Search: “model-releases”

Neural networks for Text-to-Speech evaluation

arXiv:2604.08562v1 Announce Type: cross Abstract: Ensuring that Text-to-Speech (TTS) systems deliver human-perceived quality at scale is a central challenge for modern speech technologies. Human subje

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Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima

DGX agent

arXiv:2604.09258v1 Announce Type: new Abstract: Pretraining is the cornerstone of Large Language Models (LLMs), dominating the vast majority of computational budget and data to serve as the primary en

model-releasesarxiv-cs-lg

13 Apr 2026

Noise-Aware In-Context Learning for Hallucination Mitigation in ALLMs

arXiv:2604.09021v1 Announce Type: cross Abstract: Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliab

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)

arXiv:2604.09030v1 Announce Type: new Abstract: This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchm

On Semiotic-Grounded Interpretive Evaluation of Generative Art

arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs

arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c

Online Intention Prediction via Control-Informed Learning

arXiv:2604.09303v1 Announce Type: cross Abstract: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is

PACED: Distillation and On-Policy Self-Distillation at the Frontier of Student Competence

arXiv:2603.11178v3 Announce Type: replace Abstract: Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve.

Parameterized Complexity Of Representing Models Of MSO Formulas

arXiv:2604.08707v1 Announce Type: new Abstract: Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the probl

PhysInOne: Visual Physics Learning and Reasoning in One Suite

arXiv:2604.09415v1 Announce Type: cross Abstract: We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike exi

PilotBench: A Benchmark for General Aviation Agents with Safety Constraints

arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained

PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos

arXiv:2604.08991v1 Announce Type: cross Abstract: Small object-centric spatial understanding in indoor videos remains a significant challenge for multimodal large language models (MLLMs), despite its

Precise Shield: Explaining and Aligning VLLM Safety via Neuron-Level Guidance

arXiv:2604.08881v1 Announce Type: new Abstract: In real-world deployments, Vision-Language Large Models (VLLMs) face critical challenges from multilingual and multimodal composite attacks: harmful ima

Precomputing Multi-Agent Path Replanning using Temporal Flexibility

arXiv:2601.04884v2 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to

Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search

arXiv:2604.08598v1 Announce Type: cross Abstract: Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotati

Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos

arXiv:2508.04853v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including

QARIMA: A Quantum Approach To Classical Time Series Analysis

arXiv:2604.08277v2 Announce Type: replace-cross Abstract: We present a quantum-inspired ARIMA methodology that integrates quantum-assisted lag discovery with fixed-configuration variational quantum ci

QoS-QoE Translation with Large Language Model

arXiv:2604.08703v1 Announce Type: cross Abstract: QoS-QoE translation is a fundamental problem in multimedia systems because it characterizes how measurable system and network conditions affect user-p

QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation

arXiv:2604.08570v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks

Quantisation Reshapes the Metacognitive Geometry of Language Models

arXiv:2604.08976v1 Announce Type: new Abstract: We report that model quantisation restructures domain-level metacognitive efficiency in LLMs rather than degrading it uniformly. Evaluating Llama-3-8B-I

R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII

arXiv:2604.08810v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is

RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models

arXiv:2511.19704v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation (OVSS) underpins many vision and robotics tasks that require generalizable semantic understanding. Existing ap

RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection

arXiv:2604.08739v1 Announce Type: cross Abstract: Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ran

Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization

arXiv:2602.02188v2 Announce Type: replace Abstract: While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO

ReplicatorBench: Benchmarking LLM Agents for Replicability in Social and Behavioral Sciences

arXiv:2602.11354v2 Announce Type: replace Abstract: The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers. Existing benchmarks focus primarily on

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

arXiv:2510.17640v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable performance on complex tasks through imitation learning in recent robotic man

Retrieval Augmented Classification for Confidential Documents

arXiv:2604.08628v1 Announce Type: cross Abstract: Unauthorized disclosure of confidential documents demands robust, low-leakage classification. In real work environments, there is a lot of inflow and

Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios

arXiv:2509.20006v3 Announce Type: replace Abstract: With the large models easing the labor-intensive manipulation process, image manipulations in today's real scenarios often entail a complex manipula

Robust Reasoning Benchmark

arXiv:2604.08571v1 Announce Type: cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly ov

SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning

arXiv:2604.09452v1 Announce Type: cross Abstract: Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments

SAGE: A Service Agent Graph-guided Evaluation Benchmark

arXiv:2604.09285v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Ex

SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment

arXiv:2604.08988v1 Announce Type: new Abstract: Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, faili

See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models

arXiv:2512.02231v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are expected to jointly interpret vision, audio, and language, yet existing video benchmarks rarely a

Seeing is Believing: Robust Vision-Guided Cross-Modal Prompt Learning under Label Noise

arXiv:2604.09532v1 Announce Type: cross Abstract: Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual conten

Semantic Rate-Distortion for Bounded Multi-Agent Communication: Capacity-Derived Semantic Spaces and the Communication Cost of Alignment

arXiv:2604.09521v1 Announce Type: cross Abstract: When two agents of different computational capacities interact with the same environment, they need not compress a common semantic alphabet differentl

SenBen: Sensitive Scene Graphs for Explainable Content Moderation

arXiv:2604.08819v1 Announce Type: cross Abstract: Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behav

Sentiment Classification of Gaza War Headlines: A Comparative Analysis of Large Language Models and Arabic Fine-Tuned BERT Models

arXiv:2604.08566v1 Announce Type: new Abstract: This study examines how different artificial intelligence architectures interpret sentiment in conflict-related media discourse, using the 2023 Gaza War

SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding

arXiv:2507.20185v2 Announce Type: replace Abstract: Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an us

SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos

arXiv:2604.09037v1 Announce Type: cross Abstract: Current video benchmarks for multimodal large language models (MLLMs) focus on event recognition, temporal ordering, and long-context recall, but over

SimScale: Learning to Drive via Real-World Simulation at Scale

arXiv:2511.23369v3 Announce Type: replace Abstract: Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-di

Skill-Conditioned Visual Geolocation for Vision-Language

arXiv:2604.09025v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown a promising ability in image geolocation, but they still lack structured geographic reasoning and the capacit

Skip-Connected Policy Optimization for Implicit Advantage

arXiv:2604.08690v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has proven effective in RLVR by using outcome-based rewards. While fine-grained dense rewards can theoretica

SPASM: Stable Persona-driven Agent Simulation for Multi-turn Dialogue Generation

arXiv:2604.09212v1 Announce Type: new Abstract: Large language models are increasingly deployed in multi-turn settings such as tutoring, support, and counseling, where reliability depends on preservin

Spectral Geometry of LoRA Adapters Encodes Training Objective and Predicts Harmful Compliance

arXiv:2604.08844v1 Announce Type: new Abstract: We study whether low-rank spectral summaries of LoRA weight deltas can identify which fine-tuning objective was applied to a language model, and whether

Spectral-Transport Stability and Benign Overfitting in Interpolating Learning

arXiv:2604.08625v1 Announce Type: cross Abstract: We develop a theoretical framework for generalization in the interpolating regime of statistical learning. The central question is why highly overpara

SPP-SBL: Space-Power Prior Sparse Bayesian Learning for Block Sparse Recovery

arXiv:2505.08518v2 Announce Type: replace-cross Abstract: The recovery of block-sparse signals with unknown structural patterns remains a fundamental challenge in structured sparse signal reconstructi

STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding

arXiv:2604.09000v1 Announce Type: new Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memo