Search: “model-releases”

CoDial: Interpretable Task-Oriented Dialogue Systems Through Dialogue Flow Alignment

arXiv:2506.02264v3 Announce Type: replace Abstract: Building Task-Oriented Dialogue (TOD) systems that generalize across different tasks remains a challenging problem. Data-driven approaches often str

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Model Releases

Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situations

DGX agent

arXiv:2507.20409v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting helps models think step by step. But naive CoT breaks down in visually grounded social tasks, where models must per

model-releasesarxiv-cs-cl

21 Apr 2026

CoLLM: A Unified Framework for Co-execution of LLMs Federated Fine-tuning and Inference

arXiv:2604.16400v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly adopted in edge intelligence to power domain-specific applications and personalized services, the qua

Combined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning

arXiv:2510.05643v2 Announce Type: replace Abstract: Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data p

ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship

arXiv:2604.18356v1 Announce Type: new Abstract: Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While r

Complex normalizing flows can be information Kahler-Ricci flows

arXiv:2604.17954v1 Announce Type: cross Abstract: We develop interconnections between the complex normalizing flow for data drawn from Borel probability measures on the twofold realification of the co

Concurrent Criterion Validation of a Validity Screen for LLM Confidence Signals via Selective Prediction

arXiv:2604.17716v1 Announce Type: new Abstract: The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid. We test whether these classifications

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604.17616v1 Announce Type: new Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems. Existing explanation m

Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees

arXiv:2604.01502v2 Announce Type: replace-cross Abstract: Conformal risk control (CRC) provides distribution-free guarantees for controlling the expected loss at a user-specified level. Existing theor

ConMeZO: Adaptive Descent-Direction Sampling for Gradient-Free Finetuning of Large Language Models

arXiv:2511.02757v2 Announce Type: replace Abstract: Zeroth-order or derivative-free optimization (MeZO) is an attractive strategy for finetuning large language models (LLMs) because it eliminates the

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

arXiv:2510.09741v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) often miss small details and spatial relations in cluttered scenes, leading to errors in fine-grained perce

Continuous Limits of Coupled Flows in Representation Learning

arXiv:2604.16801v1 Announce Type: new Abstract: While modern representation learning relies heavily on global error signals, decentralized algorithms driven by local interactions offer a fundamental d

Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining

arXiv:2604.17633v1 Announce Type: new Abstract: Large language models exhibit impressive cross-lingual capabilities. However, prior work analyzes this phenomenon through isolated factors and at sparse

CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks

arXiv:2404.03191v3 Announce Type: replace Abstract: Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems resea

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601.14590v2 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine

Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs

arXiv:2510.07591v3 Announce Type: replace Abstract: We present a system that uses LLMs as a tool in the development of Constructed Languages -- ConLangs, which we call IASC (Interactive Agentic System

CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit

arXiv:2510.06133v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms

CROC: Evaluating and Training T2I Metrics with Pseudo- and Human-Labeled Contrastive Robustness Checks

arXiv:2505.11314v2 Announce Type: replace-cross Abstract: The assessment of evaluation metrics (meta-evaluation) is crucial for determining the suitability of existing metrics in text-to-image (T2I) g

Cross-Family Speculative Decoding for Polish Language Models on Apple~Silicon: An Empirical Evaluation of Bielik~11B with UAG-Extended MLX-LM

arXiv:2604.16368v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by using a small draft model to propose k candidate tokens for a target model to verify. While effective

Cross-Modal Bayesian Low-Rank Adaptation for Uncertainty-Aware Multimodal Learning

arXiv:2604.16657v1 Announce Type: new Abstract: Large pre-trained language models are increasingly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT), but existing PEFT methods a

D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation

arXiv:2604.16940v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) accelerates taskspecific large language models (LLMs) development, but the resulting proliferation of finetuned models incu

DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training

arXiv:2604.16845v1 Announce Type: new Abstract: Large language models (LLMs) tuned for safety often avoid acknowledging demographic differences, even when such acknowledgment is factually correct (e.g

Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework

arXiv:2510.24074v2 Announce Type: replace-cross Abstract: The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration r

Deep Learning for Virtual Reality User Identification: A Benchmark

arXiv:2604.16341v1 Announce Type: cross Abstract: Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motio

DEM Refinement and Validation on the Lunar Surface Using Shape-from-Shading with Chandrayaan-2 OHRC Imagery

arXiv:2604.17436v1 Announce Type: new Abstract: This study presents a Shape from Shading (SfS) framework to enhance sub-metre resolution lunar digital elevation models (DEMs) using imagery from the Or

Depth Registers Unlock W4A4 on SwiGLU: A Reader/Generator Decomposition

arXiv:2604.18128v1 Announce Type: new Abstract: We study post-training W4A4 quantization in a controlled 300M-parameter SwiGLU decoder-only language model trained on 5B tokens of FineWeb-Edu, and ask

Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network

arXiv:2510.01801v2 Announce Type: replace Abstract: The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These review

DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection

arXiv:2604.17961v1 Announce Type: new Abstract: In this work, we introduce DifFoundMAD, a parameter-efficient D-MAD framework that exploits the generalisation capabilities of vision foundation models

Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining

arXiv:2604.16391v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma-misalignment of 2D image foreca

Do LLM-derived graph priors improve multi-agent coordination?

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion

arXiv:2604.18257v1 Announce Type: cross Abstract: Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQ

Document-as-Image Representations Fall Short for Scientific Retrieval

arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying

Domain-oriented RAG Assessment (DoRA): Synthetic Benchmarking for RAG-based Question Answering on Defense Documents

arXiv:2604.17943v1 Announce Type: new Abstract: Open-domain RAG benchmarks over public corpora can overestimate deployment performance due to pretraining overlap and weak attribution requirements. We

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

arXiv:2604.17244v1 Announce Type: new Abstract: Despite the rapid progress, LLMs for sequential decision-making (i.e., LLM agents) still struggle to produce diverse outputs. This leads to insufficient

DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation

arXiv:2604.17209v1 Announce Type: new Abstract: Automating medical reports for retinal images requires a sophisticated blend of visual pattern recognition and deep clinical knowledge. Current Large Vi

DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking

arXiv:2507.20879v3 Announce Type: replace Abstract: The advent of Vision-Language Models (VLMs) has significantly advanced end-to-end autonomous driving, demonstrating powerful reasoning abilities for

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation

arXiv:2603.08090v2 Announce Type: replace Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjec

Dual-stream Spatio-Temporal GCN-Transformer Network for 3D Human Pose Estimation

arXiv:2604.17688v1 Announce Type: new Abstract: 3D human pose estimation is a classic and important research direction in the field of computer vision. In recent years, Transformer-based methods have

DuConTE: Dual-Granularity Text Encoder with Topology-Constrained Attention for Text-attributed Graphs

arXiv:2604.17411v1 Announce Type: new Abstract: Text-attributed graphs integrate semantic information of node texts with topological structure, offering significant value in various applications such

DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization

arXiv:2604.17789v1 Announce Type: cross Abstract: The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrat

E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes

arXiv:2604.17969v1 Announce Type: new Abstract: Visual search in 3D environments requires embodied agents to actively explore their surroundings and acquire task-relevant evidence. However, existing v

EasyVideoR1: Easier RL for Video Understanding

arXiv:2604.16893v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large languag

EchoChain: A Full-Duplex Benchmark for State-Update Reasoning Under Interruptions

arXiv:2604.16456v1 Announce Type: new Abstract: Real-time voice assistants must revise task state when users interrupt mid-response, but existing spoken-dialog benchmarks largely evaluate turn-based i

EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning

arXiv:2509.20360v3 Announce Type: replace Abstract: Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. W

Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization

arXiv:2604.17051v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated excellent performance in general language understanding, generation and other tasks. However, when fine-t

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher

Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models

arXiv:2604.18167v1 Announce Type: new Abstract: Modern text-to-image (T2I) models amplify harmful societal biases, challenging their ethical deployment. We introduce an inference-time method that reli

EmbodiedLGR: Integrating Lightweight Graph Representation and Retrieval for Semantic-Spatial Memory in Robotic Agents

model-releasesarxiv-cs-ro

arXiv:2604.18271v1 Announce Type: new Abstract: As the world of agentic artificial intelligence applied to robotics evolves, the need for agents capable of building and retrieving memories and observa