Search: “model-releases”

SeekerGym: A Benchmark for Reliable Information Seeking

arXiv:2604.17143v1 Announce Type: new Abstract: Despite their substantial successes, AI agents continue to face fundamental challenges in terms of trustworthiness. Consider deep research agents, taske

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Model Releases

Semantically Stable Image Composition Analysisvia Saliency and Gradient Vector Flow Fusion

DGX agent

arXiv:2604.16500v1 Announce Type: new Abstract: The reliable computational assessment of photographic composition requires features that are discriminative of spatial layout yet robust to semantic con

model-releasesarxiv-cs-cv

21 Apr 2026

SemMorph3D: Unsupervised Semantic-Aware 3D Morphing via Mesh-Guided Gaussians

arXiv:2510.02034v2 Announce Type: replace Abstract: We introduce METHODNAME, a novel framework for semantic-aware 3D shape and texture morphing directly from multi-view images. While 3D Gaussian Splat

SetFlow: Generating Structured Sets of Representations for Multiple Instance Learning

arXiv:2604.16362v1 Announce Type: cross Abstract: Data scarcity and weak supervision continue to limit the performance of machine learning models in many real-world applications, such as mammography,

SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation

arXiv:2511.10370v2 Announce Type: replace Abstract: Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We int

SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models

arXiv:2604.17041v1 Announce Type: new Abstract: The public accessibility of large vision-language models (LVLMs) raises serious concerns about unauthorized model reuse and intellectual property infrin

SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention

arXiv:2604.17324v1 Announce Type: new Abstract: Graph transformers achieve strong results on molecular and long-range reasoning tasks, yet remain hampered by over-smoothing (the progressive collapse o

SinkRouter: Sink-Aware Routing for Efficient Long-Context Decoding in Large Language and Multimodal Models

arXiv:2604.16883v1 Announce Type: new Abstract: In long-context decoding for LLMs and LMMs, attention becomes increasingly memory-bound because each decoding step must load a large amount of KV-cache

Sky2Ground: A Benchmark for Site Modeling under Varying Altitude

arXiv:2603.13740v3 Announce Type: replace Abstract: We introduce Sky2Ground, a three-view dataset designed for varying altitude camera localization, correspondence learning, and reconstruction. The da

Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity

arXiv:2604.18058v1 Announce Type: new Abstract: We introduce Sonata, a compact latent world model for six-axis trunk IMU representation learning under clinical data scarcity. Clinical cohorts typicall

Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models

arXiv:2604.17873v1 Announce Type: new Abstract: Video Large Language Models (Vid-LLMs) have demonstrated remarkable performance in video understanding tasks, yet their robustness under conversational

SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues?

arXiv:2601.04029v2 Announce Type: replace Abstract: Large Audio-Language Models (LALMs) as judges have emerged as a prominent approach for evaluating speech generation quality, yet their ability to as

Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection

arXiv:2601.06498v2 Announce Type: replace Abstract: Due to the limited generalization and interpretability of deep learning classifiers, The final vetting of rare celestial object candidates still rel

SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation

arXiv:2601.04638v2 Announce Type: replace Abstract: Medical consultations are intrinsically speech-centric. However, most prior works focus on long-text-based interactions, which are cumbersome and pa

SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks

arXiv:2604.17771v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance on natural language to SQL (NL2SQL) benchmarks, yet their reported accuracy may be inflate

Spike-NVPT: Learning Robust Visual Prompts via Bio-Inspired Temporal Filtering and Discretization

arXiv:2604.18284v1 Announce Type: new Abstract: Pre-trained vision models have found widespread application across diverse domains. Prompt tuning-based methods have emerged as a parameter-efficient pa

SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion

arXiv:2602.11698v2 Announce Type: replace Abstract: Recursive (looped) Transformers decouple computational depth from parameter depth by repeatedly applying shared layers, providing an explicit archit

SQL Query Engine: A Self-Healing LLM Pipeline for Natural Language to PostgreSQL Translation

arXiv:2604.16511v1 Announce Type: cross Abstract: We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through

Stable On-Policy Distillation through Adaptive Target Reformulation

arXiv:2601.07155v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however,

StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement

model-releasesarxiv-cs-ro

arXiv:2604.17887v1 Announce Type: new Abstract: Inverse Dynamics Models (IDMs) map visual observations to low-level action commands, serving as central components for data labeling and policy executio

STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs

arXiv:2604.18177v1 Announce Type: new Abstract: Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

arXiv:2604.18401v1 Announce Type: new Abstract: General agents have given rise to phenomenal applications such as OpenClaw and Claude Code. As these agent systems (a.k.a. Harnesses) strive for bolder

Stochastic Control Methods for Optimization

arXiv:2601.01248v4 Announce Type: replace-cross Abstract: In this work, we investigate a stochastic control framework for global optimization over both Euclidean spaces and the Wasserstein space of pr

Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval

arXiv:2604.17782v1 Announce Type: new Abstract: Zero-shot EEG-to-image retrieval aims to decode perceived visual content from electroencephalography (EEG) by aligning neural responses with pretrained

SynthPID: P&ID digitization from Topology-Preserving Synthetic Data

arXiv:2604.16513v1 Announce Type: new Abstract: Automating the digitization of Piping and Instrumentation Diagrams (P&IDs) into structured process graphs would unlock significant value in plant operat

Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks

arXiv:2604.17159v1 Announce Type: cross Abstract: We present, to our knowledge, the most comprehensive cross-model evaluation of LLM agents on offensive cybersecurity tasks, benchmarking 10 frontier m

TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation

arXiv:2603.02972v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch: V

Tape: A Cellular Automata Benchmark for Evaluating Rule-Shift Generalization in Reinforcement Learning

arXiv:2601.04695v2 Announce Type: replace-cross Abstract: Out-of-distribution generalization in reinforcement learning is hard to diagnose when benchmark shifts mix dynamics, observations, goals, and

TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications

arXiv:2604.17778v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Gener

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604.16926v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet

The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus

arXiv:2604.16913v1 Announce Type: cross Abstract: Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposa

The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

arXiv:2604.17306v1 Announce Type: new Abstract: This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the result

The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability

arXiv:2604.17698v1 Announce Type: cross Abstract: Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a mo

The Illusion of Insight in Reasoning Models

arXiv:2601.00514v2 Announce Type: replace-cross Abstract: Do reasoning models have 'Aha!' moments? Prior work suggests that models like DeepSeek-R1-Zero undergo sudden mid-trace realizations that lead

The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias

arXiv:2412.02271v4 Announce Type: replace Abstract: We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild

The Virtue of Sparsity in Complexity

arXiv:2604.17166v1 Announce Type: cross Abstract: Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: richer feature spaces appear to favo

TimeColor: Flexible Reference Colorization via Temporal Concatenation

arXiv:2601.00296v2 Announce Type: replace Abstract: Most colorization models condition only on a single reference, typically the first frame of the scene. However, this approach ignores other sources

TinySR: Pruning Diffusion for Real-World Image Super-Resolution

arXiv:2508.17434v2 Announce Type: replace Abstract: Real-world image super-resolution (Real-ISR) focuses on recovering high-quality images from low-resolution inputs that suffer from complex degradati

TLoRA: Task-aware Low Rank Adaptation of Large Language Models

arXiv:2604.18124v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data

arXiv:2604.18493v1 Announce Type: new Abstract: Reinforcement Learning (RL) enhances LLM reasoning, yet a paradox emerges as models scale: strong base models saturate standard benchmarks (e.g., MATH),

Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks

arXiv:2512.12642v2 Announce Type: replace Abstract: Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernod

Towards a Data-Parameter Correspondence for LLMs: A Preliminary Discussion

arXiv:2604.17384v1 Announce Type: new Abstract: Large language model optimization has historically bifurcated into isolated data-centric and model-centric paradigms: the former manipulates involved sa

Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis

arXiv:2604.16888v1 Announce Type: new Abstract: Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergenc

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604.17376v1 Announce Type: new Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generali

Towards Joint Quantization and Token Pruning of Vision-Language Models

arXiv:2604.17320v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) under aggressive low-bit inference remains challenging because inference cost is dominated by the long visual-to

Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training

arXiv:2603.23885v3 Announce Type: replace Abstract: Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Tradit

TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework

arXiv:2604.16848v1 Announce Type: new Abstract: Fine-grained semantic segmentation of transmission-corridor point clouds is fundamental for intelligent power-line inspection. However, current progress

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

arXiv:2508.11281v3 Announce Type: replace Abstract: Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in