model releases

Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Bias

arXiv:2607.01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answerin

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Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach

DGX agent

arXiv:2607.02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking diffi

model-releasesarxiv-cs-ai

3 Jul 2026

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

arXiv:2509.25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whit

Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation

arXiv:2607.02182v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence,

Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification

arXiv:2607.01272v1 Announce Type: cross Abstract: Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must

Beyond Pixel Diffs: Benchmarking Image Change Captioning for Web UI Visual Regression Testing

arXiv:2607.01728v1 Announce Type: cross Abstract: Visual regression testing (VRT) is a standard quality assurance step in modern software release pipelines. On every change, it re-renders user interfa

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework

arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i

Black-Box Inference of LLM Architectural Properties with Restrictive API Access

arXiv:2607.01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given l

Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks

arXiv:2607.02003v1 Announce Type: cross Abstract: Although neural networks are remarkably effective, their underlying optimization principles remain theoretically elusive, often characterized by non-c

Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers

arXiv:2607.01478v1 Announce Type: cross Abstract: We measured quantization-induced decision-boundary changes using local logit-margin radii, first-order boundary displacement, normal variation, slice-

BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems

arXiv:2607.01600v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_

Breaking Safety at the Token Boundary: How BPE Tokenization Creates Exploitable Gaps in LLM Alignment

arXiv:2607.01239v1 Announce Type: cross Abstract: Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural

BRIDGE: Predicting Human Task Completion Time From Model Performance

arXiv:2602.07267v2 Announce Type: replace Abstract: Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty. Ex

Bringing Agentic Search to Earth Observation Data Discovery

arXiv:2607.02387v1 Announce Type: cross Abstract: NASA and its data centers hold thousands of geoscience datasets and tools like Worldview, Giovanni, the Science Discovery Engine, and Harmony. Finding

BuilderBench: The Building Blocks of Intelligent Agents

arXiv:2510.06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set b

CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation

arXiv:2607.02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decompositio

COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows

arXiv:2607.01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently. As these workflows become repeated

Composite Reward Design in PPO-Driven Adaptive Filtering

arXiv:2506.06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environme

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

arXiv:2607.01916v1 Announce Type: new Abstract: Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long

Controllable Sim Agents with Behavior Latents

arXiv:2607.02496v1 Announce Type: cross Abstract: Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enabl

CPG-PAD: Concept-Informed Prompts Guided Presentation Attack Detection

arXiv:2607.01303v1 Announce Type: cross Abstract: Presentation Attack Detection (PAD) serves as a crucial safeguard for face recognition systems against presentation attacks such as printed photos, re

Customers are not an abstraction for us: we exist to help enterprises, public institutions, and industries build their own intelligence, so …

model-releasesmistral-ai--x

Customers are not an abstraction for us: we exist to help enterprises, public institutions, and industries build their own intelligence, so the value created from their data, workflows, feedback, and

DecompRL: Solving Harder Problems by Learning Modular Code Generation

arXiv:2607.02390v1 Announce Type: new Abstract: How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with a

Decoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS

arXiv:2607.01810v1 Announce Type: cross Abstract: Open-source projects depend on a steady inflow of newcomers. A growing concern is that AI coding agents (tools such as Cursor and Claude Code that wri

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network

arXiv:2607.02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state

Denser neq Better: Limits of On-Policy Self-Distillation for Continual Post-Training

arXiv:2607.01763v1 Announce Type: cross Abstract: Continual post-training enables foundation models to acquire new knowledge while preserving existing capabilities. Recent work suggests that on-policy

Discrete Diffusion Language Models for Interactive Radiology Report Drafting

arXiv:2607.01436v1 Announce Type: new Abstract: Diffusion language models, which generate text by denoising a token canvas bidirectionally instead of emitting tokens left to right, have become competi

Distributed Attacks in Persistent-State AI Control

arXiv:2607.02514v1 Announce Type: new Abstract: As AI coding agents become more autonomous, they increasingly ship code iteratively, with the codebase persisting across sessions. This persistence crea

Distributionally Robust Listwise Preference Optimization

arXiv:2607.01715v1 Announce Type: new Abstract: Existing robust preference optimization for language-model alignment mainly studies pairwise supervision and places robustness at the dataset, prompt, o

Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry

arXiv:2607.01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibr

DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM

model-releasesarxiv-cs-ro

arXiv:2607.01757v1 Announce Type: cross Abstract: Deep-learning features excel in visual matching, yet their practical value in tightly coupled visual-inertial SLAM (VI-SLAM) remains insufficiently ch

Do Newer Lightweight CNNs Perform Better Under Resource Constraints? A Controlled Multigenerational Study of Architecture, Initialization, Training Budget, and Efficiency

arXiv:2607.01984v1 Announce Type: cross Abstract: Newer lightweight convolutional neural networks are often presented as improving predictive performance and deployment efficiency, but such claims req

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on …

model-releasesclem-delangue--x

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and le

eCream-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian

arXiv:2606.12569v2 Announce Type: replace-cross Abstract: We present eCream-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals. Th

EduArt: An educational-level benchmark for evaluating art history knowledge in large language models

arXiv:2607.02007v1 Announce Type: new Abstract: Large language models now score near ceiling on general benchmarks, but these aggregate measures reveal little about how models behave within single dis

Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots

model-releasesarxiv-cs-ro

arXiv:2607.02501v1 Announce Type: new Abstract: Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model

EO-Agents: A Three-Agent LLM Pipeline for Earth Observation Hypothesis Generation

arXiv:2607.01584v1 Announce Type: new Abstract: Large language models have recently been explored for scientific hypothesis generation, but most prior work relies on unstructured literature and free-f

EPnG: Adaptive Expert Prune-and-Grow for Parameter-Efficient MoE Fine-tuning

arXiv:2607.01789v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale efficiently but remain costly to adapt due to redundant experts and uniform parameter allocation. Existing param

Evidence-State Rewards for Long-Context Reasoning

arXiv:2607.02073v1 Announce Type: new Abstract: Long-context reasoning requires models to locate, revise, and synthesize evidence distributed across lengthy inputs. Existing long-context RL methods us

EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments

arXiv:2607.02440v1 Announce Type: new Abstract: Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a

eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions

arXiv:2607.01275v1 Announce Type: cross Abstract: Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture t

Excited to share our paper, “Learning Multi-Agent Coordination via Sheaf-ADMM” to be presented at #ICML2026 Blog: https://pub.sakana.ai/shea…

model-releasesdavid-ha--x

Excited to share our paper, “Learning Multi-Agent Coordination via Sheaf-ADMM” to be presented at #ICML2026 Blog: https://pub.sakana.ai/sheaf-admm/ Most AI models process information as one giant, mon

Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability

arXiv:2607.01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcom

Fable's judgement

model-releasessimon-willison

One of the most interesting tips I got from the Fireside Chat I hosted with Cat Wu and Thariq Shihipar from the Claude Code team at AIE on Wednesday was to let Fable (and to a certain extent Opus) use

Fast Multi-dimensional Refusal Subspaces via RFM-AGOP

arXiv:2607.02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behav

Fixed-Set Robustness in Programming by Example: Example Corruption and Semantic Partition Recovery

arXiv:2607.01280v1 Announce Type: new Abstract: Programming-by-example systems infer programs from a small set of input-output examples. Robust PBE work usually models wrong examples as samples from a

Frequency Shift Physics-Informed Extreme Learning Machine for Solving High-Frequency Partial Differential Equations

arXiv:2607.01694v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) with high-frequency solutions remains a central challenge in physics-informed machine learning due to spec

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

arXiv:2512.10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiv