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arXiv:2606.10333v1 Announce Type: new Abstract: Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction mod

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Quality Is Not a Safety Proxy Under Quantization

DGX agent

arXiv:2606.10154v1 Announce Type: new Abstract: Quantized checkpoints are often screened first with quality metrics and only later, if at all, with direct safety tests. This paper audits that shortcut

model-releasesarxiv-cs-lg

10 Jun 2026

REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs

model-releasesarxiv-cs-cl

arXiv:2606.10694v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons. However, due to their finite context window, LLMs

RobotEQ: Transitioning from Passive Intelligence to Active Intelligence in Embodied AI

model-releasesarxiv-cs-ro

arXiv:2605.06234v2 Announce Type: replace Abstract: Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instruc

SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

arXiv:2606.10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model trainin

researcharxiv-cs-lg

Safety

Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs

arXiv:2606.10487v1 Announce Type: cross Abstract: Deploying large language models in user-facing systems requires efficient output safety filtering. Existing approaches typically rely on a separate mo

safetyarxiv-cs-ai

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606.11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop i

Applications

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

arXiv:2606.10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching mo

applicationsarxiv-cs-ai

Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport

arXiv:2606.10071v1 Announce Type: cross Abstract: We introduce Temporal Sheaf Neural Networks (TSNN), a temporal link prediction framework that equips each node with a time-varying orthogonal frame an

Safety

The Role of Feedback Alignment in Self-Distillation

arXiv:2606.11173v1 Announce Type: new Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains t

safetyarxiv-cs-ai

Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization

arXiv:2606.09927v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantiza

Training LLMs to Enforce Multi-Level Instruction Hierarchies via Gravity-Weighted Direct Preference Optimization

model-releasesarxiv-cs-cl

arXiv:2606.10860v1 Announce Type: cross Abstract: Production LLMs receive instructions from sources with very different levels of trust, yet attend to every token with uniform architectural privilege.

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions

arXiv:2512.11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in

Whisfusion: Parallel ASR Decoding with Masked Diffusion

arXiv:2508.07048v2 Announce Type: replace-cross Abstract: Autoregressive (AR) encoder-decoder models dominate high-quality multilingual ASR, but their left-to-right decoders make inference latency sca

researcharxiv-cs-ai

XtrAIn: Training-Guided Occlusion for Feature Attribution

arXiv:2606.10877v1 Announce Type: cross Abstract: Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting c

A Framework for Evaluating and Benchmarking Concept Drift Detection Methods

arXiv:2606.07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance. Despite the proliferation o

A Unifying Framework for Concept-Based Representational Similarity

arXiv:2606.09653v1 Announce Type: new Abstract: Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions

AgentCompile: An LLM-Guided Compiler for Direct CUDA Inference

arXiv:2606.07665v1 Announce Type: cross Abstract: Transformer inference increasingly depends on specialized compiler and runtime support, but real model graphs still require semantic decisions about w

AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation

arXiv:2606.09556v1 Announce Type: new Abstract: AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different

Benchmarking Open-Ended Multi-Agent Coordination in Language Agents

arXiv:2606.08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.

Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing

arXiv:2606.08780v1 Announce Type: new Abstract: Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but

Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (and Fail) Red Team Attacks

arXiv:2606.07833v1 Announce Type: cross Abstract: Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate (ASR), not taking into account the se

Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

arXiv:2606.07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS

CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning

arXiv:2606.09393v1 Announce Type: new Abstract: Image and video captioning are fundamental tasks that bridge the visual and linguistic domains, playing a critical role in pre-training Large Vision-Lan

researcharxiv-cs-cv

CHROMA: Detecting AI-Generated Images through Inter-Channel Color-Space Correlations

arXiv:2606.08864v1 Announce Type: new Abstract: The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photog

Claude Fable 5 and new AI safety fables

model-releasesinterconnects

This article discusses Claude Fable 5, likely exploring Anthropic's latest version of their AI model and examining new fables or narratives related to AI safety concepts. The piece probably analyzes h

Claude Fable 5 is now available on Databricks, fully governed through Unity AI Gateway

Claude Fable 5 is now available as a model option on the Databricks platform, integrated with Unity AI Gateway to provide governance controls for enterprise users. This integration allows organization

model-releasesdatabricks

Claude Mythos went from “too dangerous to release” to publicly available (with some extra guard rails) in two months. And y’all fell for Ant…

model-releasesgary-marcus--x

Gary Marcus critiques Anthropic's rapid shift in positioning Claude from a model deemed too dangerous for public release to one made widely available with safety measures, suggesting this represents i

ComplexConstraints and Beyond: Expert Rubrics for RLVR

arXiv:2606.09118v1 Announce Type: new Abstract: As LLM capabilities advance rapidly, the evaluation methods used to assess them increasingly lag behind. Traditional benchmarks relied on programmatic v

Safety

CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation

arXiv:2601.15408v2 Announce Type: replace-cross Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consis

safetyarxiv-cs-ai

Deep Tree Tensor Networks

arXiv:2502.09928v2 Announce Type: replace-cross Abstract: Originating in quantum physics, tensor networks (TNs) have been widely adopted as exponential machines and parametric decomposers for recognit

Disjoint Generation of Synthetic Data

arXiv:2507.19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into

researcharxiv-cs-lg

Frequency-Domain Latent Attention Gating for Cross-Domain Token Aggregation

arXiv:2606.08191v1 Announce Type: cross Abstract: Token aggregation is a common bottleneck in models that map token representations to sample-level predictions, yet most pooling methods operate only i

Graph2Idea:Retrieval-Augmented Scientific Idea Generation with Graph-Structured Contexts

arXiv:2606.09105v1 Announce Type: new Abstract: Generating novel, feasible, and high-quality research ideas is an important yet challenging task in scientific discovery.Recent Large Language Model (LL

HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents

arXiv:2606.09738v1 Announce Type: new Abstract: Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-ba

iOSWorld: A Benchmark for Personally Intelligent Phone Agents

arXiv:2606.09764v1 Announce Type: new Abstract: A useful phone agent needs to be personally intelligent. It should reason over a user's identity, history, and preferences as they exist on the device,

Hardware

Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

arXiv:2602.10016v3 Announce Type: replace-cross Abstract: Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing

hardwarearxiv-cs-ai

Language as a Sensor: Calibrated Spatial Belief Estimation in 3D Scenes from Natural Language

model-releasesarxiv-cs-ro

arXiv:2606.08666v1 Announce Type: new Abstract: Robots deployed in human-centric environments routinely receive natural-language descriptions of spatial information ('I left my backpack on the table')

MedVision: Benchmarking Quantitative Medical Image Analysis

arXiv:2511.18676v2 Announce Type: replace-cross Abstract: Current vision-language models (VLMs) in medicine are primarily designed for categorical question answering (e.g., 'Is this normal or abnormal

Microsoft AI head calls out Anthropic for acting like Claude is conscious

model-releasesthe-verge-ai

Microsoft AI CEO Mustafa Suleyman says it's 'really, really dangerous' for Anthropic to speculate about Claude's consciousness inside its 'constitution,' or the instructions that tell the model how to

More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs

arXiv:2606.08471v1 Announce Type: cross Abstract: Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whet

researcharxiv-cs-ai

Applications

Next-Token Prediction Learns Generalisable Representations of Sleep Physiology

arXiv:2606.09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applica

applicationsarxiv-cs-ai

OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics

arXiv:2606.09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a s

OmniTryOn: Video Try-On Anything at Once!

arXiv:2606.08514v1 Announce Type: new Abstract: Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restr

One Stone, Three Birds: Self-adaptive Optimal Transport for Multi-VLM Selection, Adaptation, and Ensembling

arXiv:2606.08126v1 Announce Type: new Abstract: Vision-language models (VLMs) enable visual recognition from semantic class descriptions, which makes them attractive when target annotations are scarce

researcharxiv-cs-cv

Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits

arXiv:2606.08977v1 Announce Type: new Abstract: Motivated by the recency effect in online learning, we study algorithms for single-pass *sliding-window streaming multi-armed bandits (MABs)* in this pa

PereStruct: Multimodal Semantic Assembly for Robust Historical Document Parsing

arXiv:2606.07661v1 Announce Type: new Abstract: Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typ

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

arXiv:2603.05500v2 Announce Type: replace-cross Abstract: Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this cha