Search: “models”

A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot

arXiv:2609.29528v1 Announce Type: cross Abstract: Real conversations between fraudsters and their targets are among the most informative artifacts for studying telephone scams, yet also the scarcest:

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A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimization

DGX agent

arXiv:2609.29150v1 Announce Type: new Abstract: This paper investigates the joint optimization of the transmit precoder and the transmission/reflection coefficients of a simultaneously transmitting an

model-releasesarxiv-cs-lg

25 Sept 2026

A Wrong Turn Does Not Ruin the Journey: Deviation-Guided Skill Self-Evolution for LLM Agents

arXiv:2609.29154v1 Announce Type: new Abstract: Large language model agents increasingly rely on natural-language skills to solve complex tool-use tasks. However, such tasks often admit multiple valid

agentsarxiv-cs-ai

Accent Analogy Guidance: More Speaker Similarity at Equal Accent in Cross-Lingual Voice Cloning

arXiv:2609.29123v1 Announce Type: cross Abstract: In cross-lingual zero-shot text-to-speech, the accent of the reference leaks into the target speech. We propose accent analogy guidance (AAG), a train

researcharxiv-cs-cl

Active Client Selection in Federated Trajectory Prediction with Uncertainty-Awareness and Heterogeneous Complexity

arXiv:2609.29600v1 Announce Type: new Abstract: Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized data

agentsarxiv-cs-lg

AFT Neural Function Approximators for 1D Nonlinear Force Laws

arXiv:2609.29242v1 Announce Type: cross Abstract: Nonlinear contacts and friction strongly influence the vibration response of assembled structures, but their accurate numerical treatment is computati

agentic-ger: terminology recovery in long-form speech using global context

arXiv:2609.29428v1 Announce Type: cross Abstract: Recent advances in speech language models have improved automatic speech recognition (ASR) for long-form audio. However, accurately and consistently t

agentsarxiv-cs-ai

AgriCountDINO: Parameter-Efficient Exemplar-Guided Counting and Localization in Agriculture

arXiv:2609.29460v1 Announce Type: cross Abstract: Accurate counting and localization of plants and their organs support phenotyping and yield estimation, yet target appearance, scale, and density vary

AlphaDiverse: Post-Training Local Quantitative Research Agents for Diverse Exploration in Alpha Factor Mining

arXiv:2609.29014v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems can automate alpha factor mining, but their reliance on external APIs limits control over cost, ava

local-aiarxiv-cs-ai

AquaMend: Minimal Re-probing and Conditional Rollback for Latent-Belief Failures in Embodied Agents

model-releasesarxiv-cs-ro

arXiv:2609.28973v1 Announce Type: new Abstract: Physical changes or sensing errors can invalidate embodied agents' task-relevant beliefs. AquaMend compares re-probing, rollback, and supported continua

BanglaKontho: Closing the Long-Form Gap in Bangla Text-to-Speech

arXiv:2609.29146v1 Announce Type: new Abstract: Bangla, the seventh most spoken language in the world, remains under-resourced for neural text-to-speech. Public Bangla speech corpora are dominated by

Between the Commits: Process, Error, and Claim Reliability in a Wholly AI-Authored Codebase

arXiv:2609.29744v1 Announce Type: cross Abstract: We present: (i) a new dataset consisting of the full development history of a 21,000-line Python tool built entirely by Claude AI, with no human-autho

Hardware

Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers

arXiv:2609.30198v1 Announce Type: new Abstract: Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent sp

hardwarearxiv-cs-lg

BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.30223v1 Announce Type: new Abstract: Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically cr

Canopy: Exploiting Piecewise Smooth Tree Priors for Multi-Fidelity Bandits

arXiv:2609.30017v1 Announce Type: cross Abstract: Many LLM inference problems, including model routing, prefix-cache management, prompt trimming, and test-time search, can be viewed as optimization ov

local-aiarxiv-cs-ai

Context-aware Skin Cancer Epithelial Cell Classification with Scalable Graph Transformers

arXiv:2602.15783v2 Announce Type: replace Abstract: Whole-slide images (WSIs) from cancer patients contain rich information that can be used for medical diagnosis or to follow treatment progress. To a

researcharxiv-cs-cv

Tutorials

CORDIAL: Calibrating Ordinal LLM Outputs from Few Labels

arXiv:2609.29807v1 Announce Type: new Abstract: A large language model (LLM) can turn a text into a distribution over an ordered scale, but that distribution is a noisy measurement: saturated, compres

tutorialsarxiv-cs-cl

Cultural Divergence Preservation: Diagnosing Flattening and Caricature in LLM-Simulated Survey Populations

arXiv:2609.29928v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents to estimate population response distributions. In cross-cultural su

researcharxiv-cs-ai

Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions

arXiv:2609.28613v1 Announce Type: cross Abstract: Most studies of prompt injection focus on generative agents, leaving their effects on models with schema-defined outputs unclear. We examine these eff

researcharxiv-cs-ai

Diverse Geometries, Frozen Weights: Robust Heterogeneous Treatment-Effect Estimation via Causal Expert Ensembles

arXiv:2609.29974v1 Announce Type: new Abstract: Estimating heterogeneous treatment effects from observational data is difficult because the most appropriate inductive bias varies with overlap, treatme

Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancement

arXiv:2609.29867v1 Announce Type: cross Abstract: Deep learning-based speech enhancement is increasingly deployed on-device in hearing aids, headsets, and earbuds. Most of these devices, however, can

local-aiarxiv-cs-lg

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

arXiv:2609.29864v1 Announce Type: new Abstract: High-resolution digital elevation models (DEMs) support Earth observation applications, but paired training references are often available only at coars

local-aiarxiv-cs-cv

Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop

arXiv:2609.29945v1 Announce Type: new Abstract: Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compe

researcharxiv-cs-lg

Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits

arXiv:2609.28718v1 Announce Type: cross Abstract: We study Bayesian linear bandits with an isotropic Gaussian parameter, independent Gaussian candidate arms, and Gaussian reward noise when the horizon

ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds

arXiv:2609.30199v1 Announce Type: new Abstract: Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and itera

Federated Learning of AnDE Classifiers

arXiv:2609.28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged n-Dependence Estimators (AnDE) in distributed environments. The proposed method focuses o

researcharxiv-cs-lg

Tutorials

How To Do Things With Prompts

arXiv:2609.29657v1 Announce Type: new Abstract: When users address large language models, they produce directive speech acts whose pragmatic features differ from those of both everyday conversation an

tutorialsarxiv-cs-cl

Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation

arXiv:2609.28859v1 Announce Type: new Abstract: Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality s

safetyarxiv-cs-ai

Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

arXiv:2609.28491v1 Announce Type: cross Abstract: Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating cu

Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora

arXiv:2609.29661v1 Announce Type: new Abstract: Most agentic question answering (QA) systems do an important part of their semantic work at the worst possible time: every time someone asks a question.

agentsarxiv-cs-ai

KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization

arXiv:2609.28818v1 Announce Type: cross Abstract: Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. H

Tutorials

Learning New Words from Unlabeled Test Data in Automatic Speech Recognition

arXiv:2609.28877v1 Announce Type: cross Abstract: New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This

tutorialsarxiv-cs-cl

Hardware

Less is More: Encoder-only Audio-Visual Segmentation

arXiv:2609.29121v1 Announce Type: cross Abstract: Audio-Visual Semantic Segmentation (AVSS) aims to identify, segment, and classify sound-emitting objects in video frames. Previous Transformer-based A

hardwarearxiv-cs-ai

MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimization

arXiv:2609.29941v1 Announce Type: new Abstract: Many real-world optimization problems rely on expensive simulations or experiments, making the efficient use of available data essential. Multi-fidelity

MILO: Efficient Many-shot In-Context Learning with Block-wise Low-rank Compression

arXiv:2609.29913v1 Announce Type: new Abstract: Many-shot in-context learning (ICL) enables large language models (LLMs) to adapt to complex tasks by conditioning on thousands of demonstration example

local-aiarxiv-cs-cl

ModularSQL: A Runtime Guardrail for the Multiplicity Blind Spot in Text-to-SQL

arXiv:2609.29573v1 Announce Type: new Abstract: Text-to-SQL systems are increasingly deployed on production databases, where queries that pass benchmark evaluation can still produce results that disto

Multimodal Routing and Region Refinement for Language-Guided Medical Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2609.28860v1 Announce Type: new Abstract: Textual descriptions can reduce ambiguity in medical image segmentation by specifying the finding and location to be delineated. Existing text-guided me

Neural Transport Nested Sampling

arXiv:2609.29413v1 Announce Type: new Abstract: Sampling from Boltzmann distributions of molecular systems is an inference problem that has seen significant recent developments fuelled by advances in

NNV3: Expanding Neural Network Verification to New Architectures and Domains

arXiv:2609.30050v1 Announce Type: new Abstract: We present NNV3, the latest version of the Neural Network Verification (NNV) tool, a MATLAB framework for formal verification of deep learning models an

safetyarxiv-cs-ai

Not Every Token Is Worth Distilling: Selective Supervision for Direct-OPD

arXiv:2609.29142v1 Announce Type: cross Abstract: Direct On-Policy Distillation (Direct-OPD) transfers reinforcement-learning-induced policy improvements from a small model to a larger student by usin

safetyarxiv-cs-ai

Only What Was Seen: Observation-Gram Compaction of View-Dependent Appearance in 3D Gaussian Splatting

arXiv:2609.28997v1 Announce Type: new Abstract: Most of the memory of a 3D Gaussian Splatting model holds spherical-harmonic colour coefficients, yet each Gaussian is seen only from the narrow cone of

researcharxiv-cs-cv

Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis

arXiv:2609.30115v1 Announce Type: cross Abstract: Modern deep neural networks treat parameters as static floating-point matrices stored in physical memory, incurring Von Neumann memory bottlenecks and

OREO: Fidelity Alignment in 3D Generation via On-the-fly Rendering-Editing Optimization

arXiv:2609.29788v1 Announce Type: new Abstract: Despite recent advancements in 3D generation, models often struggle to produce assets with high visual fidelity. To bridge this gap, we propose OREO, an

safetyarxiv-cs-cv

Pairwise Approximation Can Select the Wrong Multi-Robot Plan

model-releasesarxiv-cs-ro

arXiv:2609.29929v1 Announce Type: new Abstract: Multi-robot coordination methods often score a joint plan from singleton and pairwise terms, leaving out the terms that involve three or more robots. We

PartHackBench: Certified Equal-Progress Stress Tests for Partial-Credit Tool-Agent Evaluation

arXiv:2609.29578v1 Announce Type: new Abstract: Long-horizon tool agents often make useful progress without reaching terminal success, motivating partial-credit evaluation. Yet evaluators may reward m

Personalized Korean Lipreading as Visual Speech Recognition: Transfer, Census and Adaptation on OLKAVS

arXiv:2609.28988v1 Announce Type: cross Abstract: We present a personalized Korean visual speech recognition (VSR) system and quantify, on the nine-camera OLKAVS corpus, the gap between the population

PlenoCI: Plenoptic CharacterIstics for View Dependence Aware Change Classification

model-releasesarxiv-cs-cv

arXiv:2609.28930v1 Announce Type: new Abstract: Radiance field representations such as 3D Gaussian Splatting (3DGS) natively encode complex visual phenomena such as occlusions and view dependence, but

Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

arXiv:2609.29183v1 Announce Type: new Abstract: Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, howe

researcharxiv-cs-cl