Search: “model-releases”

Decoupled DiLoCo for Resilient Distributed Pre-training

arXiv:2604.21428v1 Announce Type: new Abstract: Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across

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Model Releases

Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research

DGX agent

arXiv:2604.21006v1 Announce Type: new Abstract: We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research.

model-releasesarxiv-cs-ai

24 Apr 2026

DenoiseRank: Learning to Rank by Diffusion Models

arXiv:2604.20852v1 Announce Type: cross Abstract: Learning to rank (LTR) is one of the core tasks in Machine Learning. Traditional LTR models have made great progress, but nearly all of them are imple

Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles

arXiv:2604.21152v1 Announce Type: cross Abstract: As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. Howev

Differentially Private Model Merging

arXiv:2604.20985v1 Announce Type: cross Abstract: In machine learning applications, privacy requirements during inference or deployment time could change constantly due to varying policies, regulation

Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models

arXiv:2507.04023v3 Announce Type: replace Abstract: Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while gene

Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision

arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite

Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis

arXiv:2604.20928v1 Announce Type: cross Abstract: Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault

Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning

arXiv:2601.13690v2 Announce Type: replace Abstract: Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face notable challenges, including high ma

Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces

arXiv:2604.20993v1 Announce Type: new Abstract: Spreading of liquid droplets on solid substrates constitutes a classic multiphysics problem with widespread applications ranging from inkjet printing, s

Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms

arXiv:2604.21473v1 Announce Type: cross Abstract: In the treatment of complex diseases, treatment regimens using a single drug often yield limited efficacy and can lead to drug resistance. In contrast

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization

arXiv:2411.00171v2 Announce Type: replace Abstract: To avoid myopic behavior, multi-step lookahead Bayesian optimization (BO) algorithms consider the sequential nature of BO and have demonstrated prom

Efficient Multi-Source Knowledge Transfer by Model Merging

arXiv:2508.19353v2 Announce Type: replace-cross Abstract: While transfer learning is an effective strategy, it often overlooks the opportunity to leverage knowledge from numerous available models onli

Empirical Comparison of Agent Communication Protocols for Task Orchestration

arXiv:2603.22823v3 Announce Type: replace Abstract: Context. The problem of comparative evaluation of communication protocols for task orchestration by large language model (LLM) agents is considered.

EngramaBench: Evaluating Long-Term Conversational Memory with Structured Graph Retrieval

arXiv:2604.21229v1 Announce Type: cross Abstract: Large language model assistants are increasingly expected to retain and reason over information accumulated across many sessions. We introduce Engrama

Enhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification

arXiv:2604.21137v1 Announce Type: cross Abstract: Analyzing the reasoning patterns of students in science classrooms is critical for understanding knowledge construction mechanism and improving instru

Evaluating AI Meeting Summaries with a Reusable Cross-Domain Pipeline

arXiv:2604.21345v1 Announce Type: new Abstract: We present a reusable evaluation pipeline for generative AI applications, instantiated for AI meeting summaries and released with a public artifact pack

EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents

arXiv:2604.21890v1 Announce Type: new Abstract: Event extraction identifies the central aspects of events from text. It supports event understanding and analysis, which is crucial for tasks such as in

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels

arXiv:2604.20913v1 Announce Type: new Abstract: Large language models are increasingly deployed on CPU-only platforms where memory bandwidth is the primary bottleneck for autoregressive generation. We

Fake or Real, Can Robots Tell? Evaluating VLM Robustness to Domain Shift in Single-View Robotic Scene Understanding

arXiv:2506.19579v3 Announce Type: replace-cross Abstract: Robotic scene understanding increasingly relies on Vision-Language Models (VLMs) to generate natural language descriptions of the environment.

Federated Co-tuning Framework for Large and Small Language Models

arXiv:2411.11707v3 Announce Type: replace-cross Abstract: By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the c

Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge

arXiv:2510.04772v2 Announce Type: replace-cross Abstract: Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making F

Fine-Tuning Regimes Define Distinct Continual Learning Problems

arXiv:2604.21927v1 Announce Type: new Abstract: Continual learning (CL) studies how models acquire tasks sequentially while retaining previously learned knowledge. Despite substantial progress in benc

Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis

arXiv:2510.04823v2 Announce Type: replace Abstract: Generating synthetic CT (sCT) from MRI or CBCT plays a crucial role in enabling MRI-only and CBCT-based adaptive radiotherapy, improving treatment p

From Codebooks to VLMs: Evaluating Automated Visual Discourse Analysis for Climate Change on Social Media

arXiv:2604.21786v1 Announce Type: new Abstract: Social media platforms have become primary arenas for climate communication, generating millions of images and posts that - if systematically analysed -

From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation

arXiv:2604.21910v1 Announce Type: new Abstract: Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it.

GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure

arXiv:2512.22274v2 Announce Type: replace Abstract: We introduce GeCo, a geometry-grounded metric for jointly detecting geometric deformation and occlusion-inconsistency artifacts in static scenes. By

Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning

arXiv:2604.21495v1 Announce Type: cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift. Models trained with super

Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning

arXiv:2509.21976v3 Announce Type: replace-cross Abstract: Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationshi

Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation

arXiv:2604.21638v1 Announce Type: new Abstract: Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model devel

Geometric Monomial (GEM): a family of rational 2N-differentiable activation functions

arXiv:2604.21677v1 Announce Type: cross Abstract: The choice of activation function plays a crucial role in the optimization and performance of deep neural networks. While the Rectified Linear Unit (R

GeoMind: An Agentic Workflow for Lithology Classification with Reasoned Tool Invocation

arXiv:2604.21501v1 Announce Type: new Abstract: Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequ

GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR

arXiv:2601.09361v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models. Unlike supervised fine-tun

GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark

arXiv:2601.13711v2 Announce Type: replace Abstract: Authorship verification (AV) is the task of determining whether two texts were written by the same author and has been studied extensively, predomin

GiVA: Gradient-Informed Bases for Vector-Based Adaptation

arXiv:2604.21901v1 Announce Type: cross Abstract: As model sizes continue to grow, parameter-efficient fine-tuning has emerged as a powerful alternative to full fine-tuning. While LoRA is widely adopt

Grounding Machine Creativity in Game Design Knowledge Representations: Empirical Probing of LLM-Based Executable Synthesis of Goal Playable Patterns under Structural Constraints

arXiv:2603.07101v3 Announce Type: replace Abstract: Creatively translating complex gameplay ideas into executable artifacts (e.g., games as Unity projects and code) remains a central challenge in comp

Grounding Video Reasoning in Physical Signals

arXiv:2604.21873v1 Announce Type: new Abstract: Physical video understanding requires more than naming an event correctly. A model can answer a question about pouring, sliding, or collision from textu

HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks

arXiv:2604.14709v2 Announce Type: replace Abstract: Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL mo

HyperAdapt: Simple High-Rank Adaptation

arXiv:2509.18629v3 Announce Type: replace-cross Abstract: Foundation models excel across diverse tasks, but adapting them to specialized applications often requires fine-tuning, an approach that is me

HyperFM: An Efficient Hyperspectral Foundation Model with Spectral Grouping

arXiv:2604.21127v1 Announce Type: new Abstract: The NASA PACE mission provides unprecedented hyperspectral observations of ocean color, aerosols, and clouds, offering new insights into how these compo

Hyperloop Transformers

arXiv:2604.21254v1 Announce Type: cross Abstract: LLM architecture research generally aims to maximize model quality subject to fixed compute/latency budgets. However, many applications of interest su

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming

arXiv:2505.05261v3 Announce Type: replace-cross Abstract: Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability remains a cha

Ideological Bias in LLMs' Economic Causal Reasoning

arXiv:2604.21334v1 Announce Type: new Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in polic

ILDR: Geometric Early Detection of Grokking

arXiv:2604.20923v1 Announce Type: new Abstract: Grokking describes a delayed generalization phenomenon in which a neural network achieves perfect training accuracy long before validation accuracy impr

Intent Laundering: AI Safety Datasets Are Not What They Seem

arXiv:2602.16729v3 Announce Type: replace-cross Abstract: We systematically evaluate the quality of widely used adversarial safety datasets from two perspectives: in isolation and in practice. In isol

Interpretable facial dynamics as behavioral and perceptual traces of deepfakes

arXiv:2604.21760v1 Announce Type: new Abstract: Deepfake detection research has largely converged on deep learning approaches that, despite strong benchmark performance, offer limited insight into wha

IRIS: Interpolative Renyi Iterative Self-play for Large Language Model Fine-Tuning

arXiv:2604.20933v1 Announce Type: cross Abstract: Self-play fine-tuning enables large language models to improve beyond supervised fine-tuning without additional human annotations by contrasting annot

It's High Time: A Survey of Temporal Question Answering

arXiv:2505.20243v4 Announce Type: replace Abstract: Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Tempo