model releases

NCL-UoR at SemEval-2026 Task 5: Embedding-Based Methods, Fine-Tuning, and LLMs for Word Sense Plausibility Rating

arXiv:2603.08256v2 Announce Type: replace Abstract: Word sense plausibility rating requires predicting the human-perceived plausibility of a given word sense on a 1-5 scale in the context of short nar

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Neural Neural Scaling Laws

DGX agent

arXiv:2601.19831v2 Announce Type: replace-cross Abstract: Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation lo

model-releasesarxiv-cs-cl

11 May 2026

Neural Operators as Efficient Function Interpolators

arXiv:2605.07792v1 Announce Type: cross Abstract: Neural operators (NOs) are designed to learn maps between infinite-dimensional function spaces. We propose a novel reframing of their use. By introduc

New TIL: I figured out how to use my LLM CLI tool in a shebang line, which means you can write executable scripts in English, or hook up mor…

model-releasessimon-willison--x

Simon Willison discovered how to use an LLM command-line interface tool in Unix shebang lines, enabling the creation of executable scripts written in English or natural language. This technique allows

NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting

arXiv:2605.07476v1 Announce Type: new Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variab

NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection

arXiv:2508.01248v4 Announce Type: replace Abstract: The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing

NSMQ Riddles: A Benchmark of Scientific and Mathematical Riddles for Quizzing Large Language Models

arXiv:2605.07051v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown good performance on various science educational benchmarks, demonstrating their potential for use in science and

OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning

arXiv:2605.06728v1 Announce Type: cross Abstract: Interpreting transcriptomic data is one of the most common analytical tasks in modern biology. Yet most current models either consume expression profi

On the Invariance and Generality of Neural Scaling Laws

arXiv:2605.07546v1 Announce Type: new Abstract: Neural scaling laws establish a predictable relationship between model performance and data or compute, offering crucial guidance for resource allocatio

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Lab…

model-releasesethan-mollick--x

One of the most important properties of LLMs that we take for granted is that newer, bigger models are just better at everything. The AI Labs are pouring effort into economically valuable fields like

OpenAI Campus Network: Student club interest form

The OpenAI Campus Network is a program that facilitates student engagement with OpenAI's technology and research on college campuses. This interest form allows students to express interest in starting

model-releasesopenai

OpenAI just released its answer to Claude Mythos

model-releasesthe-verge-ai

OpenAI is launching Daybreak, an AI initiative focused on detecting and patching vulnerabilities before attackers find them. Daybreak uses the Codex Security AI agent that launched in March to create

OpenAI launches Daybreak, a cybersecurity initiative integrating AI models and Codex Security to help organizations patch vulnerabilities (Alexey Shabanov/TestingCatalog AI News)

Alexey Shabanov / TestingCatalog AI News: OpenAI launches Daybreak, a cybersecurity initiative integrating AI models and Codex Security to help organizations patch vulnerabilities — OpenAI launches Da

model-releasestechmeme

OpenAI launches DeployCo to help businesses build around intelligence

OpenAI launched DeployCo, a new service designed to assist businesses in building and deploying applications leveraging OpenAI's AI models and intelligence capabilities. The offering appears to focus

model-releasesopenai

OpenAI launches professional services business with $4B investment

model-releasessiliconangle

OpenAI Group PBC today unveiled a new business unit, The OpenAI Deployment Company, that will help companies adopt its artificial intelligence models. The subsidiary is launching with 4 billion in fun

OpenAI launches the OpenAI Deployment Company with a $4B+ investment to help organizations build and deploy AI systems, and acquires AI consulting firm Tomoro (Reuters)

Reuters: OpenAI launches the OpenAI Deployment Company with a 4B+ investment to help organizations build and deploy AI systems, and acquires AI consulting firm Tomoro — OpenAI said on Monday it is set

model-releasestechmeme

Optimal Experiments for Partial Causal Effect Identification

arXiv:2605.06993v1 Announce Type: new Abstract: Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically cost

Optimizing Language Models for Crosslingual Knowledge Consistency

arXiv:2603.04678v2 Announce Type: replace-cross Abstract: Large language models are known to often exhibit inconsistent knowledge. This is particularly problematic in multilingual scenarios, where mod

OrScale: Orthogonalised Optimization with Layer-Wise Trust-Ratio Scaling

arXiv:2605.07815v1 Announce Type: cross Abstract: Muon improves neural-network training by orthogonalizing matrix-valued updates, but it leaves each layer's update magnitude controlled mostly by a glo

Outlier Smoothing with Closed-Form Rotations for W4A4 Large Language Model Quantization

arXiv:2511.22316v2 Announce Type: replace Abstract: Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gr

PAIR-Former: Budgeted Relational Multi-Instance Learning for Functional miRNA Target Prediction

arXiv:2602.00465v3 Announce Type: replace-cross Abstract: Functional miRNA--mRNA targeting is a large-bag prediction problem where each transcript yields a heavy-tailed pool of candidate target sites

PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation

model-releasesarxiv-cs-ro

arXiv:2605.07496v1 Announce Type: new Abstract: Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided b

PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning

arXiv:2605.07267v1 Announce Type: new Abstract: Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time. Existing

PerfCoder: Large Language Models for Interpretable Code Performance Optimization

arXiv:2512.14018v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress in automatic code generation, yet their ability to produce high-performance cod

PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN

model-releasesarxiv-cs-ro

arXiv:2605.07687v1 Announce Type: new Abstract: Physics-based digital twins aim to predict the dynamics of real-world objects under interaction, enabling real-to-sim-to-real applications in robotics.

PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models

arXiv:2605.07574v1 Announce Type: new Abstract: Mainstream vision-language models (VLMs) fundamentally struggle with severe optical ambiguities, such as reflections and transparent objects, due to the

Pretraining Induces a Reusable Spectral Basis for Downstream Task Adaptation

arXiv:2605.07302v1 Announce Type: new Abstract: Finetuning pretrained models occurs in a low-dimensional subspace of the full parameter space. Prior work has focused on characterizing this optimizatio

PRIMED: Adaptive Modality Suppression for Referring Audio-Visual Segmentation via Biased Competition

arXiv:2605.07154v1 Announce Type: new Abstract: Referring Audio-Visual Segmentation (Ref-AVS) seeks to localize and segment target objects in video frames based on visual, auditory, and textual referr

ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices

arXiv:2602.21858v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have made significant progress in mobile agent development, yet their capabilities are predominantly confin

ProcObject-10K: Benchmarking Object-Centric Procedural Understanding in Instructional Videos

arXiv:2512.03479v2 Announce Type: replace Abstract: Procedural activities are fundamentally driven by object state transitions, yet existing instructional video benchmarks remain action-centric and ca

Prompt Engineering Strategies for LLM-based Qualitative Coding of Psychological Safety in Software Engineering Communities: A Controlled Empirical Study

arXiv:2605.07422v1 Announce Type: cross Abstract: Qualitative analysis plays a pivotal role in understanding the human and social aspects of software engineering. However, it remains a demanding proce

PSK@EEUCA 2026: Fine-Tuning Large Language Models with Synthetic Data Augmentation for Multi-Class Toxicity Detection in Gaming Chat

arXiv:2605.07201v1 Announce Type: cross Abstract: This paper describes our system for the EEUCA 2026 Shared Task on Understanding Toxic Behavior in Gaming Communities. The task involves classifying Wo

Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation

arXiv:2605.07647v1 Announce Type: cross Abstract: Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. Th

Query-efficient model evaluation using cached responses

arXiv:2605.07096v1 Announce Type: cross Abstract: Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, gen

Quotient Semivalues for False-Name-Resistant Data Attribution

arXiv:2605.07663v1 Announce Type: cross Abstract: Data valuation methods allocate payments and audit training data's contribution to machine-learning pipelines; however, they often assume passive cont

Qwen 3.6 Plus by @Alibaba_Qwen is now FREE for a limited time on Nous Portal! Nous Portal is one easy subscription that gives you access to …

model-releasesnous-research--x

Qwen 3.6 Plus by @Alibaba_Qwen is now FREE for a limited time on Nous Portal! Nous Portal is one easy subscription that gives you access to 300+ models, exclusive discounts, and bundles your tokens an

Qwen3-VL-Seg: Unlocking Open-World Referring Segmentation with Vision-Language Grounding

arXiv:2605.07141v1 Announce Type: cross Abstract: Open-world referring segmentation requires grounding unconstrained language expressions to precise pixel-level regions. Existing multimodal large lang

Randomness is sometimes necessary for coordination

arXiv:2605.06825v1 Announce Type: new Abstract: Full parameter sharing is standard in cooperative multi-agent reinforcement learning (MARL) for homogeneous agents. Under permutation-symmetric observat

RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation

arXiv:2605.07334v1 Announce Type: new Abstract: Video Reasoning Segmentation (VRS) aims to segment target objects in videos based on implicit instructions that convey human intent and temporal logic.

Read the full blog: https://www.together.ai/blog/serving-deepseek-v4-why-million-token-context-is-an-inference-systems-problem# Watch the fu…

model-releasestogether-ai--x

Read the full blog: https://www.together.ai/blog/serving-deepseek-v4-why-million-token-context-is-an-inference-systems-problem# Watch the full webinar on DeepSeek v4: https://www.youtube.com/watch?v=D

Real-IAD MVN: A Multi-View Normal Vector Dataset and Benchmark for High-Fidelity Industrial Anomaly Detection

arXiv:2605.07149v1 Announce Type: new Abstract: Industrial Anomaly Detection (IAD) is critical for quality control, but existing methods struggle with subtle, geometric defects. Standard 2D (RGB) imag

ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning

arXiv:2605.06483v2 Announce Type: replace Abstract: Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has

RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion

arXiv:2503.06223v5 Announce Type: replace Abstract: Large Vision-Language Models (VLMs) are increasingly deployed in open-ended environments, where ensuring reliable safety under multimodal inputs is

Region4Web: Rethinking Observation Space Granularity for Web Agents

arXiv:2605.07134v1 Announce Type: cross Abstract: Web agents perceive web pages through an observation space, yet its granularity has remained an underexamined design choice. Existing work treats obse

Rep2Text: Decoding Full Text from a Single LLM Token Representation

arXiv:2511.06571v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In t

ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards

arXiv:2510.00568v3 Announce Type: replace Abstract: Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement l

Rethinking Dense Optical Flow without Test-Time Scaling

arXiv:2605.08000v1 Announce Type: new Abstract: Recent progress in dense optical flow has been driven by increasingly complex architectures and multi-step refinement for test-time scaling. While these

Rethinking Weight Tying: Pseudo-Inverse Tying for LM Stable Training and Updates

arXiv:2602.04556v2 Announce Type: replace Abstract: Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output pr