model releases

All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

arXiv:2604.24401v1 Announce Type: cross Abstract: Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory percep

Model Releases

AmaraSpatial-10K: A Spatially and Semantically Aligned 3D Dataset for Spatial Computing and Embodied AI

DGX agent

arXiv:2604.23018v1 Announce Type: cross Abstract: Web-scale 3D asset collections are abundant, but rarely deployment-ready. Assets ship with arbitrary metric scale, incorrect pivots and forward axes,

model-releasesarxiv-cs-ai

28 Apr 2026

An Aircraft Upset Recovery System with Reinforcement Learning

arXiv:2604.24355v1 Announce Type: new Abstract: This article explores the progress made in the creation of a pilot activated recovery system (PARS) for advanced jet trainers that utilizes artificial i

An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations

arXiv:2604.23290v1 Announce Type: cross Abstract: Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human anno

An easy way to get a team engaged with AI is just to build the thing you are talking about in the meeting during the meeting using Codex or …

model-releasesethan-mollick--x

An easy way to get a team engaged with AI is just to build the thing you are talking about in the meeting during the meeting using Codex or Claude Code. At worst, it fails in ways that can be construc

An Efficient Beam Search Algorithm for Active Perception in Mobile Robotics

model-releasesarxiv-cs-ro

arXiv:2604.23327v1 Announce Type: new Abstract: Active perception is a fundamental problem in autonomous robotics in which the robot must decide where to move and what to sense in order to obtain the

An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code

arXiv:2604.23361v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysi

AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval

arXiv:2604.23195v1 Announce Type: cross Abstract: Analog circuit design relies heavily on reusing existing intellectual property (IP), yet searching across heterogeneous representations such as SPICE

Anthropic partners with Blender, Autodesk, Adobe, Ableton, and others to launch connectors that integrate Claude directly into professional creative workflows (Anthropic)

Anthropic: Anthropic partners with Blender, Autodesk, Adobe, Ableton, and others to launch connectors that integrate Claude directly into professional creative workflows — Creative professionals look

model-releasestechmeme

Applications of the Transformer Architecture in AI-Assisted English Reading Comprehension

arXiv:2604.23615v1 Announce Type: cross Abstract: This paper studies interpretable and fair artificial intelligence architectures for understanding English reading. Introduced transformer-based models

ArguAgent: AI-Supported Real-Time Grouping for Productive Argumentation in STEM Classrooms

arXiv:2604.23449v1 Announce Type: new Abstract: Argumentation is a core practice in STEM education, but its productivity depends on who participates and how they interact. Higher-achieving students of

Artificial General Intelligence Forecasting and Scenario Analysis: State of the Field, Methodological Gaps, and Strategic Implications

arXiv:2604.22766v1 Announce Type: cross Abstract: In this report, we review the current state of methodologies to forecast the arrival of artificial general intelligence, assess their reliability, and

AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation

arXiv:2604.24086v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have been demonstrated possessing strong zero-shot generalization for robot control, their massive parameter

ATTN-FIQA: Interpretable Attention-based Face Image Quality Assessment with Vision Transformers

arXiv:2604.22841v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) aims to assess the recognition utility of face samples and is essential for reliable face recognition (FR) systems.

Audio2Tool: Bridging Spoken Language Understanding and Function Calling

arXiv:2604.22821v1 Announce Type: cross Abstract: Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmark

AusSmoke meets MultiNatSmoke: a fully-labelled diverse smoke segmentation dataset

arXiv:2604.23542v1 Announce Type: new Abstract: Wildfires are an escalating global concern due to the devastating impacts on the environment, economy, and human health, with notable incidents such as

AutoCompress: Critical Layer Isolation for Efficient Transformer Compression

arXiv:2604.22786v1 Announce Type: new Abstract: We present AutoCompress, a transformer compression method motivated by an empirical finding: in small transformers, Layer 0 carries disproportionately h

AutoGUI-v2: A Comprehensive Multi-Modal GUI Functionality Understanding Benchmark

arXiv:2604.24441v1 Announce Type: new Abstract: Autonomous agents capable of navigating Graphical User Interfaces (GUIs) hold the potential to revolutionize digital productivity. However, achieving tr

AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs

arXiv:2604.22937v1 Announce Type: new Abstract: Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current v

AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models

arXiv:2604.22871v1 Announce Type: cross Abstract: Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack

Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark

arXiv:2604.22869v1 Announce Type: cross Abstract: In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is la

AWS launches a desktop app for its Amazon Quick AI assistant, letting users connect their tools and local files to build custom apps, live dashboards, and more (Jigar Thakkar/About Amazon)

Jigar Thakkar / About Amazon: AWS launches a desktop app for its Amazon Quick AI assistant, letting users connect their tools and local files to build custom apps, live dashboards, and more — Amazon Q

model-releasestechmeme

AWS launches Amazon Connect Decisions and Amazon Connect Talent, which are AI agentic tools aimed at logistics workers and recruiters (Matt Day/Bloomberg)

Matt Day / Bloomberg: AWS launches Amazon Connect Decisions and Amazon Connect Talent, which are AI agentic tools aimed at logistics workers and recruiters — Amazon.com Inc.'s cloud unit, best known f

model-releasestechmeme

BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal

arXiv:2604.24428v1 Announce Type: cross Abstract: Electroencephalography (EEG) is highly susceptible to artifact contamination, such as electrooculographic (EOG) and electromyographic (EMG) interferen

Benchmarking and Mitigating Sycophancy in Medical Vision Language Models

arXiv:2509.21979v4 Announce Type: replace-cross Abstract: Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite thi

Benchmarking Emergent Coordination in Large-Scale LLM Populations: An Evaluation Framework on the MoltBook Archive

arXiv:2603.03555v2 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. Howeve

Benchmarking Pathology Foundation Models for Breast Cancer Survival Prediction

arXiv:2604.24679v1 Announce Type: new Abstract: Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across

Benchmarking Testing in Automated Theorem Proving

arXiv:2604.23698v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. E

Beyond coauthorship: semantic structure and phantom collaborators in transportation research, 1967--2025

arXiv:2604.23699v1 Announce Type: cross Abstract: We present a semantic-structural atlas of transportation research built from 120{,}323 papers across 34 peer-reviewed journals published between 1967

Beyond Context: Large Language Models' Failure to Grasp Users' Intent

arXiv:2512.21110v3 Announce Type: replace Abstract: Current Large Language Models (LLMs) safety approaches focus on explicitly harmful content while overlooking a critical vulnerability: the inability

Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition

arXiv:2604.23413v1 Announce Type: new Abstract: Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external ser

Beyond Single-Agent Alignment: Preventing Context-Fragmented Violations in Multi-Agent Systems

arXiv:2604.22879v1 Announce Type: cross Abstract: We identify and formalize a novel security risk: Context-Fragmented Violations (CFVs) - a class of policy breaches where individual agent actions appe

Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols

arXiv:2604.24512v1 Announce Type: new Abstract: As LLM agents transition to autonomous digital coworkers, maintaining deterministic goal-directedness in non-linear multi-turn conversations emerged as

BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration

arXiv:2509.06904v3 Announce Type: replace Abstract: We introduce the BIR-Adapter, a parameter-efficient diffusion adapter for blind image restoration. Diffusion-based restoration methods have demonstr

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment

arXiv:2604.24273v1 Announce Type: new Abstract: The deployment of intelligent reinforcement learning (RL) agents on resource-constrained edge devices remains a fundamental challenge due to the substan

Boosting MLLM Spatial Reasoning with Geometrically Referenced 3D Scene Representations

arXiv:2603.08592v2 Announce Type: replace Abstract: While Multimodal Large Language Models (MLLMs) have achieved remarkable success in 2D visual understanding, their ability to reason about 3D space r

Both are great models and neither wins everywhere. I use both Opus and 5.5 depending on the task. LangSmith Fleet lets you choose the model …

model-releasesharrison-chase--x

Both are great models and neither wins everywhere. I use both Opus and 5.5 depending on the task. LangSmith Fleet lets you choose the model for each agent, so you can match it to the work https://www.

Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement

arXiv:2604.22886v1 Announce Type: new Abstract: Thermal infrared image enhancement aims to restore high-quality images from complex compound degradations. Existing all-in-one approaches typically empl

Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search

arXiv:2604.23282v1 Announce Type: new Abstract: Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries. Although recent pose-aw

Builders

model-releasesben-s-bites

Builders likely refers to a discussion of AI tools, platforms, or frameworks that enable developers and creators to build applications or solutions using AI technologies. The article probably covers e

C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs

arXiv:2604.23061v1 Announce Type: cross Abstract: Large language models (LLMs) show promise for molecular optimization, but aligning them with selective and competing drug-design constraints remains c

Can Aha Moments Be Fake? Identifying True and Decorative Thinking Steps in Chain-of-Thought

arXiv:2510.24941v3 Announce Type: replace Abstract: Large language models can generate long chain-of-thought (CoT) reasoning, but it remains unclear whether the verbalized steps reflect the models' in

Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft

arXiv:2604.24697v1 Announce Type: new Abstract: Discovering causal regularities and applying them to build functional systems--the discovery-to-application loop--is a hallmark of general intelligence,

Can Large Language Models Really Recognize Your Name?

arXiv:2505.14549v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly being used in privacy pipelines to detect and remedy sensitive data leakage. These solutions oft

Can LLMs Act as Historians? Evaluating Historical Research Capabilities of LLMs via the Chinese Imperial Examination

arXiv:2604.24690v1 Announce Type: new Abstract: While Large Language Models (LLMs) have increasingly assisted in historical tasks such as text processing, their capacity for professional-level histori

Can Multimodal Large Language Models Truly Understand Small Objects?

arXiv:2604.22884v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown promising potential in diverse understanding tasks, e.g., image and video analysis, math and physi

Cataract-LMM Large-Scale Multi-Source Multi-Task Benchmark for Deep Learning in Surgical Video Analysis

arXiv:2510.16371v2 Announce Type: replace-cross Abstract: The development of computer-assisted surgery systems relies on large-scale, annotated datasets. Existing cataract surgery resources lack the d

Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework

arXiv:2604.23633v1 Announce Type: new Abstract: Constraint-based causal discovery is brittle in finite-sample regimes because erroneous conditional-independence (CI) decisions can cascade into substan