE) medical visual question answering (VQA) u

OptoAgent: A Trustworthy Multi-Agent Framework for Opportunistic Vision Micro-Screening in Classroom Environments

arXiv:2609.14514v1 Announce Type: new Abstract: A child with reduced distance vision often does not know that anything is wrong. Children adapt, move closer, and rarely report the difficulty, so the p

safetyarxiv-cs-ai

Hardware

OpWeave: Flexible Operator Disaggregation for Heterogeneous LLM Serving

arXiv:2609.14237v1 Announce Type: cross Abstract: LLM serving systems increasingly disaggregate inference into finer-grained stages, with recent approaches separating attention from FFN or MoE executi

hardwarearxiv-cs-ai

Agents

OrchSLM: Probing the Dynamics of Small Language Model Orchestration

arXiv:2609.13470v1 Announce Type: new Abstract: Although large language models (LLMs) have demonstrated remarkable capabilities, their reliance on cloud-scale infrastructure poses fundamental challeng

agentsarxiv-cs-ai

Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds

arXiv:2609.14376v1 Announce Type: new Abstract: Soft robots are commonly sought for safety-critical interactions in delicate environments, where accurate position and orientation control is imperative

safetyarxiv-cs-ro

Overflip: Repetition-Induced Label Flips in Guardrail Models

arXiv:2609.15013v1 Announce Type: new Abstract: Guardrail models are classifiers deployed to screen malicious prompts and responses in LLM-based services. To meet latency constraints, many lightweight

P-POSEMEM: Projective Semantic Memory for Consistent Language Grounding under Pose-Graph Rewrites

arXiv:2609.15475v1 Announce Type: new Abstract: A robot following language instructions needs its semantic memory to keep naming the same physical object while the SLAM pose graph underneath is optimi

researcharxiv-cs-ro

PACE: Progressive Angular-to-Norm Contrastive Embedding

arXiv:2609.15152v1 Announce Type: cross Abstract: Multimodal embedding models encode heterogeneous inputs into a shared embedding space, enabling efficient similarity computation across modalities and

Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting

arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, w

Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT

model-releasesarxiv-cs-cv

arXiv:2609.14106v1 Announce Type: new Abstract: We evaluated parameter-efficient fine-tuning (PEFT) of the Segment Anything Model (SAM) for liver tumor segmentation in abdominal CT of colorectal liver

Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement

model-releasesarxiv-cs-cl

arXiv:2609.14529v1 Announce Type: cross Abstract: Rigorous empirical validation of quantum machine learning on natural language tasks remains scarce. We evaluate a 10-qubit hybrid quantum-classical va

Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder

arXiv:2609.14277v1 Announce Type: cross Abstract: Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excita

Parameter isolation with domain-specific experts for incremental audio classification

arXiv:2609.14730v1 Announce Type: cross Abstract: To successfully deploy a model in time-varying environments such as streaming data prediction and sensing control, domain-incremental learning (DIL) h

PARSE: Provenance-Aware Retrieval Sanitization for Professional Domain LLM Agents

model-releasesarxiv-cs-cl

arXiv:2606.17467v3 Announce Type: replace-cross Abstract: Prompt injection defenses evaluated on synthetic benchmarks do not generalize to real enterprise documents, which are longer, denser, and inte

Tutorials

Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction

arXiv:2609.13225v1 Announce Type: new Abstract: Benchmarks agree that vision-language models reason poorly about low-level manipulation, but an aggregate accuracy score does not say which step fails.

tutorialsarxiv-cs-cv

Hardware

Partition-Aware Scheduling for Mobile Heterogeneous Inference Co-Execution

arXiv:2609.14213v1 Announce Type: cross Abstract: Modern mobile inference runs on heterogeneous platforms combining mobile GPUs with multiple CPU core clusters. Existing optimizations typically exploi

hardwarearxiv-cs-lg

Partition Scores Are Not System Scores: Deployment-Fidelity Gaps in Decomposed Algorithm Selection

arXiv:2609.13785v1 Announce Type: new Abstract: Oracle-style quantities, including virtual best solvers, selected-portfolio VBS, virtual-best encodings, and best-in-family summaries, are widely report

Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design

arXiv:2609.14591v1 Announce Type: new Abstract: Participation in federated learning (FL) comes at a cost. Clients trade off privacy, communication, and compute costs for potentially greater gains in m

researcharxiv-cs-lg

Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification

model-releasesarxiv-cs-cv

arXiv:2609.14703v1 Announce Type: new Abstract: Dental caries and endodontic disease are among the most common health conditions worldwide, and intraoral periapical radiographs are central to their de

PC^2-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devices

arXiv:2609.14722v1 Announce Type: new Abstract: Low-cost and low-resolution sensors used in edge deployments can produce test point clouds that are substantially sparser than the normal training data.

researcharxiv-cs-cv

Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation

arXiv:2609.13328v1 Announce Type: cross Abstract: Anticipating whether a pedestrian will cross the road is safety-critical for autonomous vehicles, requiring real-time inference under challenging cond

PeerPen: AI-Assisted Writing for Online Mental Health Peer Support

arXiv:2609.14886v1 Announce Type: cross Abstract: Online mental health communities thrive on peer support, yet those who volunteer to help often lack formal training and may struggle to articulate sup

Per-Matrix Optimality Is Not Enough: Three-Level Optimization for Low-Rank LLM Compression

arXiv:2609.15838v1 Announce Type: cross Abstract: Per-matrix singular value decomposition (SVD) truncation is Eckart-Young optimal in the whitened Frobenius norm, but errors from independently compres

Perceive, Refine, Reason: A Calibrated Pipeline for Measuring Indicators in Strategic Visual Communication on Social Media

arXiv:2609.14699v1 Announce Type: new Abstract: Visual content shapes audience perception and opinion on social media, and computational social science increasingly relies on automated tools to analyz

researcharxiv-cs-cv

Perceptual Reality Transformer: What Must an Illustration Preserve?

arXiv:2508.09852v2 Announce Type: replace-cross Abstract: How can models help people communicate unusual perceptual experiences without changing what they mean? A recognizable image is only part of th

Applications

Personalized and Explainable Blood Pressure Estimation from PPG via Hybrid CNN--Morphological Features

arXiv:2609.13190v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) offers a promising solution for personalized healthcare. However, ex

applicationsarxiv-cs-cv

Personalizing Personal Health Interfaces: Co-Design with Generative AI

arXiv:2609.15046v1 Announce Type: cross Abstract: Personal health interfaces present wellbeing data through standardized dashboards that rarely fit how people interpret or act on it. Personalizing the

safetyarxiv-cs-ai

Applications

Phorecaster365: A Human-Supervised Reference Architecture for Hybrid Pharmaceutical Sales Forecasting and Planning Decision Support

arXiv:2609.13907v1 Announce Type: cross Abstract: Pharmaceutical sales forecasts inform planning across products, regions, and distribution channels, yet their interpretation depends on inventory avai

applicationsarxiv-cs-ai

PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models

model-releasesarxiv-cs-cv

arXiv:2609.14973v1 Announce Type: new Abstract: We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the ph

Physical Kernel: Structured Visual Latents for Dark Manipulation

arXiv:2609.13244v1 Announce Type: cross Abstract: We study dark manipulation: after a brief lit Write encodes z0 = Enc(rgb), a policy pi(z) and open-loop dynamics f(z,a) complete contact-rich skills w

safetyarxiv-cs-cv

Physically Partitioned KVCache Format for CPU--GPU Load Balancing in MoE Inference

arXiv:2609.14507v1 Announce Type: cross Abstract: Single-GPU long-context inference with Mixture-of-Experts (MoE) models requires spilling the key-value cache (KVCache) to CPU memory. The spilled KV s

Local Ai

Physically Typed and Geometry-Aware Representations for Earth Foundation Models

arXiv:2609.13868v1 Announce Type: new Abstract: Earth-observation (EO) foundation models have become exceptionally effective at learning se mantic, high-dimensional geospatial embeddings, while modern

local-aiarxiv-cs-cv

Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm

arXiv:2609.15104v1 Announce Type: cross Abstract: We present the development and application of a three-dimensional Physics-Informed Neural Network (PINN) framework for the investigation of haemodynam

PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems

model-releasesarxiv-cs-cl

arXiv:2609.13152v1 Announce Type: new Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experime

Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks

arXiv:2609.15029v1 Announce Type: cross Abstract: Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns to