Search: “model-releases”

DS@GT ARC at ImageCLEFmedical 2026: Architectural Diversity for Concept Detection and Foundation-Model Scaling for Caption Prediction in Medical Image Analysis

arXiv:2607.27763v1 Announce Type: new Abstract: We describe the DS@GT submissions to the ImageCLEFmedical Caption 2026 challenge, which continues a long-running benchmark on the ROCOv2 dataset with tw

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ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders

arXiv:2607.27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

arXiv:2607.28074v1 Announce Type: cross Abstract: Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that mat

EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits

arXiv:2607.27857v1 Announce Type: new Abstract: Recent EEG-to-image retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such succes

Efficient LLMs with AMP: Attention Heads and MLP Pruning

arXiv:2504.21174v2 Announce Type: replace Abstract: Deep learning drives a new wave in computing systems and triggers the automation of increasingly complex problems. In particular, Large Language Mod

EgoGVAE: Ego-body Mesh Reconstruction via Guided Variational Autoencoder

arXiv:2607.27755v1 Announce Type: new Abstract: We address the problem of recovering the full-body mesh from only the head pose. This task has become essential for various applications based on head-m

EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception

arXiv:2504.16616v4 Announce Type: replace Abstract: Event cameras, characterized by microsecond temporal resolution and very High Dynamic Range (HDR), emit high-speed event streams for perception task

EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents

arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines

Epistemic diversity across language models mitigates knowledge collapse

arXiv:2512.15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems. This feedback loop can degrade model quality,

eta-OPSD: Deriving with Policy Optimization, Training with Self-Distillation

arXiv:2607.28582v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reli

Evidence-Ledger Adjudication for Claim-Evidence Traceability

arXiv:2607.26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them. We study evidence-ledger adjudication: a

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

arXiv:2607.27290v1 Announce Type: new Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identif

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

arXiv:2607.27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators. Model

Expected Survival-Time Bounds for Robust Optimization Over Time under Isotropic Gaussian Dynamics

arXiv:2607.27280v1 Announce Type: cross Abstract: Robust Optimization Over Time (ROOT) is a recent branch of evolutionary dynamic optimization that seeks solutions capable of remaining effective acros

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

arXiv:2607.27372v1 Announce Type: cross Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages. Generat

Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?

arXiv:2607.26367v1 Announce Type: new Abstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent t

Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins

arXiv:2607.09306v3 Announce Type: replace Abstract: Behavioural auditing asks whether a language model behaves as it claims, but detection scores are reported without separating two targets: whether a

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations

arXiv:2607.28319v1 Announce Type: new Abstract: This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias

Fantastic Adaptive Taxonomies and How to Use Them

arXiv:2607.16387v2 Announce Type: replace-cross Abstract: An agent system's execution traces record how it fails, and procedures that improve such a system without changing model weights (trajectory s

Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants

arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in us

FinanceHarness: Autonomous Financial Deep Research Framework

arXiv:2607.27853v1 Announce Type: new Abstract: Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research

Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents

arXiv:2607.27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless. We test this claim for multi-turn, tool-calling agents, where it n

FlexiGrad: Adaptive Gradient Modulation for Hierarchical Fine-Grained Classification

arXiv:2607.17563v2 Announce Type: replace Abstract: Many fine-grained recognition tasks contain hierarchical labels such as order, family and species. Although this supervision should be beneficial, j

FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing

arXiv:2508.02092v3 Announce Type: replace-cross Abstract: Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable

From Expert Reduction to Behavioral Divergence: Tracing Numerical State through Sparse MoE Inference

arXiv:2607.28097v1 Announce Type: new Abstract: Mathematically equivalent expert-reduction orders can produce observably different sparse-MoE executions. We isolate this effect in native DeepSeek-V4-F

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

arXiv:2607.28568v1 Announce Type: new Abstract: Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a

Generalization and Trade-off in Adversarial Training: An RKHS Perspective via Kernel Integral Operators

arXiv:2607.27995v1 Announce Type: cross Abstract: Adversarial training has emerged as a powerful approach for protecting models against adversarial attacks in a broad range of real-world applications.

Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search

arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,

Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning

arXiv:2607.27766v1 Announce Type: new Abstract: On-device in-context learning (ICL) relies on pre-inference retrieval to select demonstrations for useful context before downstream model inference. Thi

Graph Neural Multilevel Preconditioners for Iterative Solvers

arXiv:2607.28456v1 Announce Type: cross Abstract: Solving large, sparse linear systems is a core task in scientific computing, and efficient iterative solvers rely critically on effective and robust p

Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets

arXiv:2607.28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically re

GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning

arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather t

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference

arXiv:2607.27694v1 Announce Type: cross Abstract: Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. Ho

Hallucinations and Truth: A Comprehensive Accuracy Evaluation of RAG, LoRA and DoRA

arXiv:2502.10497v2 Announce Type: replace Abstract: Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, parti

HARGO: Heterogeneity-Aware Reward-Guided Optimization for RL Post-Training of LLMs on HPC Tasks

arXiv:2607.28301v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) can equip large language models (LLMs) with domain knowledge for high-performance computing (HPC) tasks such as data race d

IDP AutoOpt: Agent-Driven Optimization of Document Processing Pipeline Configurations

arXiv:2607.26075v1 Announce Type: cross Abstract: We present IDP AutoOpt, an autonomous LLM agent that discovers high-performing configurations for intelligent document processing (IDP) pipelines. Tun

IFCMemoryBench: Evaluating Long-Term Memory of LLM-Based Agents in BIM Information Retrieval

arXiv:2607.26072v1 Announce Type: cross Abstract: Long-term memory is becoming a core capability of LLM-based agents, but existing evaluations largely test conversational recall in open-domain or pers

IFHierBench: Hierarchical Instruction Following for Large Language Models

arXiv:2607.27912v1 Announce Type: cross Abstract: Instruction-following ability is critical for deploying large language models in real-world applications, where downstream components depend on the ou

JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles

arXiv:2607.27670v1 Announce Type: new Abstract: Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that creat

KAISEN: Reproducible Subgroup Fairness Auditing for Clinical Risk Models

arXiv:2607.28608v1 Announce Type: new Abstract: Clinical risk models routinely achieve strong aggregate performance while producing materially different error rates across patient subgroups. Audit pip

Kalman Meets Curriculum: Efficient Dynamic Prompt Selection for Adaptive RL Finetuning

arXiv:2607.27610v1 Announce Type: new Abstract: Reinforcement learning (RL) finetuning significantly enhances the reasoning capabilities of large language models (LLMs), yet its effectiveness critical

KernelGenBench: A Multi-Source and Multi-Chip Benchmark for LLM-based Kernel Generation

arXiv:2607.27231v1 Announce Type: cross Abstract: Large language models (LLMs) have significantly increased the demand for efficient accelerator kernels, but kernel development remains a highly specia

Language Diversity: Evaluating Language Usage and AI Performance on African Languages in Digital Spaces

arXiv:2512.01557v3 Announce Type: replace Abstract: This study examines the digital representation of African languages and the challenges this presents for current language detection tools. We evalua

LayerRAG-Bench: A Cross-Layer Reliability Benchmark for Agentic Retrieval-Augmented Generation

arXiv:2607.27353v1 Announce Type: new Abstract: Agentic retrieval-augmented generation systems can produce answers that appear grounded while failing at the evidence, tool-contract, authorization, or

Learning-Augmented and Randomized Algorithms for Line Aggregation with Delays

arXiv:2607.27807v1 Announce Type: new Abstract: This paper studies learning-augmented and randomized online aggregation with delays on a line metric. We consider advice given as online suggested servi

Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement

arXiv:2607.27659v1 Announce Type: new Abstract: Personalized image enhancement should reflect individual aesthetic taste, yet learning such preferences commonly depends on private photos and ratings t

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

arXiv:2607.28036v1 Announce Type: new Abstract: It is well known that Newton's method converges faster when the initial guess is closer to a root of a system of nonlinear equations. In this paper, a t