Search: “model-releases”

LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

arXiv:2604.14922v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent ad

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MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events

DGX agent

arXiv:2604.15203v1 Announce Type: new Abstract: Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification

model-releasesarxiv-cs-cl

17 Apr 2026

Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data

arXiv:2604.14229v1 Announce Type: cross Abstract: Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models naturally operate in complex Hilbert spa

MARCA: A Checklist-Based Benchmark for Multilingual Web Search

arXiv:2604.14448v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as sources of information, yet their reliability depends on the ability to search the web, select rel

MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents

arXiv:2509.06477v2 Announce Type: replace Abstract: Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, fostering a promising hybrid paradigm for ML

Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips

arXiv:2502.07408v2 Announce Type: replace-cross Abstract: Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping only a handful of parameter bits. We introduce Deep Neural Lesion (D

Measuring multi-calibration

arXiv:2506.11251v2 Announce Type: replace-cross Abstract: A suitable scalar metric can help measure multi-calibration, defined as follows. When the expected values of observed responses are equal to c

MEBench: A Novel Benchmark for Understanding Mutual Exclusivity Bias in Vision-Language Models

arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor

Mechanistic Decoding of Cognitive Constructs in LLMs

arXiv:2604.14593v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate increasingly sophisticated affective capabilities, the internal mechanisms by which they process complex

MemGround: Long-Term Memory Evaluation Kit for Large Language Models in Gamified Scenarios

arXiv:2604.14158v1 Announce Type: new Abstract: Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the

MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry

arXiv:2604.14866v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains

Mitigating LLM biases toward spurious social contexts using direct preference optimization

arXiv:2604.02585v2 Announce Type: replace-cross Abstract: LLMs are increasingly used for high-stakes decision-making, yet their sensitivity to spurious contextual information can introduce harmful bia

MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining

arXiv:2604.14198v1 Announce Type: cross Abstract: Domain reweighting can improve sample efficiency and downstream generalization, but data-mixture optimization for multimodal midtraining remains large

MM-WebAgent: A Hierarchical Multimodal Web Agent for Webpage Generation

arXiv:2604.15309v1 Announce Type: cross Abstract: The rapid progress of Artificial Intelligence Generated Content (AIGC) tools enables images, videos, and visualizations to be created on demand for we

Modular Continual Learning via Zero-Leakage Reconstruction Routing and Autonomous Task Discovery

arXiv:2604.14375v1 Announce Type: new Abstract: Catastrophic forgetting remains a primary hurdle in sequential task learning for artificial neural networks. We propose a silicon-native modular archite

ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation

arXiv:2604.07021v2 Announce Type: replace Abstract: Weakly supervised semantic segmentation aims to achieve pixel-level predictions using image-level labels. Existing methods typically entangle semant

Multigrain-aware Semantic Prototype Scanning and Tri-Token Prompt Learning Embraced High-Order RWKV for Pan-Sharpening

arXiv:2604.14622v1 Announce Type: new Abstract: In this work, we propose a Multigrain-aware Semantic Prototype Scanning paradigm for pan-sharpening, built upon a high-order RWKV architecture and a tri

Neuro-Oracle: A Trajectory-Aware Agentic RAG Framework for Interpretable Epilepsy Surgical Prognosis

arXiv:2604.14216v1 Announce Type: cross Abstract: Predicting post-surgical seizure outcomes in pharmacoresistant epilepsy is a clinical challenge. Conventional deep-learning approaches operate on stat

OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism

arXiv:2604.14762v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) challenges methods to identify known and novel classes using partially labeled data, mirroring human category learn

Open-Set Vein Biometric Recognition with Deep Metric Learning

arXiv:2604.14874v1 Announce Type: new Abstract: Most state-of-the-art vein recognition methods rely on closed-set classification, which inherently limits their scalability and prevents the adaptive en

OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis

arXiv:2604.15093v1 Announce Type: cross Abstract: Mobile agents powered by vision-language models have demonstrated impressive capabilities in automating mobile tasks, with recent leading models achie

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games

arXiv:2506.03610v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game b

Pangu-ACE: Adaptive Cascaded Experts for Educational Response Generation on EduBench

arXiv:2604.14828v1 Announce Type: new Abstract: Educational assistants should spend more computation only when the task needs it. This paper rewrites our earlier draft around the system that was actua

Parameter estimation for land-surface models using Neural Physics

arXiv:2505.02979v3 Announce Type: replace-cross Abstract: We propose a novel inverse-modelling approach which estimates the parameters of a simple land-surface model (LSM) by assimilating data into a

PeerPrism: Peer Evaluation Expertise vs Review-writing AI

arXiv:2604.14513v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, expansion, and refinement. However, ex

Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification

arXiv:2604.14643v1 Announce Type: new Abstract: Adversarial attacks pose a severe threat to the reliability of deep learning models in remote sensing (RS) image classification. Most existing methods r

PolyBench: Benchmarking LLM Forecasting and Trading Capabilities on Live Prediction Market Data

arXiv:2604.14199v1 Announce Type: cross Abstract: Predicting real-world events from live market signals demands systems that fuse qualitative news with quantitative order-book dynamics under strict te

POP: Prefill-Only Pruning for Efficient Large Model Inference

arXiv:2602.03295v2 Announce Type: replace Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) have demonstrated remarkable capabilities. However, their deployment is hindered by s

PortraitCraft: A Benchmark for Portrait Composition Understanding and Generation

arXiv:2604.03611v2 Announce Type: replace Abstract: Portrait composition plays a central role in portrait aesthetics and visual communication, yet existing datasets and benchmarks mainly focus on coar

Prism: Symbolic Superoptimization of Tensor Programs

arXiv:2604.15272v1 Announce Type: cross Abstract: This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that

Prompt-Guided Image Editing with Masked Logit Nudging in Visual Autoregressive Models

arXiv:2604.14591v1 Announce Type: new Abstract: We address the problem of prompt-guided image editing in visual autoregressive models. Given a source image and a target text prompt, we aim to modify t

Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems

arXiv:2604.14585v1 Announce Type: cross Abstract: Prompt optimization in compound AI systems is statistically indistinguishable from a coin flip: across 72 optimization runs on Claude Haiku (6 methods

ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking

arXiv:2506.03487v3 Announce Type: replace-cross Abstract: Reranking is fundamental to information retrieval and retrieval-augmented generation, with recent Large Language Models (LLMs) significantly a

Purging the Gray Zone: Latent-Geometric Denoising for Precise Knowledge Boundary Awareness

arXiv:2604.14324v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit hallucinations due to their inability to accurately perceive their own knowledge boundaries. Existing abstent

QuantCode-Bench: A Benchmark for Evaluating the Ability of Large Language Models to Generate Executable Algorithmic Trading Strategies

arXiv:2604.15151v1 Announce Type: new Abstract: Large language models have demonstrated strong performance on general-purpose programming tasks, yet their ability to generate executable algorithmic tr

Query pipeline optimization for cancer patient question answering systems

arXiv:2412.14751v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external

Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards

arXiv:2604.14876v1 Announce Type: cross Abstract: We study the tail behavior of regret in stochastic multi-armed bandits for algorithms that are asymptotically optimal in expectation. While minimizing

RELOAD: A Robust and Efficient Learned Query Optimizer for Database Systems

arXiv:2604.14725v1 Announce Type: cross Abstract: Recent advances in query optimization have shifted from traditional rule-based and cost-based techniques towards machine learning-driven approaches. A

Rethinking Patient Education as Multi-turn Multi-modal Interaction

arXiv:2604.14656v1 Announce Type: cross Abstract: Most medical multimodal benchmarks focus on static tasks such as image question answering, report generation, and plain-language rewriting. Patient ed

Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring

arXiv:2604.14616v1 Announce Type: new Abstract: Clinical value set authoring -- the task of identifying all codes in a standardized vocabulary that define a clinical concept -- is a recurring bottlene

ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents

arXiv:2604.14261v1 Announce Type: new Abstract: The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-base

Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification

arXiv:2604.05302v2 Announce Type: replace Abstract: Text simplification supports second language (L2) learning by providing comprehensible input, consistent with the Input Hypothesis. However, constru

SafeHarness: Lifecycle-Integrated Security Architecture for LLM-based Agent Deployment

arXiv:2604.13630v1 Announce Type: cross Abstract: The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use, context

SAGE Celer 2.6 Technical Card

arXiv:2604.14168v1 Announce Type: new Abstract: We introduce SAGE Celer 2.6, the latest in our line of general-purpose Celer models from SAGEA. Celer 2.6 is available in 5B, 10B, and 27B parameter siz

SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization

arXiv:2604.07663v2 Announce Type: replace Abstract: The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's

SAQ: Stabilizer-Aware Quantum Error Correction Decoder

arXiv:2512.08914v2 Announce Type: replace-cross Abstract: Quantum Error Correction (QEC) decoding faces a fundamental accuracy-efficiency tradeoff. Classical methods like Minimum Weight Perfect Matchi

Schema Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding

arXiv:2604.14862v1 Announce Type: new Abstract: Constrained decoding has been widely adopted for structured generation with large language models (LLMs), ensuring that outputs satisfy predefined forma

Secure and Privacy-Preserving Vertical Federated Learning

arXiv:2604.13474v1 Announce Type: cross Abstract: We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering bot