Search: “models”

A Reliability Assessment of LALM Audio Judges for Full-Duplex Voice Agents

arXiv:2607.07985v1 Announce Type: cross Abstract: We report the empirical reliability of Gemini models as audio judges that score full-duplex agent conversations directly from the raw stereo waveform,

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Adaptive Generation of Bias-Eliciting Questions for LLMs

DGX agent

arXiv:2510.12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide. Despite th

model-releasesarxiv-cs-ai

10 Jul 2026

Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

arXiv:2607.07760v1 Announce Type: new Abstract: We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains

Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning

arXiv:2607.07761v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This surv

CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

arXiv:2607.08093v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant b

Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion

arXiv:2607.08241v1 Announce Type: new Abstract: Deploying classifier-free guidance (CFG) diffusion models under real-world compute budgets requires quantization, yet existing post-training quantizatio

Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

arXiv:2407.13632v2 Announce Type: replace Abstract: Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory

Different Teachers, Different Capabilities: Sub-1B On-Device Distillation for Structured Text Enrichment

arXiv:2607.08268v1 Announce Type: new Abstract: High-volume structured extraction pays a large model's latency on every item, so distilling the task into a small on-device model is attractive: compara

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

arXiv:2607.08595v1 Announce Type: new Abstract: Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across i

applicationsarxiv-cs-lg

Local Ai

FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection

arXiv:2607.08014v1 Announce Type: new Abstract: Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models witho

local-aiarxiv-cs-cv

Safety

Native Video-Action Pretraining for Generalizable Robot Control

arXiv:2607.08639v1 Announce Type: cross Abstract: The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed

safetyarxiv-cs-cv

Local Ai

Resolving Predictive Multiplicity for the Rashomon Set

arXiv:2601.09071v2 Announce Type: replace Abstract: The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a Rashomon set of models achi

local-aiarxiv-cs-lg

Towards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model Finetuning

arXiv:2607.08393v1 Announce Type: new Abstract: Fine-tuning LLMs to inject new knowledge faces a critical challenge: LLMs can quickly memorize new facts, yet fail to use them for downstream reasoning

Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench

arXiv:2607.08284v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. Ho

Bound to Disagree: Generalization Bounds via Certifiable Surrogates

arXiv:2602.23128v2 Announce Type: replace Abstract: Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes. In this paper, we tack

researcharxiv-cs-lg

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607.07179v1 Announce Type: new Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information

Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data

arXiv:2607.07060v1 Announce Type: cross Abstract: Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The margi

applicationsarxiv-cs-ai

Dynamic Object Detection and Tracking in Construction: A Fisheye Camera and LiDAR Sensor Fusion Model

arXiv:2607.06896v1 Announce Type: cross Abstract: Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environme

applicationsarxiv-cs-cv

FDRMFL: Multimodal Federated Feature Extraction Model Based on Information Maximization and Contrastive Learning

arXiv:2512.02076v2 Announce Type: replace-cross Abstract: We propose FDRMFL, a task-driven multimodal feature extraction framework for federated regression under non-IID data distributions. Extracting

applicationsarxiv-cs-ai

Geometric Collapse: When Vision Models Fail to Verify Physical Causality

arXiv:2607.06871v1 Announce Type: new Abstract: Recent progress in large-scale self-supervised learning has improved dense geometric prediction, but it remains unclear whether such scaling yields infe

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

arXiv:2607.06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hall

SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation

arXiv:2607.07469v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) for e-commerce attribute extraction requires labeled data representative across thousands of product types, a

Think Big, Search Small: Where Capacity Matters in Hierarchical Search Agents?

model-releasesarxiv-cs-cl

arXiv:2607.07548v1 Announce Type: new Abstract: Large language model based search agents increasingly adopt multi-agent architectures in which a main agent decomposes a complex question into sub-queri

Local Ai

When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning

arXiv:2607.06720v1 Announce Type: new Abstract: Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise

local-aiarxiv-cs-ai

aiAuthZ: Off-Host, Identity-Bound Authorization for AI Agents

arXiv:2607.05518v1 Announce Type: cross Abstract: AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authorit

Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding

arXiv:2606.22511v2 Announce Type: replace Abstract: In open-ended generation, LLMs frequently fall into the 'likelihood trap', marked by repetitive degeneration and vocabulary dullness, creating a dis

researcharxiv-cs-cl

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

model-releasesarxiv-cs-lg

arXiv:2607.05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities. However, RVFL models face

NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis

arXiv:2603.15932v2 Announce Type: replace Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival outcomes critically dependent on early and accurate detec

Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation

arXiv:2607.05554v1 Announce Type: cross Abstract: Survey-style evaluations of large language models often treat a prompted response as a measure of a model's values or beliefs. This assumption is part

Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification

arXiv:2607.06309v1 Announce Type: cross Abstract: Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolat

UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation

arXiv:2607.06306v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated growing competence in web page generation. However, existing text-driven approaches rely on complex pro

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607.06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and

When Should LLMs Search? Counterfactual Supervision for Search Routing

arXiv:2607.05752v1 Announce Type: cross Abstract: Search-augmented language models can use external evidence to compensate for limitations in parametric knowledge, but search is not uniformly benefici

AutoCedar: An Agentic Framework for Verifier-Guided Access Control Policy Synthesis

arXiv:2607.03656v1 Announce Type: cross Abstract: Large Language Models are increasingly used to turn natural-language requirements into code. In access control, that shortcut is dangerous: a generate

Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems

model-releasesarxiv-cs-lg

arXiv:2607.03671v1 Announce Type: cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables: similar activity can eme

Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making

arXiv:2111.15255v2 Announce Type: replace-cross Abstract: The probabilistic linguistic term has been proposed to deal with probability distributions in provided linguistic evaluations. However, becaus

Effective Distillation to Hybrid xLSTM Architectures

model-releasesarxiv-cs-lg

arXiv:2603.15590v2 Announce Type: replace Abstract: There have been numerous attempts to distill quadratic attention-based large language models (LLMs) into sub-quadratic linearized architectures. How

Enhancing Video Physical Consistency via Role-aware Joint Training and Modality-decoupled Denoising

arXiv:2607.04653v1 Announce Type: new Abstract: While modern video diffusion models excel in visual fidelity, maintaining long-range physical consistency remains a formidable challenge. Conventional p

IDEAL-Bench: Indoor Dataset and Evaluation suite for Analyzing 3D Layout reasoning

arXiv:2607.03614v1 Announce Type: new Abstract: Spatial question answering is the dominant paradigm for evaluating spatial intelligence in Vision-Language Models (VLMs), but it leaves a complementary

Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models

arXiv:2602.17750v2 Announce Type: replace-cross Abstract: A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and

IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation

arXiv:2512.10730v2 Announce Type: replace Abstract: Recent advances in motion-aware large language models have shown remarkable promise for jointly learning motion understanding and generation knowled

Language Models as Higher-Order Planning Formalizers

arXiv:2603.23844v2 Announce Type: replace Abstract: Recent work provides overwhelming evidence that LLMs, even those trained to scale their reasoning trace, quickly deteriorate at planning as problems

researcharxiv-cs-cl

LLMs Encode Harmfulness and Refusal Separately

model-releasesarxiv-cs-cl

arXiv:2507.11878v5 Announce Type: replace Abstract: LLMs are trained to refuse harmful instructions, but do they truly understand harmfulness beyond just refusing? Prior work has shown that LLMs' refu

miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models

arXiv:2409.07589v2 Announce Type: cross Abstract: EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in extracting discriminative

researcharxiv-cs-lg

Model Confidence-Guided Multi-Image Fusion of Fundus Images for Diabetic Retinopathy Diagnosis

arXiv:2607.03643v1 Announce Type: cross Abstract: Purpose: Early screening for eye diseases is critical in low- and middle-income countries where access to care is limited. We investigate whether a co

OpenSIR: Open-Ended Self-Improving Reasoner

arXiv:2511.00602v4 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may

researcharxiv-cs-cl

Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters

arXiv:2504.08791v3 Announce Type: replace-cross Abstract: On-device inference offers privacy, offline use, and instant response, but consumer hardware restricts large language models (LLMs) to low thr

Safety

Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization

arXiv:2607.03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements. A common

safetyarxiv-cs-lg