Search: “model-releases”

Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation

arXiv:2508.20909v2 Announce Type: replace Abstract: Foundation models pre-trained on large-scale natural image datasets offer a powerful paradigm for medical image segmentation. However, effectively t

Model Releases

Disagreement-Regularized Importance Sampling for Adversarial Label Corruption

DGX agent

arXiv:2605.07551v1 Announce Type: new Abstract: Standard Importance Sampling (IS) collapses under label corruption because high-norm examples, prioritized for variance reduction, are often adversarial

model-releasesarxiv-cs-lg

11 May 2026

Discord launches Nitro Rewards, giving Nitro subscribers access to offers from gaming services like Xbox Game Pass and hardware like Logitech G at no extra cost (Amanda Silberling/TechCrunch)

Amanda Silberling / TechCrunch: Discord launches Nitro Rewards, giving Nitro subscribers access to offers from gaming services like Xbox Game Pass and hardware like Logitech G at no extra cost — Disco

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Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation

arXiv:2605.07323v1 Announce Type: new Abstract: Discovering governing differential equations from observational data is a fundamental challenge in scientific machine learning. Existing symbolic regres

Divide and Conquer: Object Co-occurrence Helps Mitigate Simplicity Bias in OOD Detection

arXiv:2605.07821v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangle

DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization

arXiv:2511.09117v4 Announce Type: replace Abstract: Kuzushiji, a pre-modern Japanese cursive script, can currently be read and understood by only a few thousand trained experts in Japan. With the rapi

Do Joint Audio-Video Generation Models Understand Physics?

arXiv:2605.07061v1 Announce Type: cross Abstract: Joint audio-video generation models are rapidly approaching professional production quality, raising a central question: do they understand audio-visu

Domain-level metacognitive monitoring in frontier LLMs: A 33-model atlas

arXiv:2605.06673v1 Announce Type: cross Abstract: Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, un

DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain

arXiv:2605.07699v1 Announce Type: cross Abstract: LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently a

DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization

arXiv:2512.14263v2 Announce Type: replace-cross Abstract: Preferential Bayesian Optimization (PBO) aims to find a decision-maker's most preferred solution in as few pairwise comparisons as possible. E

Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators

arXiv:2605.06997v1 Announce Type: new Abstract: Long chain-of-thought reasoning and agentic tool-calling produce traces spanning tens of thousands of tokens, yet Transformer KV caches grow linearly wi

Effective and Memory-Efficient Alternatives to ECC for Reliable Large-Scale DNNs

arXiv:2605.07417v1 Announce Type: cross Abstract: Modern Deep Learning (DL) workloads are increasingly deployed in safety-critical domains, such as automotive systems and hyperscale data centers, wher

Efficient Data Selection for Multimodal Models via Incremental Optimization Utility

arXiv:2605.07488v1 Announce Type: new Abstract: The scaling of Large Multimodal Models (LMMs) is constrained by the quality-quantity trade-off inherent in synthetic data. Previous approaches, such as

EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams

arXiv:2605.07299v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users

End-to-end PDDL Planning with Hardcoded and Dynamic Agents

arXiv:2512.09629v2 Announce Type: replace Abstract: We present an end-to-end framework for planning supported by verifiers. An orchestrator receives a human specification written in natural language a

EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation

arXiv:2605.07247v1 Announce Type: new Abstract: Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments

ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning

arXiv:2602.01003v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often

Evaluating Large Language Models in Scientific Discovery

arXiv:2512.15567v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and

Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs

arXiv:2605.06669v1 Announce Type: cross Abstract: Educational LLM tutors face a core AI alignment challenge: they must follow user intent while preserving pedagogical constraints and safety policies.

Ever wished your agent could read PDFs, images, and Office documents as easily as plain text? Or combine the safety of a secure sandbox with…

model-releasesjerry-liu--x

Ever wished your agent could read PDFs, images, and Office documents as easily as plain text? Or combine the safety of a secure sandbox with the full power of Bash access? We built exactly that. Meet

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics

arXiv:2512.12602v4 Announce Type: replace Abstract: In this paper, we introduce Exact Flow Linear Attention~(EFLA), an exact-flow formulation of delta-rule linear attention. We show that the delta-rul

Exact Is Easier: Credit Assignment for Cooperative LLM Agents

arXiv:2603.06859v2 Announce Type: replace-cross Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts t

Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints

arXiv:2605.07485v1 Announce Type: cross Abstract: Extrapolation to out-of-distribution conditions is a fundamental challenge for physics-constrained deep generative models. Existing methods apply phys

FactoryBench: Evaluating Industrial Machine Understanding

arXiv:2605.07675v1 Announce Type: new Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs

Falcon 9 is vertical at pad 40 in Florida ahead of tomorrow’s launch of Dragon’s 34th Commercial Resupply Services mission to the @Space_Sta…

model-releaseselon-musk--x

Falcon 9 is vertical at pad 40 in Florida ahead of tomorrow’s launch of Dragon’s 34th Commercial Resupply Services mission to the @Space_Station. Teams are keeping an eye on weather, which is currentl

FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution

arXiv:2605.07208v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to brainstorm and evaluate research ideas, yet assessing such judgments is fundamentally difficult be

FANoS-v2: Feedback-Controlled Momentum with Thermostat Damping for Lightweight Neural Optimization

arXiv:2601.00889v2 Announce Type: replace Abstract: FANOS{} is a PyTorch optimizer that augments RMS-preconditioned momentum with a scalar feedback controller over update energy. The public reference

Fast Byte Latent Transformer

arXiv:2605.08044v1 Announce Type: cross Abstract: Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limi

FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings

arXiv:2605.06982v1 Announce Type: new Abstract: Embedding models in natural language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provid

Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting

arXiv:2509.24789v4 Announce Type: replace Abstract: The evaluation of time series forecasting models is hindered by a lack of high-quality benchmarks, leading to overestimated assessments of progress.

Fine-tuning a vision-language model for fracture-surface morphology recognition

arXiv:2605.07145v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown strong potential for scientific image understanding, but general-purpose models often lack the domain-specifi

Fine-tuning on your proprietary data is the highest leverage thing you can do. Prompts get copied overnight. A model trained on your data, y…

model-releasesfireworks-ai--x

Fine-tuning on your proprietary data is the highest leverage thing you can do. Prompts get copied overnight. A model trained on your data, your evals, your edge cases is a strong moat. OpenAI is windi

FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting

model-releasesarxiv-cs-cl

arXiv:2603.19254v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in financial research workflows, where their role is evolving from single-model assistance fo

Flat Channels to Infinity in Neural Loss Landscapes

arXiv:2506.14951v4 Announce Type: replace-cross Abstract: The loss landscapes of neural networks contain minima and saddle points that may be connected in flat regions or appear in isolation. We ident

Frequency-Aware Model Parameter Explorer: A new attribution method for improving explainability

arXiv:2510.03245v2 Announce Type: replace-cross Abstract: State-of-the-art attribution methods rely on adversarial sample generation that applies an all-pass filter across the frequency spectrum, disc

From Clouds to Hallucinations: Atmospheric Retrieval Hijacking in Remote Sensing Vision-Language RAG

arXiv:2605.07273v1 Announce Type: cross Abstract: Multimodal RAG systems increasingly rely on vision-language retrievers to ground visual queries in external textual evidence. Existing adversarial stu

From Pixels to Prompts: Vision-Language Models

arXiv:2605.07544v1 Announce Type: new Abstract: When you read a paper about a new Vision-Language Model today, it can be easy to forget how strange this idea would have sounded not so long ago. Teachi

From Synthetic to Real: Toward Identity-Consistent Makeup Transfer with Synthetic and Real Data

arXiv:2605.07861v1 Announce Type: new Abstract: Makeup transfer aims to apply the makeup style of a reference portrait to a source portrait while preserving identity and background. Early methods form

GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges

arXiv:2605.07133v1 Announce Type: cross Abstract: Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform go

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

arXiv:2605.06734v1 Announce Type: cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (

GazeVLM: Active Vision via Internal Attention Control for Multimodal Reasoning

arXiv:2605.07817v1 Announce Type: cross Abstract: Human visual reasoning is governed by active vision, a process where metacognitive control drives top-down goal-directed attention, dynamically routin

GC-ART: Global Learnable Second-Order Rational Tone Curves for Illumination Robustness

arXiv:2605.07329v1 Announce Type: new Abstract: We introduce GC-ART (Global Curve Adaptive Rational Tone-mapping), a lightweight differentiable pre-processing module for robust image classification. G

Generalized Euler Logarithm and its Applications in Machine Learning: Natural Gradient, Backpropagation, Generalized EG, Mirror Descent and OLPS

arXiv:2502.17500v3 Announce Type: replace-cross Abstract: This paper investigates in depth the fundamental properties of the two-parameter generalized Euler logarithm and its inverse, the associated d

GLiGuard: Schema-Conditioned Classification for LLM Safeguard

model-releasesarxiv-cs-cl

arXiv:2605.07982v1 Announce Type: new Abstract: Ensuring safe, policy-compliant outputs from large language models requires real-time content moderation that can scale across multiple safety dimension

Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction

arXiv:2605.08022v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) have been proposed as biologically plausible and energy-efficient alternatives to conventional Artificial Neural Networ

Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning

arXiv:2410.06347v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While O

Google says criminals used AI to build a working zero-day exploit for the first time

model-releasessiliconangle

Criminal hackers have used artificial intelligence to develop a working zero-day exploit, the first confirmed case of its kind, according to a report released today by Google LLC’s Google Threat Intel

Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study

model-releasesarxiv-cs-cl

arXiv:2605.07366v1 Announce Type: new Abstract: Adaptive rank allocation for LoRA, allocating more parameters to important layers and fewer to unimportant ones, consistently improves efficiency under