Search: “models”

Improving Complex Moire Removal with Generative Supervision

arXiv:2608.17883v1 Announce Type: new Abstract: The availability of high-quality paired data is essential for training learning-based image demoireing models. However, it remains challenging for exist

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Model Releases

Key-Frame Reasoning with SAM3: Third Place Solution for the MeViS-Text Track of the 8th LSVOS Challenge

DGX agent

arXiv:2608.17279v1 Announce Type: new Abstract: This report presents a two-stage, training-free solution for the MeViS-Text track of the 8th LSVOS Challenge. The task requires a model to localize and

model-releasesarxiv-cs-cv

19 Aug 2026

Leveraging existing sparse point annotations for benthic imagery dense segmentation

arXiv:2608.17561v1 Announce Type: new Abstract: The health of marine ecosystems is a critical indicator of global environmental change, yet the physical constraints of underwater observation and the i

Likelihood Hacking in Probabilistic Program Synthesis

arXiv:2603.24126v2 Announce Type: replace Abstract: When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likeli

safetyarxiv-cs-lg

MANIGUARD: A Benchmark and Data Suite for Specification-Grounded Safety Evaluation and Improvement of Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.17386v1 Announce Type: new Abstract: Foundation-model policies for robotic manipulation are advancing rapidly on task success, but rigorous evaluation of whether they succeed safely is stil

Local Ai

Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals

arXiv:2608.17687v1 Announce Type: new Abstract: Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known

local-aiarxiv-cs-ai

Applications

Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication

arXiv:2608.17246v1 Announce Type: new Abstract: This article investigates several physics-informed and hybrid machine learning strategies that incorporate physics knowledge in experimental data-driven

applicationsarxiv-cs-lg

Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents

arXiv:2608.18008v1 Announce Type: cross Abstract: Combining large language models with reinforcement learning is increasingly explored, yet the theoretical status of LLM-derived reward signals is ofte

Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations

arXiv:2608.16970v1 Announce Type: cross Abstract: LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc -- static analyzers, f

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

arXiv:2608.17379v1 Announce Type: cross Abstract: We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimizati

Q-Interference: Memory-Efficient Phase-Aware Quantum-Inspired Attention

arXiv:2608.17288v1 Announce Type: new Abstract: GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not ex

S^3AM: A Single-Stream SAM with Reliability-Calibrated Frequency Adapter for Multi-modal Salient Object Detection

arXiv:2608.17475v1 Announce Type: new Abstract: Vision foundation models have recently advanced multi-modal salient object detection (MSOD) through parameter-efficient tuning and prompt learning. Howe

SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version

model-releasesarxiv-cs-lg

arXiv:2608.17164v1 Announce Type: new Abstract: Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by

Teach and Grow: An Agent-Centered Architecture for General Robot Learning

arXiv:2608.17209v1 Announce Type: cross Abstract: End-to-end vision-language-action (VLA) and world-action models offer an elegant route to general-purpose robotics, but their reliability is bounded b

Towards Safer RAG: Only Agents Capable of System 2 Thinking may Access Untrusted Documents

arXiv:2608.17153v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has significantly enhanced the performance of large language models (LLMs), yet these systems remain vulnerable to

researcharxiv-cs-cl

A Large-Scale Chinese Knowledge Graph-Text Alignment Dataset for Benchmarking Knowledge-Grounded LLMs

arXiv:2510.06039v2 Announce Type: replace-cross Abstract: Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language te

A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction

arXiv:2608.14750v1 Announce Type: cross Abstract: Left ventricular ejection fraction (LVEF) estimation (Task 1), global longitu-dinal strain (GLS)-based dysfunction classification (Task 2), and early

Aborted but Not Forgotten: KV-Cache Retention Breaks Rollback Consistency in Language Agents

arXiv:2608.15939v1 Announce Type: new Abstract: Stateful language agents assume a rejected branch can be taken back by clearing it from the application transcript. We show this breaks when the serving

Benchmarking Identity-Sensitive LLM Outputs for Surveillance and Security Robots

model-releasesarxiv-cs-ro

arXiv:2608.16030v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate textual robot design specifications, interaction policies, and risk assessments during ea

Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior

arXiv:2608.16196v1 Announce Type: new Abstract: Personalized game generation requires inferring a player's abilities and behavioral style from how they play. Large language models have made this infer

Characterization of Thermal Systems from Noisy and Low-resolution Measurements Using Dynamic Mode Decomposition

model-releasesarxiv-cs-lg

arXiv:2608.14581v1 Announce Type: cross Abstract: Thermal monitoring in practical applications is often constrained by sparse sensing, measurement noise, and limited spatial resolution, which hinder t

Decorrelation Is Not Complementarity: Skill, Not Lineage, Governs Trusted-Monitor Ensembles

arXiv:2608.16190v1 Announce Type: cross Abstract: Trusted monitoring has a cheap, trusted model score a stronger untrusted model's actions, and a diverse ensemble of them beats a single stronger monit

researcharxiv-cs-lg

DeepInsight II: One Trace from Benchmark to Robot

arXiv:2608.16556v1 Announce Type: new Abstract: Across a Physical AI stack, evaluation maturity is inversely aligned with deployment risk: foundation models enjoy mature, standardized harnesses, while

Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection

arXiv:2608.16259v1 Announce Type: cross Abstract: The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliab

DepthArb: Training-Free Depth-Arbitrated Generation for Occlusion-Robust Image Synthesis

arXiv:2603.23924v2 Announce Type: replace Abstract: Text-to-image models often struggle to synthesize correct occlusion relationships among multiple objects, especially in densely overlapping regions.

Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking

arXiv:2608.14808v1 Announce Type: new Abstract: When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it,

researcharxiv-cs-ai

Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics

arXiv:2608.16084v1 Announce Type: new Abstract: Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error g

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

arXiv:2608.15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and

FusionBERT: Multi-View Image--3D Retrieval via Cross-Attention Visual Fusion and Normal-Aware 3D Encoder

arXiv:2604.02583v2 Announce Type: replace Abstract: We propose FusionBERT, a novel multi-view visual fusion framework for image--3D multimodal retrieval. Existing image--3D representation learning met

safetyarxiv-cs-cv

Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance

arXiv:2608.16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task. We ask whether it finished it within policy. We introduce Fiducia-bench, a benchmark

HarnessEval-W: Agentifying the Evaluation of Visual Worlds

arXiv:2608.16859v1 Announce Type: new Abstract: A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially

Incoherent by Design? On the Moral Self-Consistency of LLMs

arXiv:2608.15354v1 Announce Type: new Abstract: LLMs are increasingly used in morally sensitive contexts, yet it is unclear whether they apply ethical principles consistently across situations. A mode

INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval

arXiv:2608.16203v1 Announce Type: cross Abstract: Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmar

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506.07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG)

LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks

arXiv:2608.14927v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collabora

MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration

arXiv:2601.23049v2 Announce Type: replace Abstract: Medical calculators are fundamental to quantitative, evidence-based clinical practice. However, their real-world use is an adaptive, multi-stage pro

MIRROR: Multimodal Intelligent Radiology Reasoning and Observation Reporter

arXiv:2608.16709v1 Announce Type: cross Abstract: A radiologist reading a model's output faces two problems. The model returns a number and no reason, and any system that turns that number into readab

researcharxiv-cs-ai

Misconception Diagnosis From Student-Tutor Dialogue: Generate, Retrieve, Rerank

arXiv:2602.02414v2 Announce Type: replace Abstract: Timely and accurate identification of student misconceptions is key to improving learning outcomes and pre-empting the compounding of student errors

MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios

arXiv:2505.21333v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved considerable accuracy in Optical Character Recognition (OCR) from static images. However, the

Muse Glimmer 30B is now available on Fireworks' Dedicated Training API for both LoRA and Full-Parameter fine-tuning. This is a U.S.-develope…

model-releasesfireworks-ai--x

Muse Glimmer 30B is now available on Fireworks' Dedicated Training API for both LoRA and Full-Parameter fine-tuning. This is a U.S.-developed, open-weight model and one of the strongest of its size fo

Neurosymbolic Embodied Agents

arXiv:2608.16794v1 Announce Type: cross Abstract: Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dyn

Local Ai

Perspective-Invariant Attack with Enhanced Transferability of Adversarial Examples

arXiv:2608.15115v1 Announce Type: new Abstract: Adversarial examples generated on a surrogate deep neural network (DNN) can often successfully fool other black-box DNN models. This cross-model transfe

local-aiarxiv-cs-cv

Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

arXiv:2608.16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a f

Prior Audit-Repair Context Shifts LLM Verifier Thresholds Toward Leniency

arXiv:2608.16003v1 Announce Type: new Abstract: Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer. We ask whether that wiring

researcharxiv-cs-ai

PRISM: Streaming Human Motion Generation with Per-Joint Latent Decomposition

arXiv:2603.08590v3 Announce Type: replace Abstract: Text-to-motion generation has advanced with larger corpora and stronger generators, yet many models still rely on holistic frame- or clip-level late

Applications

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

arXiv:2509.10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensit

applicationsarxiv-cs-ai

Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026

arXiv:2608.16318v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and

ROC-n-reroll: How verifier imperfection affects test-time scaling

model-releasesarxiv-cs-lg

arXiv:2507.12399v3 Announce Type: replace Abstract: Test-time scaling aims to improve language model performance by leveraging additional compute during inference. Many works have empirically studied