model releases

Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning

arXiv:2606.21447v1 Announce Type: cross Abstract: Automated radiology report generation (RRG) has gained increasing attention because it can reduce the heavy workload of clinical report writing. Howev

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Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI

DGX agent

arXiv:2501.09731v2 Announce Type: replace-cross Abstract: We establish a formal connection between the decades-old surrogate outcome model in biostatistics and economics and the emerging field of pred

model-releasesarxiv-cs-lg

23 Jun 2026

Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning

arXiv:2606.22878v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed at the network edge to provide pervasive generative AI services, decentralized federated learn

Probe-and-Refine Tuning of Repository Guidance for Coding Agents

arXiv:2606.20512v2 Announce Type: replace-cross Abstract: LLM-based coding agents need higher-level operational knowledge about a repository (which files house which subsystems, how to run the test su

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs

arXiv:2606.22914v1 Announce Type: new Abstract: Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic g

Protein contacts are already in the attention: a single-forward-pass alternative to the Categorical Jacobian

arXiv:2606.21876v1 Announce Type: new Abstract: The Categorical Jacobian (CJ) of Zhang et al. (2024) reads protein contacts from a language model by perturbing every residue with every alternative ami

PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMs

arXiv:2606.20913v1 Announce Type: new Abstract: Medical vision-language models (VLMs) enable zero-shot clinical image classification, yet reliably detecting out-of-distribution (OOD) inputs at deploym

QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction

arXiv:2606.22421v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within

QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks

arXiv:2511.18689v4 Announce Type: replace Abstract: Kolmogorov--Arnold Networks (KANs) replace linear weights with spline-based functions, offering strong expressivity but posing challenges for low-pr

R2HandoverSim: A Simulation Framework and Benchmark for Robot-to-Human Object Handovers

model-releasesarxiv-cs-ro

arXiv:2606.21011v1 Announce Type: new Abstract: We present R2HandoverSim, a simulation benchmark for robot-to-human (R2H) object handovers. Although R2H handover methods have advanced rapidly, the lac

Real5-OmniDocBench: A Full-Scale Physical Reconstruction Benchmark for Robust Document Parsing in the Wild

arXiv:2603.04205v2 Announce Type: replace Abstract: While Vision-Language Models (VLMs) achieve near-perfect scores on digital document benchmarks like OmniDocBench, their performance in the unpredict

Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement

arXiv:2606.22166v1 Announce Type: cross Abstract: Author rebuttals are the main post-submission window in peer review, but their effect on reviewer scores remains hard to measure because score updates

Region-Specific Calibration Achieves Excellent Inter-Device Reliability for Smartphone Dermatology: A Multi-Device Benchmark on Korean Facial Skin

arXiv:2512.21988v3 Announce Type: replace-cross Abstract: Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitativ

Reinforcement learning to improve large language model-based automated code compliance systems

arXiv:2606.22402v1 Announce Type: cross Abstract: Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucin

REKEY: Metadata-Grounded Visual-Key Regeneration for Contamination-Resilient VQA Evaluation

arXiv:2606.20736v1 Announce Type: new Abstract: Static visual question answering (VQA) benchmarks age quickly: Once the items leak into training corpora, scores can reflect memorization rather than ge

ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation

arXiv:2606.23104v1 Announce Type: new Abstract: On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-genera

Residue-Level Attributions in Protein Language Models Do Not Recover Allergen Epitopes

arXiv:2606.22181v1 Announce Type: new Abstract: Deep allergenicity classifiers are increasingly used in safety screening of novel foods, and recent protein language models have substantially improved

Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning

arXiv:2409.05755v3 Announce Type: replace Abstract: Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studi

Revisiting the Neural Tangent Kernel: the role of large width and depth

arXiv:2511.07272v2 Announce Type: replace Abstract: Overparameterized fully-connected neural networks have been shown to behave like kernel models when trained with gradient descent, assuming standard

Right Knowledge, Wrong Answer: Test-Time Steering for Temporal Fact Conflicts in Open-Weight Language Models

arXiv:2606.20959v1 Announce Type: new Abstract: Large language models can store both outdated facts and newer superseding facts in their parameters, but standard prompting may still elicit the outdate

RLM-Cascade: Response-Level Speculative Decoding for Cost-Efficient LLM API Serving

arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc

Robust 3DGS-based SLAM via Adaptive Kernel Smoothing

arXiv:2511.23221v2 Announce Type: replace Abstract: In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of tracking accuracy. We argue th

RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing

arXiv:2606.18774v2 Announce Type: replace Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://route

RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers

model-releasesarxiv-cs-ro

arXiv:2606.21755v1 Announce Type: new Abstract: Pre-Phase-A design of lunar micro-rovers is dominated by tightly coupled mobility, power, thermal, and mass trades, yet conceptual-design tooling for th

RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation

arXiv:2606.23221v1 Announce Type: new Abstract: Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. Howev

RT-DocLayout: Real-Time End-to-End Document Layout Analysis with Reading Order in the Wild

arXiv:2606.23344v1 Announce Type: new Abstract: Accurate document layout analysis remains a critical bottleneck for document parsing systems, due to the intricate coupling among heterogeneous document

rumors of new models being delayed

model-releasesgary-marcus--x

rumors of new models being delayed 🚨 SCOOP(s): - GPT-5.6 has been delayed and will no longer release this week. New target is ~mid-July. - DeepMind are not satisfied with the current state of 3.5 Pro

SamatNext v0.2-B: An Exploratory Study of RMS-Normalized Hybrid Decoders for Curriculum Retention in Small Code Models

arXiv:2606.22248v1 Announce Type: new Abstract: Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine-tuning on shifting curriculum distributions.

SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding

arXiv:2606.22694v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. Existi

Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers

arXiv:2606.23607v1 Announce Type: new Abstract: Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods

ScanBot: A Benchmark for Precision Robotic Surface Scanning with Industrial Laser Profilers

model-releasesarxiv-cs-ro

arXiv:2505.17295v2 Announce Type: replace Abstract: We introduce ScanBot, a benchmark for instruction-conditioned, high-precision surface scanning with robot-mounted industrial laser profilers. Unlike

Scene-agnostic ALS boresight self-calibration

arXiv:2606.23101v1 Announce Type: new Abstract: ALS boresight calibration has relied for two decades on dedicated flight patterns over structured scenes containing planar surfaces of varied aspect and

SeFi-Image: A Text-to-Image Foundation Model with Semantic-First Diffusion

arXiv:2606.22568v1 Announce Type: new Abstract: Training image generation foundation models consumes substantial resources. Previous methods have attempted to leverage semantic guidance to accelerate

SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

arXiv:2606.17702v2 Announce Type: replace Abstract: Characterising the tumour microenvironment (TME) from routine H&E-stained histology images requires simultaneous cell segmentation, feature extracti

Select-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance

arXiv:2606.22350v1 Announce Type: new Abstract: Reinforcement Learning (RL) has been widely applied to sequential decision-making, yet it often suffers from poor sample efficiency due to costly intera

Self-Evolution for Multi-Turn Tool-Calling Agents via Divergence-Point Preference Learning

arXiv:2606.23112v1 Announce Type: new Abstract: Multi-turn tool-using agents must coordinate long-horizon tool sequences while tracking dialogue state and policy constraints. Existing approaches often

Self-Improvement Can Self-Regress: The Rise-and-Collapse Failure Mode of LLM Self-Training

arXiv:2606.21090v1 Announce Type: cross Abstract: Self-improvement can self-regress. In REINFORCE post-training for code, a model can quickly improve on its optimized metric and then collapse within t

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

arXiv:2606.22210v1 Announce Type: new Abstract: One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trai

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study

arXiv:2602.22747v2 Announce Type: replace Abstract: Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on pos

SFT Overtraining Predicts Rank Inversion via Entropy Collapse Under RLVR

arXiv:2606.18487v2 Announce Type: replace Abstract: The standard heuristic of selecting the SFT checkpoint with the highest pass@1 for GRPO can fail when SFT compresses the rollout distribution. For b

SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning

arXiv:2606.22873v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed in consumer, medical, financial, and enterprise applications. This broad deployment expands the

Skill Coverage: A Test Adequacy Metric for Agent Skills

arXiv:2606.20659v1 Announce Type: cross Abstract: Agent skills encode reusable procedural knowledge that guides large language model agents across tasks and execution contexts. Existing evaluations pr

Small LLMs: Pruning vs. Training from Scratch

arXiv:2606.14150v2 Announce Type: replace Abstract: Pruning promises a shortcut to strong small language models. In this work, we examine this promise by pruning Llama-3.1-8B at pruning ratios of 0.5-

Snyk launches Evo Agentic Development Security to police AI coding agents

model-releasessiliconangle

Cybersecurity company Snyk Ltd. today launched Evo Agentic Development Security, a new layer of its artificial intelligence security platform built to police the autonomous coding agents that increasi

SOHET: Sequence Of Heterogeneous Events Transformer with Self-Supervised Pre-Training

arXiv:2606.21356v1 Announce Type: new Abstract: Many machine learning applications rely on heterogeneous event streams to make predictions, either causally as events arrive or bidirectionally over com

SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation

arXiv:2605.24354v2 Announce Type: replace Abstract: Recently, world models have made significant progress in enhancing end-to-end driving systems through both future situation forecasting and improved

Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams

arXiv:2606.20629v1 Announce Type: cross Abstract: LLM agents are increasingly deployed as multi-role teams, where tasks are divided across specialized roles such as planner, executor, and verifier. In