model releases

Depth-guided Multi-view Exposure Bracketing for HDR Robot Vision

arXiv:2608.16014v1 Announce Type: new Abstract: Achieving reliable single-shot high dynamic range (HDR) imaging under extreme illumination conditions remains a long-standing challenge, yet no comprehe

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Model Releases

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

DGX agent

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.

model-releasesarxiv-cs-cv

18 Aug 2026

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

arXiv:2608.15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activi

DFlash 2 available for Qwen 3.8 27B and Muse Glimmer

model-releasesr-localllama

Apparently a second version of DFlash from the original authors of DFlash GGUF quants are already made available with an accompanying llama.cpp PR: https://github.com/ggml-org/llama.cpp/pull/27342 sub

Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Models

arXiv:2608.16805v1 Announce Type: cross Abstract: Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Gen

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models

arXiv:2505.14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing v

Diffusion-model approach to flavor models: A case study for S_4^prime modular flavor model

arXiv:2504.00944v2 Announce Type: replace-cross Abstract: We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models

DirMixE: Harnessing Test Agnostic Long-tail Recognition with Hierarchical Label Variations

arXiv:2405.07780v3 Announce Type: replace-cross Abstract: This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbit

Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task

arXiv:2607.01503v2 Announce Type: replace Abstract: In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and

Do Geometry-Aware Positional Encodings Help Transformers in Spatial Imperfect-Information Games?

arXiv:2608.14982v1 Announce Type: cross Abstract: Transformers applied to spatial imperfect-information games must represent map geometry while tracking hidden entities through time. We ask whether ge

Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP

arXiv:2608.14613v1 Announce Type: new Abstract: Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Go

Do Visual Grounding Decoders Need Feed-Forward Networks? A Controlled Study over Frozen Vision-Language Features

arXiv:2608.15061v1 Announce Type: new Abstract: Do feed-forward networks (FFNs) in visual grounding decoders add essential computation once a pretrained vision-language model has already encoded image

Does a Tool Result Carry More Authority Than Plain Text? Three Prospective Studies of False-Claim Adoption in a Synthetic Assignment Task with Claude Opus 5

arXiv:2608.14992v1 Announce Type: new Abstract: Language-model systems increasingly read from stores they also write to, so a claim that was merely written earlier can return looking retrieved. We tes

Does an Ollama-ready build keep this 27B's MTP and vision stack?

One line in a new Qwen3.8-27B derivative card caught my eye: the uploader says the vision-language tower and MTP head were preserved through its offline block-FP8 build. The checkpoint is OrcaRouter's

model-releasesr-ollama

Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature

arXiv:2607.14882v2 Announce Type: replace-cross Abstract: With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label cl

Does the LM Head Create a Harmful Gradient Bottleneck? A Causal Test

model-releasesarxiv-cs-cl

arXiv:2608.16671v1 Announce Type: new Abstract: The language-model head maps a hidden state of width D to a vocabulary of size V, so its transpose can return at most D independent directions to the Tr

Does your context compactor actually save anything? If you built or tuned one, this one is worth your time. (bookmark it) Task completion is…

Does your context compactor actually save anything? If you built or tuned one, this one is worth your time. (bookmark it) Task completion is the standard way to validate compression, and it hides the

model-releasesdair-ai--x

Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation

model-releasesarxiv-cs-cl

arXiv:2608.16269v1 Announce Type: new Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-trainin

Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory

arXiv:2608.16889v1 Announce Type: cross Abstract: Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the

Don't ignore llama.cpp RPC with old hardware. Results of a 5070 Ti and 1080 Ti over gigabit ethernet: it's actually functional.

model-releasesr-localllama

Results up front: I had to prioritize prefill or token generation - there was no happy medium. Using UD-Q4_K_XL, q8 kv cache, and 96k max context: focus on generation (MTP = 2): 350 pp and 36 tg @ 12k

DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection

arXiv:2608.16632v1 Announce Type: new Abstract: Deep learning-based object detectors are fundamental to intelligent transportation systems, enabling traffic monitoring, vehicle analytics, and infrastr

Drive, Pack, Fly: The Travelling Thief Problem with Drone

arXiv:2608.16435v1 Announce Type: new Abstract: In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency. An onboard drone ca

DUET: Dual-Teacher On-Policy Distillation via Same-Weight Disagreement for Prohibition Compliance

model-releasesarxiv-cs-cl

arXiv:2608.14644v1 Announce Type: cross Abstract: Real-world LLM deployments increasingly rely on runtime-injected prohibitions--enterprise policies, PII redlines, tool boundaries--that vary per reque

DumpsterCluster: From Dumpster Diving to Serving LLaMA-70B on $60 GPUs

arXiv:2608.14614v1 Announce Type: cross Abstract: As AI datacenters retire functional GPUs, vast quantities of still capable accelerators enter secondary markets. This paper investigates whether these

Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference

arXiv:2608.15282v1 Announce Type: cross Abstract: Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process model

Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning

arXiv:2503.09020v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and pro

ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

arXiv:2608.14773v1 Announce Type: cross Abstract: The efficient-KAN literature---covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network---has been be

Evaluating Agentic Code Repair Capabilities in Distributed Systems

arXiv:2608.14863v1 Announce Type: cross Abstract: LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified.

Evidence of conceptual mastery in the application of rules by Large Language Models

arXiv:2503.00992v2 Announce Type: replace Abstract: In this paper we leverage psychological methods to investigate LLMs' conceptual mastery in applying rules. We introduce a novel procedure to match t

Evolving Executable Pipeline Programs for AutoML with Language Models

arXiv:2608.16416v1 Announce Type: new Abstract: Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in

Exact Attention Sensitivity and the Geometry of Transformer Stability

arXiv:2602.18849v2 Announce Type: replace-cross Abstract: We develop a sensitivity analysis for transformer attention in a geometry aligned with tokenwise computation. Our main result is the exact ide

Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR

arXiv:2412.09557v3 Announce Type: replace-cross Abstract: Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in

FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy

arXiv:2608.16697v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogene

Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction

arXiv:2608.15343v1 Announce Type: new Abstract: Reconstructing three-dimensional computed tomography (CT) from severely constrained projections is highly ill-posed. Sparse angular sampling, restricted

Fiber Fingerprints of Hidden Learning-State Dynamics

arXiv:2608.15976v1 Announce Type: new Abstract: A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future

FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection

arXiv:2608.15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational

FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction

arXiv:2608.15727v1 Announce Type: cross Abstract: Diffusion models have recently shown strong potential for multivariate time-series anomaly detection by learning the distribution of normal data throu

FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning

arXiv:2608.16523v1 Announce Type: new Abstract: Event cameras generate asynchronous, high-frequency data streams offering spatially sparse information at lower latency than traditional cameras.In prin

FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge

arXiv:2608.15410v1 Announce Type: cross Abstract: Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offeri

Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic

arXiv:2608.16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research p

Fractional Optimizers Meet Fractal Activation Functions: An Empirical Study of Multi-Scale Optimization in Neural Network

arXiv:2608.14636v1 Announce Type: cross Abstract: Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional opti

Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation

arXiv:2608.15054v1 Announce Type: new Abstract: Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information

From Errors to Proofs: Minimal-Core-Guided Repair for Neuro-Symbolic Constraint Solving

arXiv:2608.14771v1 Announce Type: new Abstract: Making language models solve constraint problems reliably often means having them translate the problem into a formal specification and delegating the s

From Generalist to Specialist: A Context-Fusion Framework for Endoscopic Polyp Reporting with a Frozen VLM

arXiv:2608.15580v1 Announce Type: new Abstract: Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morpho

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

Frontier AI Forecasting Has a Measurement Problem: An Audit of Progress Evidence

arXiv:2608.14903v1 Announce Type: new Abstract: Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or

FZ-VLM: A Two Stage Florence-Zephyr Vision Language Model Framework for Pulmonary Nodule Characterization and Clinical Decision Making

arXiv:2608.15004v1 Announce Type: cross Abstract: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screen

Gated Against One Model, Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks

arXiv:2608.15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question. Measuring that gap takes care: