Search: “model-releases”

Whose Name Comes Up? II: Benchmarking and Intervention-Based Auditing of LLM-Based Scholar Recommendation

arXiv:2602.08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation. Existing audits typically evaluate such recommendations in isola

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout

DGX agent

arXiv:2606.03038v1 Announce Type: new Abstract: Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate dev

model-releasesarxiv-cs-lg

3 Jun 2026

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

arXiv:2503.07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content. However, existing research and evalua

Wordle 1,809 5/6 🟨⬛🟨⬛⬛ ⬛🟨⬛⬛⬛ ⬛🟩🟨🟨⬛ 🟩🟩🟩🟩⬛ 🟩🟩🟩🟩🟩

model-releasesanthropic--x

This post documents a completed Wordle puzzle (puzzle #1,809) solved in five attempts, showing the progression of letter guesses through color-coded feedback (yellow for correct letters in wrong posit

WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents

arXiv:2606.02908v1 Announce Type: cross Abstract: Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute val

3D Segment Anything Model with Visual Mamba for Diagnosing Placenta Accreta Spectrum

arXiv:2606.00489v1 Announce Type: new Abstract: Placenta Accreta Spectrum (PAS) is a rare but highly dangerous obstetric disease. Early and accurate PAS diagnosis is critical for maternal health. Trad

3DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Code

arXiv:2606.01057v1 Announce Type: cross Abstract: Procedural 3D modeling through code is emerging as a versatile paradigm, offering deterministic, engine-ready, and precisely editable assets that neur

3rd Place at CVPR 2026 CASTLE Challenge: Agentic Multi-View Long-Context Video Understanding via Hierarchical Knowledge Graph Retrieval

arXiv:2606.01933v1 Announce Type: new Abstract: This paper presents our winning methodology for the CASTLE 2026 Challenge at the CVPR 2026 EgoVis Workshop, where our team secured third place globally.

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

arXiv:2606.01973v1 Announce Type: cross Abstract: Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes. While recent methods have m

A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process

arXiv:2606.00821v1 Announce Type: new Abstract: This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine lea

A Finite-Calibration Regime Map for LLM Judge Panels

arXiv:2606.01034v1 Announce Type: new Abstract: We study when LLM judge panels should be calibrated with low-dimensional stackers versus joint output tables under finite human-label budgets. Low-dimen

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL

arXiv:2606.02398v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation,

A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning

model-releasesarxiv-cs-ro

arXiv:2606.00922v1 Announce Type: cross Abstract: In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatme

A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces

arXiv:2606.00106v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials

A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models

arXiv:2606.00027v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis

arXiv:2507.12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient asses

A Per-Component Diagnostic Protocol for Neural HJB-PIDE Solvers under Control-Dependent Levy Jumps

arXiv:2606.01122v1 Announce Type: new Abstract: We propose a five-step diagnostic protocol for residual-trained neural HJB-PIDE solvers with control-dependent Levy jumps, targeting a general failure m

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606.01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task. Recent multimodal vision-language models have produced systems that admit textual

A Systematic Benchmark of Intraoperative Ultrasound-to-MR Synthesis for Brain Tumour Surgery

arXiv:2606.00630v1 Announce Type: new Abstract: Intraoperative ultrasound (ioUS) is a versatile, cost-effective modality in brain tumour surgery, but its interpretation is difficult: acquisition plane

AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research

arXiv:2507.08038v3 Announce Type: replace-cross Abstract: Language model agents are increasingly used to automate scientific research, yet evaluating their scientific contributions remains a challenge

Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents

arXiv:2606.01886v1 Announce Type: new Abstract: Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfo

Accelerating data lakes: Optimizing Apache Iceberg and Spark with gcs-analytics-core

model-releasesgoogle-cloud-ai

Many data engineers spend significant time managing compatibility and getting best performance across multiple analytics engines. To help solve this pain point, we are excited to announce gcs-analytic

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

arXiv:2606.01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informe

Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks

arXiv:2606.00920v1 Announce Type: cross Abstract: Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points -- and the gap is largest precisely for mid-performing systems. We

ACON: Optimizing Context Compression for Long-horizon LLM Agents

arXiv:2510.00615v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise re

Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models

arXiv:2606.02459v1 Announce Type: new Abstract: Enabling Vision-Language Models (VLMs) to perform spatial reasoning remains challenging. Existing approaches treat VLMs as passive observers, which is d

ADRA-Bank: A Modular Benchmark for Academic Deep Research Agents

arXiv:2512.00986v3 Announce Type: replace Abstract: A surge in academic publications calls for automated deep research (DR) systems, but accurately evaluating them is still an open problem. First, exi

Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation

arXiv:2512.16310v3 Announce Type: replace-cross Abstract: LLM-based agents increasingly use multiple external tools to complete complex tasks. We study Tools Orchestration Privacy Risk (TOP-R): an age

AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents

arXiv:2606.02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future e

AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science

arXiv:2603.19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large

AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design

arXiv:2606.02386v1 Announce Type: new Abstract: Protein language models (PLMs) are passive oracles: they generate sequences in a single forward pass with no mechanism to consult external biophysical f

AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents

arXiv:2603.14465v2 Announce Type: replace Abstract: While Large Language Models (LLMs) have evolved into tool-using agents, they remain brittle in long-horizon interactions. Unlike mathematical reason

AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations

arXiv:2606.02240v1 Announce Type: cross Abstract: Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, S

[AINews] NVIDIA Cosmos 3, Nemotron 3 Ultra, and RTX Spark

model-releaseslatent-space

NVIDIA announced three new offerings: Cosmos 3, an advanced video generation model; Nemotron 3 Ultra, an upgraded language model; and RTX Spark, likely a tool or framework for developers. These releas

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606.00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temp

Announcing Spanner Graph algorithms: Google-grade intelligence for connected data

model-releasesgoogle-cloud-ai

At Google Cloud Next, we announced the preview of graph algorithms with Spanner Graph, bringing Google Research’s state-of-the-art graph mining capabilities natively to your database. These graph inte

Anthropic expands Project Glasswing cybersecurity program to 150 more organizations

model-releasessiliconangle

Anthropic PBC is expanding a program that enables organizations to test their cybersecurity defenses using its Claude Mythos Preview model. The initiative, which is known as Project Glasswing, launche

APE: Agentic Prompt Enhancer for Image Generation and Editing

arXiv:2606.00204v1 Announce Type: new Abstract: Natural language has become a powerful interface for image generation and editing, yet text-guided visual systems remain highly sensitive to prompt form

APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

arXiv:2602.16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The

Approximating f-Divergences with Rank Statistics

arXiv:2601.22784v2 Announce Type: replace-cross Abstract: We introduce a rank-statistic approximation of f-divergences that avoids explicit density-ratio estimation by working directly with the distri

ARCA: Adapter-Residual Credit Assignment When Token Signals Degenerate

arXiv:2606.00257v1 Announce Type: cross Abstract: Token-level credit assignment for language-model reinforcement learning is usually formulated as if the policy were fully trainable, while practical L

Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework

arXiv:2602.18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on s

ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training

arXiv:2606.00602v1 Announce Type: new Abstract: Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak,

ASE-26: a curriculum for agentic software engineering as a discipline

arXiv:2606.01152v1 Announce Type: cross Abstract: The work of a professional software engineer has begun to consist, increasingly, of directing agents rather than writing code, and the empirical evide

Assessment of Generative Named Entity Recognition in the Era of Large Language Models

arXiv:2601.17898v2 Announce Type: replace Abstract: Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs). W

ATLAS: Agentic Test-time Learning-to-Allocate Scaling

arXiv:2606.01667v1 Announce Type: new Abstract: Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed samp

Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

arXiv:2606.02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural

Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation

arXiv:2606.02528v1 Announce Type: cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. W