model releases

An Empirical Study of LLM-Generated Specifications for VeriFast

arXiv:2606.26490v1 Announce Type: cross Abstract: Static verification tools can assure industrial scale software, but require significant human labor to write specifications. This is particularly true

And the balance returned to earth

DGX agent

And the balance returned to earth Introducing a limited preview of GPT-5.6 Sol, our next generation frontier model, as well as GPT-5.6 Terra, a balanced model for efficient, everyday work, and GPT-5.6

model-releasesitamar-friedman--x

26 Jun 2026

Anthropic says the US government is allowing Mythos 5 to be redeployed to critical infrastructure operators, and is working to restore general access to Fable 5 (@anthropicai)

@anthropicai: Anthropic says the US government is allowing Mythos 5 to be redeployed to critical infrastructure operators, and is working to restore general access to Fable 5 — Since June 12, we've be

model-releasestechmeme

Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards

arXiv:2606.27376v1 Announce Type: new Abstract: Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision,

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

arXiv:2606.26104v1 Announce Type: cross Abstract: Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about ani

Asymptotically Optimal Learning for Parametric Prophet Inequalities

arXiv:2606.26893v1 Announce Type: new Abstract: We study learning in prophet inequalities with i.i.d. rewards drawn from an exponential-type parametric family with an unknown parameter heta, a class t

auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation

arXiv:2606.26460v1 Announce Type: new Abstract: AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data. Data collection is

Autoformalization of Agent Instructions into Policy-as-Code

arXiv:2606.26649v1 Announce Type: new Abstract: Agent safety in high-stakes domains requires formal policy enforcement, but most existing approaches either rely on probabilistic guardrails (fine-tuned

Autoregressive Boltzmann Generators

arXiv:2606.27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the dev

Bayesian Optimization for General Reaction Conditions

arXiv:2502.18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications

Benchmarking Open-Weight Foundation Models for Global AI Technical Governance

arXiv:2606.26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisat

Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference

arXiv:2606.27090v1 Announce Type: cross Abstract: Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies

Beyond Perplexity: UTF-8 Validity in Byte-aware Language Models

arXiv:2606.14122v2 Announce Type: replace Abstract: Byte-level tokenization enables language models to handle any Unicode input, but models can generate invalid UTF-8 sequences when encountering rare

Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

arXiv:2606.27314v1 Announce Type: new Abstract: To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanin

Boundary-Aware Context Grounding for A Low-Channel EEG Agent

arXiv:2606.26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use. However, a general model does not automatically know which measurements a parti

BOWConnect: Parallel Bayesian Optimization over Windows with Learned Local Cost Maps for Sample-Efficient Kinodynamic Motion Planning

model-releasesarxiv-cs-ro

arXiv:2606.27292v1 Announce Type: new Abstract: This paper presents BOWConnect, a bidirectional parallel kinodynamic motion planner that addresses three fundamental limitations of existing sampling-ba

Can Large Language Models Reliably Code Qualitative Humanitarian Data? A Benchmark Study Against Human Expert Adjudication

arXiv:2606.26541v1 Announce Type: new Abstract: Data from affected populations are crucial for informing humanitarian response, but their value depends on timely and consistent interpretation of nuanc

Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT

arXiv:2606.26861v1 Announce Type: new Abstract: Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry

arXiv:2606.26538v1 Announce Type: cross Abstract: Deep Transformers are composed of uniformly stacked residual blocks, yet their deepest layers often add little value. We present two efficiency method

Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean

arXiv:2606.26618v1 Announce Type: new Abstract: Large pretrained text-to-speech (TTS) models sound almost human for well-resourced languages, but much worse for languages that are rare in their traini

Comparing BERT Sentence-Pair Classification and Few-Shot LLM Prompting for Detecting Threat and Solution Framing in German Climate News

arXiv:2606.26489v1 Announce Type: new Abstract: News media play a central role in shaping public perceptions of climate change, and whether coverage emphasizes threats or solutions has measurable effe

Confidence-Aware Tool Orchestration for Robust Video Understanding

arXiv:2606.26904v1 Announce Type: cross Abstract: Video reasoning language models implicitly assume that every input frame is equally reliable. This leads to what we term the Blind Trust Problem: unde

ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence

arXiv:2606.26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to

Context-Aware Synthesis of Optimization Pipelines for Warehouse Optimization

arXiv:2606.26852v1 Announce Type: new Abstract: Order fulfillment in manual picker-to-goods warehouses involves interconnected decisions such as item assignment, order batching, and picker routing. Wh

Context Recycling for Long-Horizon LLM Inference

arXiv:2606.26105v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong capabilities in short-context reasoning but degrade in performance over long conversational horizons due t

ConvMemory v3: A Validity Context Layer for Conversational Memory via Target-Conditioned Relation Verification

arXiv:2606.26753v1 Announce Type: new Abstract: Conversational memory retrieval optimizes relevance, yet a retrieved memory can be relevant and simultaneously outdated: a later turn updates, corrects,

CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs

arXiv:2606.27264v1 Announce Type: new Abstract: Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text jud

CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?

arXiv:2606.26216v1 Announce Type: cross Abstract: We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability det

Data-driven Machine Learning Cannot Reach Symbolic-level Logical Reasoning -- The Limit of the Scaling Law

arXiv:2606.26454v1 Announce Type: new Abstract: Sphere neural networks have achieved symbolic level syllogistic reasoning without training data, raising the question of where the limit of the scaling

Decision-Aligned Evaluation of Uncertainty Quantification

arXiv:2606.26990v1 Announce Type: cross Abstract: Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration e

DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection

arXiv:2606.26687v1 Announce Type: new Abstract: In industrial environments, new product categories arrive sequentially, requiring continual anomaly detection without access to past data. Normalizing F

DiCoBench: Benchmarking Multi-Image Fine-Grained Perception via Differential and Commonality Visual Cues

arXiv:2606.26602v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive fine-grained perception capabilities. However, existing ben

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT

arXiv:2601.01701v2 Announce Type: replace-cross Abstract: Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, wi

Disco-LoRA: Disentangled Composition of Content, Style, and Motion for Multi-concept Video Customization

arXiv:2606.26668v1 Announce Type: cross Abstract: Video customization based on Text-to-Video (T2V) models aims to learn specific features from reference data to generate controllable videos. While sig

Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation

arXiv:2606.26116v1 Announce Type: cross Abstract: A brand whose customers use both ChatGPT and Claude for product recommendations faces a strategic choice: a single optimization playbook, or one per p

Do Image Editing Models Understand Lighting?

arXiv:2606.26738v1 Announce Type: new Abstract: While recent advancements in generative image editing models have achieved stunning visual fidelity, it remains an open question whether these systems p

Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review

arXiv:2606.12716v2 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-review workflows introduces novel and significant r

Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution

arXiv:2606.26716v1 Announce Type: cross Abstract: Arbitrary slice super-resolution reconstructs isotropic volumes from anisotropic clinical acquisitions by synthesizing intermediate slices at arbitrar

DualEval: Joint Model-Item Calibration for Unified LLM Evaluation

arXiv:2606.26429v1 Announce Type: cross Abstract: Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style

Dynamic workflows (generating harnesses on the fly) are a new form of test-time compute. But LLMs aren't great at building them. I often hav…

Dynamic workflows (generating harnesses on the fly) are a new form of test-time compute. But LLMs aren't great at building them. I often have to steer agents to generate complex patterns. Curious how

model-releasesdair-ai--x

EMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening

arXiv:2606.26337v1 Announce Type: new Abstract: Gradient Boosted Decision Trees (GBDT), exemplified by LightGBM, spend a dominant fraction of training time -- typically 65-70% -- constructing per-feat

Embarrassingly Simple Self-Distillation Improves Code Generation

arXiv:2604.01193v2 Announce Type: replace Abstract: Can a large language model (LLM) improve at code generation using only its own raw outputs, without a verifier, a teacher model, or reinforcement le

Empirical Software Engineering TerraProbe: A Layered-Oracle Framework for Detecting Deceptive Fixes in LLM-Assisted Terraform

arXiv:2606.26590v1 Announce Type: new Abstract: Security misconfigurations in Terraform Infrastructure-as-Code are a growing risk in cloud deployments, and large language models are increasingly used

EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

arXiv:2606.27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions. I

Error-Conditioned Neural Solvers

arXiv:2606.27354v1 Announce Type: cross Abstract: Neural surrogate models offer fast approximate mappings from PDE parameters to solutions, but they typically treat solving as a purely statistical tas

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

arXiv:2606.26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv:2510.17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary sy

extsc{DiARC}: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models

arXiv:2606.26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~itealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and