Search: “models”

EMHO: EMbodied Agent Harness Optimization via Experience Traces

arXiv:2610.08432v1 Announce Type: new Abstract: Improving embodied agents often focuses on optimizing the underlying model through training, while the surrounding agent harness that controls planning,

Agents

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Evaluating human-AI workflows for field research in viticulture

DGX agent

arXiv:2610.07669v1 Announce Type: cross Abstract: We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 202

agentsarxiv-cs-ai

7 Oct 2026

Forensic Reserve: Eliciting Latent Knowledge for Image Forgery Detection

arXiv:2610.08639v1 Announce Type: new Abstract: As generated images become increasingly realistic, reliable forgery detection is essential for maintaining trust in visual information. However, existin

JEV versus LLMs: Accuracy, Cost and Calibration on Seven Political Science Replications

arXiv:2610.06625v2 Announce Type: replace Abstract: Large language models (LLMs) annotate and scale political text or constructs by generating text tokens. A new class of models, which TypeSafe market

researcharxiv-cs-cl

Measuring climate backlash in Twitter and Reddit archives: Lexical definitions, recorded responses and participant turnover

arXiv:2610.07634v1 Announce Type: new Abstract: Social media archives are often used to study resistance to climate action, but words, response counters and observed participants do not measure the sa

On Open-Ended Information Seeking for Information Elicitation Agents

arXiv:2610.07509v1 Announce Type: new Abstract: Information elicitation is an open-ended information-seeking problem in which an interaction can unfold in many potentially valuable directions, requiri

On the Intrinsic Limited Robustness of Latent-Based Watermarking

arXiv:2610.08178v1 Announce Type: new Abstract: Existing latent-based watermarking methods for diffusion models have overestimated their robustness to image distortions, including geometric transforma

OOPMAS: Object-Oriented Multi-Agent Systems for Query-Level Workflow Generation

arXiv:2610.07787v1 Announce Type: new Abstract: Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question an

Tutorials

OxiGen: Oxidation-State-Aware Crystal Generation

arXiv:2610.08296v1 Announce Type: new Abstract: Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable cr

tutorialsarxiv-cs-lg

Safety

Performative Prediction with Selective Labels

arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pred

safetyarxiv-cs-lg

PlotPick: AI-powered batch extraction of numerical data from scientific figures

arXiv:2605.06021v2 Announce Type: replace Abstract: Systematic reviews and meta-analyses often need numerical data reported only in figures, and extracting them with interactive digitisers usually req

Probabilistic Truly Unordered Rule Sets

arXiv:2401.09918v2 Announce Type: replace Abstract: Rule set learning has recently been frequently revisited because of its interpretability. Existing methods have several shortcomings though. First,

ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents

arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with st

RenderBench: Benchmarking Render-to-Real Video Transfer with Reconstructed Digital Twins

arXiv:2610.08684v1 Announce Type: new Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, and

Scalable next-scale autoregression for medical image generation across anatomical regions

arXiv:2602.14512v3 Announce Type: replace Abstract: Autoregressive pretraining has been key to the scalability of large language models, yet medical generative foundation models remain predominantly b

researcharxiv-cs-cv

SPECTRUM: Proximal Spectral Modulation for Looped Self-Distillation

arXiv:2610.07237v1 Announce Type: new Abstract: A model that learns from its own outputs inherits more than their correctness: it inherits which solutions it produces. We formulate Looped Self-Distill

researcharxiv-cs-ai

TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning

arXiv:2610.06855v1 Announce Type: cross Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formu

researcharxiv-cs-cv

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

arXiv:2610.08358v1 Announce Type: cross Abstract: Post-training quantization is a standard route to fitting vision transformers (ViTs) into edge compute and memory budgets, yet quantized models become

The Failure Is in the Readout: Fine-Grained Emotion Recognition Benchmarks Measure Elicitation, Not Perception

model-releasesarxiv-cs-cl

arXiv:2610.08162v1 Announce Type: cross Abstract: Fine-grained emotion recognition supports therapy tools and social robots, but it needs facial data, which raises privacy and data-protection concerns

The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

model-releasesarxiv-cs-cl

arXiv:2610.08026v1 Announce Type: new Abstract: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked wi

Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects

arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera

Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine

arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calli

researcharxiv-cs-cl

Adapting prior-data fitted networks for tabular anomaly detection

arXiv:2610.06693v1 Announce Type: new Abstract: While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the li

ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing

arXiv:2610.06514v1 Announce Type: cross Abstract: The growing adoption of large language model (LLM) agents creates a need for network administrators and security teams to audit agent behavior within

Safety

Best-of-N Guidance for Test-time Diffusion Alignment

arXiv:2610.05108v1 Announce Type: new Abstract: Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model.

safetyarxiv-cs-lg

CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research

arXiv:2610.05863v1 Announce Type: new Abstract: High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constraine

Hardware

Characterizing Parallelism Strategies in LLM Inference: Fundamental Compute-Communication Trade-offs

arXiv:2610.05305v1 Announce Type: cross Abstract: Large Language Model (LLM) inference has become the dominant workload in modern AI systems, requiring serving infrastructures to maximize throughput w

hardwarearxiv-cs-lg

CIPHER-MoE: Balancing Efficiency and Routing Fidelity in Trillion-Scale MoE Training

arXiv:2610.05744v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has been widely adopted in recent large language model (LLM) architectures. However, scaling up MoE in LLM training introduces

CodeForge-MA: Execution-Verified Multi-Agent Learning with Language-Conditioned LoRA for Multilingual Code Generation

arXiv:2610.05481v1 Announce Type: cross Abstract: Large language models for code generation often fail on execution, multilingual coverage, and contamination control, especially under frozen backbone

Correct Verdicts, Flawed Reasoning: Structured Auditing of LLM-based Vulnerability Reasoning

arXiv:2610.06366v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed for automated software vulnerability analysis. Binary classification alone is insufficient; pra

Does Neural Complexity Improve Health Misinformation Detection? A Leakage-Controlled Cross-Corpus Benchmark

arXiv:2610.04636v1 Announce Type: cross Abstract: Increasing architectural complexity is often assumed to improve health misinformation detection, yet reported gains are difficult to interpret when st

Safety

EduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity

arXiv:2507.20335v2 Announce Type: replace Abstract: Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs

safetyarxiv-cs-lg

Efficient Analog-Initialized Latent Transport for Spatially Coherent Probabilistic Downscaling from Global to Kilometer Scales

arXiv:2610.03736v1 Announce Type: cross Abstract: Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-init

Efficient Secure Federated Learning via Information-Theoretically Secure Key Distribution: A Medical Imaging Case Study

arXiv:2610.06420v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative training of models across institutions without centralizing sensitive data, making it well-suited for priv

EvoMem-VLA: State-Evolution Memory for Long-Horizon Robot Manipulation

arXiv:2610.05418v1 Announce Type: cross Abstract: Most vision-language-action (VLA) models rely on current observations and lose task-relevant evidence once it leaves view, limiting performance on lon

GNN-CB: A Graph Neural Network Competition Benchmark for Human and LLM Evaluation

arXiv:2610.05387v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong performance on coding and reasoning benchmarks; however, their ability to solve graph-structured m

GS-Codec: A Gaussian-Splatting Bottleneck for Neural Audio Coding

arXiv:2610.04651v1 Announce Type: cross Abstract: Neural audio codecs compress waveforms into compact discrete tokens that underpin speech language models, real-time communication, and large-scale aud

Hidden in the Comments: A Context-Injection Attack Surface in Code LLMs

arXiv:2610.05139v1 Announce Type: new Abstract: Code large language model (Code LLM) assistants generate code from heterogeneous development contexts, including open files, imported modules, pasted sn

Ideal Paths for Approximating Logistic Gradient Descent Trajectories at Large Initialization

arXiv:2610.04142v1 Announce Type: new Abstract: Modern training on a new task often starts from a previously trained model rather than from scratch, raising the question of how this initialization aff

LoopMoEVR: Loop-Based Degradation-Aware Mixture-of-Experts for Unified UHD Video Restoration

arXiv:2610.05109v1 Announce Type: new Abstract: Recently, unified high-definition image restoration has attracted considerable attention; however, existing models tend to excessively increase their de

ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flow

arXiv:2610.04307v1 Announce Type: cross Abstract: Machine Learning (ML) methods promise a fast solution process for Optimal Power Flow (OPF). While inconsistent test cases, implementations, and evalua

Applications

MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation

arXiv:2503.09950v2 Announce Type: replace Abstract: In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans b

applicationsarxiv-cs-cv

MOSAIC-SV: Real-Time Adaptive Identification of Vessel Dynamics for the Control and Deployment of Aquatic Robots

arXiv:2610.03898v1 Announce Type: new Abstract: Model-based control of an aquatic robotic platform depends on a hydrodynamic model that is costly to identify and specific to the hull, payload, and con

researcharxiv-cs-ro

Ontology Concept Overlap as a Training Signal: Knowledge-Grounded Reinforcement Learning for Clinical Question Answering

arXiv:2610.06360v1 Announce Type: cross Abstract: Reinforcement learning post-training for language models relies on two reward designs: human preferences (RLHF, DPO) and binary verifiers (RLVR). Clin

PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data

arXiv:2610.06825v1 Announce Type: cross Abstract: Scientific figures often encode quantitative results that are not readily available in machine-readable form, making accurate plot digitization import

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro

Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training

arXiv:2610.05491v1 Announce Type: cross Abstract: Efficient and robust tracking of surgical robotic instruments is important for robot-assisted minimally invasive surgery, yet remains challenging due

Safe Image Generation via Reinforcement Learning

arXiv:2610.05908v1 Announce Type: new Abstract: Recent Text-to-Image (T2I) models achieve remarkable visual image generation performance, but they can still generate NSFW (Not-Safe-For-Work) contents,