Search: “model-releases”

ControlFoley: Unified and Controllable Video-to-Audio Generation with Cross-Modal Conflict Handling

arXiv:2604.15086v1 Announce Type: cross Abstract: Recent advances in video-to-audio (V2A) generation enable high-quality audio synthesis from visual content, yet achieving robust and fine-grained cont

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Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge

DGX agent

arXiv:2604.14488v1 Announce Type: cross Abstract: In any domain where knowledge accumulates under formal authority -- law, drug regulation, software security -- a later document can formally void an e

model-releasesarxiv-cs-cl

17 Apr 2026

CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors

arXiv:2604.14773v1 Announce Type: new Abstract: While LLMs have demonstrated remarkable potential in Question Answering (QA), evaluating personalization remains a critical bottleneck. Existing paradig

Correcting Suppressed Log-Probabilities in Language Models with Post-Transformer Adapters

arXiv:2604.14174v1 Announce Type: new Abstract: Alignment-tuned language models frequently suppress factual log-probabilities on politically sensitive topics despite retaining the knowledge in their h

Cross Paradigm Representation and Alignment Transformer for Image Deraining

arXiv:2504.16455v2 Announce Type: replace Abstract: Transformer-based networks have achieved strong performance in low-level vision tasks like image deraining by utilizing spatial or channel-wise self

Curvature-Aligned Probing for Local Loss-Landscape Stabilization

arXiv:2604.14870v1 Announce Type: new Abstract: Local loss-landscape stabilization under sample growth is typically measured either pointwise or through isotropic averaging in the full parameter space

DeTracker: Motion-decoupled Vehicle Detection and Tracking in Unstabilized Satellite Videos

arXiv:2601.09240v2 Announce Type: replace Abstract: Satellite videos provide continuous observations of surface dynamics but pose significant challenges for multi-object tracking (MOT), especially und

DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines

arXiv:2604.14314v1 Announce Type: cross Abstract: This manuscript introduces DharmaOCR Full and Lite, a pair of specialized small language models (SSLMs) for structured OCR that jointly optimize trans

Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems

arXiv:2604.14228v1 Announce Type: cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user. This study describes

Don't Retrieve, Navigate: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG

arXiv:2604.14572v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) grounds LLM responses in external evidence but treats the model as a passive consumer of search results: it never

DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

arXiv:2602.22699v2 Announce Type: replace-cross Abstract: SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through m

ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents

arXiv:2604.13097v1 Announce Type: cross Abstract: Embodied agents increasingly rely on modular capabilities that can be installed, upgraded, composed, and governed at runtime. Prior work has introduce

EuropeMedQA Study Protocol: A Multilingual, Multimodal Medical Examination Dataset for Language Model Evaluation

arXiv:2604.14306v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated high proficiency on English-centric medical examinations, their performance often declines when fac

EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews

arXiv:2604.14165v1 Announce Type: new Abstract: We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native tria

Explainable Graph Neural Networks for Interbank Contagion Surveillance: A Regulatory-Aligned Framework for the U.S. Banking Sector

arXiv:2604.14232v1 Announce Type: new Abstract: The Spatial-Temporal Graph Attention Network (ST-GAT) framework was created to serve as an explainable GNN-based solution for detecting bank distress ea

Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations

arXiv:2604.14751v1 Announce Type: cross Abstract: The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between

Exploring and Testing Skill-Based Behavioral Profile Annotation: Human Operability and LLM Feasibility under Schema-Guided Execution

arXiv:2604.14843v1 Announce Type: new Abstract: Behavioral Profile (BP) annotation is difficult to automate because it requires simultaneous coding across multiple linguistic dimensions. We treat BP a

Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models

arXiv:2604.14640v1 Announce Type: new Abstract: The proliferation of financial misinformation poses a severe threat to market stability and investor trust, misleading market behavior and creating crit

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

arXiv:2604.14451v1 Announce Type: cross Abstract: Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribu

Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval

arXiv:2510.15946v3 Announce Type: replace Abstract: Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorica

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

arXiv:2604.15115v1 Announce Type: new Abstract: Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proporti

FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images

arXiv:2604.14388v1 Announce Type: new Abstract: Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision lang

Formal Architecture Descriptors as Navigation Primitives for AI Coding Agents

arXiv:2604.13108v1 Announce Type: cross Abstract: AI coding agents spend a substantial fraction of their tool calls on undirected codebase exploration. We investigate whether providing agents with for

Frame forecasting in cine MRI using the PCA respiratory motion model: comparing recurrent neural networks trained online and transformers

arXiv:2410.05882v3 Announce Type: replace-cross Abstract: Respiratory motion complicates accurate irradiation of thoraco-abdominal tumors during radiotherapy, as treatment-system latency entails targe

FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking

arXiv:2604.14526v1 Announce Type: new Abstract: Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new pote

Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification

arXiv:2604.14958v1 Announce Type: new Abstract: Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples.

From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures

arXiv:2601.02997v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in program synthesis, yet their capacity for neural architecture design -- balancing syntactic reliability,

GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis

arXiv:2604.13888v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. How

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning

arXiv:2603.01410v2 Announce Type: replace Abstract: Knowledge graphs provide structured and reliable information for many real-world applications, motivating increasing interest in combining large lan

H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

arXiv:2604.14507v1 Announce Type: new Abstract: As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a

Hierarchical vs. Flat Iteration in Shared-Weight Transformers

arXiv:2604.14442v1 Announce Type: new Abstract: We present an empirical study of whether hierarchically structured, shared-weight recurrence can match the representational quality of independent-layer

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations

arXiv:2604.15273v1 Announce Type: new Abstract: Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, spl

HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps

arXiv:2604.14944v1 Announce Type: cross Abstract: We present HRDexDB, a large-scale, multi-modal dataset of high-fidelity dexterous grasping sequences featuring both human and diverse robotic hands. U

IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation

arXiv:2511.01014v3 Announce Type: replace Abstract: Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imp

IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation

arXiv:2603.04738v2 Announce Type: replace Abstract: Instruction-following is a foundational capability of large language models (LLMs), with its improvement hinging on scalable and accurate feedback f

ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models

arXiv:2604.08064v2 Announce Type: replace Abstract: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavio

Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training

arXiv:2408.14728v2 Announce Type: replace Abstract: Adversarial training has proven effective in improving the robustness of deep neural networks against adversarial attacks. However, this enhanced ro

Improving Language Models with Intentional Analysis

arXiv:2502.04689v4 Announce Type: replace Abstract: Intent, a critical cognitive notion and mental state, is ubiquitous in human communication and problem-solving. Accurately understanding the underly

In Context Learning and Reasoning for Symbolic Regression with Large Language Models

arXiv:2410.17448v3 Announce Type: replace Abstract: Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not e

Internal Knowledge Without External Expression: Probing the Generalization Boundary of a Classical Chinese Language Model

arXiv:2604.14180v1 Announce Type: new Abstract: We train a 318M-parameter Transformer language model from scratch on a curated corpus of 1.56 billion tokens of pure Classical Chinese, with zero Englis

Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing e

Label-efficient underwater species classification with logistic regression on frozen foundation model embeddings

arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

arXiv:2604.15065v1 Announce Type: new Abstract: Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, wh

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence

arXiv:2512.04578v3 Announce Type: replace Abstract: Legal general intelligence (GI) refers to artificial intelligence (AI) that encompasses legal understanding, reasoning, and decision-making, simulat

Listen, Correct, and Feed Back: Spoken Pedagogical Feedback Generation

arXiv:2604.14177v1 Announce Type: new Abstract: Grammatical error correction (GEC) and explanation (GEE) have made rapid progress, but real teaching scenarios also require learner-friendly pedagogical

LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text

arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems

arXiv:2601.14053v2 Announce Type: replace-cross Abstract: The field of artificial intelligence has undergone a revolution from foundational Transformer architectures to reasoning-capable systems appro

LLMs Gaming Verifiers: RLVR can Lead to Reward Hacking

arXiv:2604.15149v1 Announce Type: new Abstract: As reinforcement Learning with Verifiable Rewards (RLVR) has become the dominant paradigm for scaling reasoning capabilities in LLMs, a new failure mode