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LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions

arXiv:2609.14849v1 Announce Type: cross Abstract: We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' auto

LLMs or Naive Bayes? Old Gems or New Ways

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DGX agent

arXiv:2609.13185v1 Announce Type: cross Abstract: Large language models (LLMs) prompt a recurring question in research computing: should classical methods like Naive Bayes (NB) be retired? We benchmar

model-releasesarxiv-cs-ai

15 Sept 2026

Long-to-Short Video Evidence Reasoning for Grounded Question Answering

arXiv:2609.15224v1 Announce Type: new Abstract: We present LOVER, a nderline{L}ong to shnderline{O}rt nderline{V}ideo nderline{E}vidence nderline{R}einforced model for grounded question answering (GQA

researcharxiv-cs-cv

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

arXiv:2609.15859v1 Announce Type: new Abstract: Extracting informative representations from longitudinal data that can predict future outcomes remains a critical challenge in medicine. Medical dataset

agentsarxiv-cs-ai

Look Before You Leap: Factual Decoding with Internal Attribution Signals

arXiv:2609.15745v1 Announce Type: cross Abstract: Hallucination remains a critical challenge in large language models (LLMs), where early factual errors compound through autoregressive generation in a

Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination

arXiv:2609.14767v1 Announce Type: cross Abstract: Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in p

agentsarxiv-cs-ai

LoRA Fine-Tuned Models for Control Systems Course Q&A: A Multidimensional Evaluation of Model Scale and Rank Effects

arXiv:2609.13918v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in specialized university courses, but control-systems questions require coordinated terminology, not

LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordinal Data

arXiv:2602.04192v3 Announce Type: replace Abstract: Learning the intrinsic dimensionality of subjective perceptual spaces such as taste, smell, or aesthetics from ordinal data is a challenging problem

applicationsarxiv-cs-lg

Lost-in-the-Middle in Long-Text Generation: Synthetic Dataset, Evaluation Framework, and Mitigation

arXiv:2503.06868v2 Announce Type: replace-cross Abstract: Existing long-text generation methods produce lengthy outputs from short inputs, leaving long-input-to-long-output generation underexplored. A

Low Clearance Hinge Joint Mechanism Based on 3D Printing on Sheet Fabrication Methodology

arXiv:2609.15276v1 Announce Type: new Abstract: This paper presents a low-clearance hinge joint mechanism based on the 3D printing on sheet fabrication method. This approach simplifies the fabrication

applicationsarxiv-cs-ro

Low-Dimensional Embeddings for Gaussian Kernels on Manifolds

arXiv:2609.15179v1 Announce Type: cross Abstract: The Gaussian kernel is a widely used similarity measure underlying kernel methods such as kernel PCA and spectral clustering, but computing Gaussian k

researcharxiv-cs-lg

LPA-CWM: A Learned Physical Adjudicator for Motion Reasoning with Counterfactual World Models

arXiv:2609.14073v1 Announce Type: cross Abstract: Counterfactual world models (CWM) extract motion from pretrained video predictors by comparing factual and intervened predictions. However, responses

Hardware

LynnReal-Omni: Native multi-modal Video Generation for Agentic Visual Workflows

arXiv:2609.15863v1 Announce Type: new Abstract: Video diffusion models are stochastic and hard to control: precise content often requires repeated sampling without guaranteed success, and long-horizon

hardwarearxiv-cs-cv

Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization

model-releasesarxiv-cs-lg

arXiv:2609.13247v1 Announce Type: cross Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly

Machine Learning in Fish Farming

arXiv:2609.13919v1 Announce Type: new Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improvin

applicationsarxiv-cs-lg

Machine Learning under Imperfect Data: Challenges and Methods

model-releasesarxiv-cs-lg

arXiv:2609.13914v1 Announce Type: new Abstract: Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn

Safety

Machine Unlearning for Speech Question Answering in Large Audio-Language Models

arXiv:2609.13195v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have recently shown strong capabilities in speech understanding and question answering (QA), but they also inherit

safetyarxiv-cs-cl

MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery

model-releasesarxiv-cs-cv

arXiv:2609.15676v1 Announce Type: new Abstract: Accurate marine pollution detection (MPD) is essential for protecting coastal ecosystems and marine biodiversity. Vision Mamba models have shown promise

MANAS-2: Constrained Reconstruction for EEG Foundation Models

arXiv:2609.13717v1 Announce Type: new Abstract: Masked reconstruction is widely used for EEG foundation models, but optimizing reconstruction on low-SNR waveforms does not necessarily produce the most

Local Ai

MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks

arXiv:2609.14660v1 Announce Type: cross Abstract: Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tai

local-aiarxiv-cs-ai

Many assume that agent spend goes toward output tokens. When we ran DeepSWE on Astra vs. DeepSeek V4.1-Flash, input tokens outnumbered outpu…

model-releasesfireworks-ai--x

Many assume that agent spend goes toward output tokens. When we ran DeepSWE on Astra vs. DeepSeek V4.1-Flash, input tokens outnumbered output 174 to 1. 99.6% were cache hits. Those hits are 60% of the

Map Users and Mapmakers: The Scope of Cognitive Attribution from Acquired Representations

arXiv:2609.13879v1 Announce Type: new Abstract: An acquired representation can enlarge a system's cognitive repertoire without transferring the capacities exercised in producing that representation. T

MAPS: Memory-Aware Predictive Scheduling Framework for Large Language Model Serving

arXiv:2609.15359v1 Announce Type: new Abstract: The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefil

applicationsarxiv-cs-ai

Local Ai

MARC: Morphology-Aware Regression of Consensus for Cell Segmentation in Subcellular Spatial Transcriptomics

arXiv:2609.13665v1 Announce Type: new Abstract: Accurate cell segmentation remains a major bottleneck in subcellular spatial transcriptomics (SST), in which morphological images and spatially resolved

local-aiarxiv-cs-cv

MARCUS: An agentic, multimodal vision-language model for cardiac diagnosis and management

arXiv:2603.22179v2 Announce Type: replace Abstract: Cardiovascular disease remains the leading cause of global mortality, with progress hindered by human interpretation of complex cardiac tests. Curre

MarKey: Marginal Utility Guided Greedy Keyframe Selection for Long Video Understanding

arXiv:2609.15408v1 Announce Type: cross Abstract: Long-video understanding remains challenging for multimodal large language models (MLLMs) because densely encoding long frame sequences is computation

Safety

Mask, Sample, Revise: A Revisable CTMC Inference Stack for Guided Discrete Flow Matching Text-to-Speech

arXiv:2606.13989v2 Announce Type: replace-cross Abstract: Recent alignment-free non-autoregressive (NAR) text-to-speech (TTS) models formulate synthesis as a conditional infilling task, bypassing expl

safetyarxiv-cs-ai

MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-speci

researcharxiv-cs-lg

Safety

Math for AI safety: an invitation for mathematicians

arXiv:2609.15289v1 Announce Type: cross Abstract: Artificial intelligence threatens to outrun human understanding and control. New mathematics is needed to design AI that is legible, steerable, and co

safetyarxiv-cs-ai

MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images

model-releasesarxiv-cs-cv

arXiv:2609.14495v1 Announce Type: new Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently,

MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use

arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (

Measuring the Cost of Variety Conflation in Multilingual MT Evaluation: Adding Mozambican Xichangana, Nyanja and Sena to FLORES+

arXiv:2609.13847v1 Announce Type: new Abstract: In this paper, we extend FLORES+ with Portuguese-source evaluation sets for three Mozambican Bantu varieties: Xichangana, Mozambican Nyanja, and Sena. W

Measuring the Creativity of Frontier LLMs in Automated Research

arXiv:2609.14057v1 Announce Type: new Abstract: Frontier LLMs are increasingly capable of conducting automated research, yet their creativity in this setting has not been systematically evaluated. In

Hardware

MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation

arXiv:2609.15647v1 Announce Type: new Abstract: Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are oft

hardwarearxiv-cs-cv

Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes

arXiv:2609.15129v1 Announce Type: new Abstract: Complex medical information is often difficult for patients to understand, making effective medical knowledge simplification essential for improving pat

applicationsarxiv-cs-ai

MedSAM3: Delving into Segment Anything with Medical Concepts

arXiv:2511.19046v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consumin

agentsarxiv-cs-ai

MedTRACE: Tool-Augmented Multimodal Clinical Reasoning Agents for Evidence-Grounded Decision-Making

arXiv:2609.14823v1 Announce Type: new Abstract: Multimodal clinical decision-making requires reliable reasoning over heterogeneous evidence from electronic health records, medical images, and physiolo

agentsarxiv-cs-cl

MedVA: An End-to-End Neuro-Symbolic Agentic System for Medical Volume Visualization

arXiv:2609.14874v1 Announce Type: cross Abstract: Medical volume visualization requires selecting regions of interest (ROIs) and carefully controlling their relative visual emphasis according to a giv

agentsarxiv-cs-cv

memory has been a hot topic for the past ~2 years every time we bring it up or do anything there, gets ton of interest, but nothing sticks f…

memory has been a hot topic for the past ~2 years every time we bring it up or do anything there, gets ton of interest, but nothing sticks few takes on why below: - how to update and then use memory n

agentsharrison-chase--x

MemRiskBench: Trace-Aware Risk-Preserving Evaluation for Long-Horizon LLM Agents

arXiv:2609.14976v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate memory across sessions, creating sparse but high-impact risks: stale facts, conflicting updates, cross-user leakage,

Merging the Knowledge of LLMs for Automatic Speech Recognition

arXiv:2609.15743v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems, trained on paired speech-text data, have been improved by leveraging language models (LMs) trained on text-o

Mesh-based Super-resolution of Multiscale Detonation Flows with Graph Transformers

arXiv:2511.12041v4 Announce Type: replace-cross Abstract: Super-resolution flow reconstruction using state-of-the-art data-driven techniques is valuable for a variety of applications, such as subgrid/

Tutorials

MessyMem: Learning-from-Doing Memory for Mobile Manipulation

arXiv:2609.15976v1 Announce Type: new Abstract: Mobile manipulators deployed across many rooms and visits should improve with experience: after discovering that a cabinet is locked or finding an objec

tutorialsarxiv-cs-ro

Mind Which Bird You Favour: Parameterizing Adequacy-Fluency Balance in Meta-Evaluation of Machine Translation

arXiv:2609.14795v1 Announce Type: cross Abstract: There is a tradeoff in machine translation meta-evaluation between prioritizing alignment with adequacy versus fluency. The balance depends on the com

Mind2Cloud: EEG-to-Point Cloud Generation with Two-Granularity Diffusion Decoding

model-releasesarxiv-cs-cv

arXiv:2609.13991v1 Announce Type: new Abstract: Reconstructing 3D objects from brain signals offers a promising avenue for understanding human visual cognition. While prior work has shown initial succ

Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States

model-releasesarxiv-cs-cl

arXiv:2609.15972v1 Announce Type: new Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the peop

Minibatch persistency, eight years later: what batch reuse costs in steps and joules, and what it saves in data

model-releasesarxiv-cs-lg

arXiv:2609.13922v1 Announce Type: new Abstract: Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on

Mirror, Mirror on the Wall: Prompt Echoing in Small Instruct Language Models

arXiv:2609.15045v1 Announce Type: cross Abstract: Prompt echoing is a recognized failure mode of instruct language models, in which a model instead of generating a response, mirrors the provided promp