All entries

ExploitBench: A Capability Ladder Benchmark for LLM Cybersecurity Agents

arXiv:2605.14153v1 Announce Type: cross Abstract: Exploitation is not a binary event. It is a ladder of acquiring progressive capabilities, from executing a single buggy line of code to taking full co

Exploring Bottlenecks in VLM-LLM Navigation: How 3D Scene Understanding Capability Impacts Zero-Shot VLN

DGX agent

arXiv:2605.14801v1 Announce Type: new Abstract: Zero-shot vision-and-language navigation (VLN) has gained significant attention due to its minimal data collection costs and inherent generalization. Th

researcharxiv-cs-ro

15 May 2026

Exploring Geographic Relative Space in Large Language Models through Activation Patching

arXiv:2605.14535v1 Announce Type: new Abstract: The increased use of Large Language Models (LLMs) in geography raises substantial questions about the safety of integrating these tools across a wide ra

safetyarxiv-cs-lg

Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study

model-releasesarxiv-cs-cv

arXiv:2605.14845v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have demonstrated strong capabilities in general visual reasoning, yet their applicability to rigor

FaceParts: Segmentation and Editing of Gaussian Splatting

arXiv:2605.13853v1 Announce Type: cross Abstract: Facial editing is an important task with applications in entertainment, virtual reality, and digital avatars. Most existing approaches rely on generat

FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding

arXiv:2602.03417v2 Announce Type: replace Abstract: Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages

researcharxiv-cs-cl

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding

arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor

researcharxiv-cs-cl

FactorizedHMR: A Hybrid Framework for Video Human Mesh Recovery

arXiv:2605.14854v1 Announce Type: cross Abstract: Human Mesh Recovery (HMR) is fundamentally ambiguous: under occlusion or weak depth cues, multiple 3D bodies can explain the same image evidence. This

Fair and Calibrated Toxicity Detection with Robust Training and Abstention

arXiv:2605.14074v1 Announce Type: new Abstract: Fairness in toxicity classification involves three integrated axes: ranking, calibration, and abstention. Training-time interventions and post-hoc safet

safetyarxiv-cs-lg

Industry

Falcon 9 lands on Landing Zone 40

Falcon 9 successfully completed a landing at Landing Zone 40, SpaceX's designated landing facility. This represents another successful booster recovery, which is part of SpaceX's reusable rocket strat

industryelon-musk--x

Hardware

Falkor-IRAC: Graph-Constrained Generation for Verified Legal Reasoning in Indian Judicial AI

arXiv:2605.14665v1 Announce Type: new Abstract: Legal reasoning is not semantic similarity search. A court judgment encodes constrained symbolic reasoning: precedent propagation, procedural state tran

hardwarearxiv-cs-ai

Hardware

FALO: Fast and Accurate LiDAR 3D Object Detection on Resource-Constrained Devices

arXiv:2506.04499v2 Announce Type: replace Abstract: Existing LiDAR 3D object detection methods predominantely rely on sparse convolutions and/or transformers, which can be challenging to run on resour

hardwarearxiv-cs-cv

Fast Adversarial Attacks with Gradient Prediction

arXiv:2605.14868v1 Announce Type: new Abstract: Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even 'fast' attacks s

Fast Rates for Inverse Reinforcement Learning

arXiv:2605.14599v1 Announce Type: cross Abstract: We establish novel structural and statistical results for entropy-regularized min-max inverse reinforcement learning (Min-Max-IRL) with linear reward

Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2

arXiv:2605.13904v1 Announce Type: cross Abstract: Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evalu

FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology

model-releasesarxiv-cs-cv

arXiv:2605.14590v1 Announce Type: new Abstract: Robust whole-slide image (WSI) analysis under strict data-governance remains challenging due to substantial cross-institutional stain heterogeneity. Dom

Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers

arXiv:2605.13974v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) and related flow-based architectures are now among the strongest text-to-image generators, yet the internal mechanisms t

Industry

Filings: YouTube and Snap reached agreements to settle a lawsuit headed to trial in June over claims addiction to social media has disrupted students' learning (Bloomberg)

Bloomberg: Filings: YouTube and Snap reached agreements to settle a lawsuit headed to trial in June over claims addiction to social media has disrupted students' learning — Google's YouTube and Snap I

industrytechmeme

Finding Interpretable Prompt-Specific Circuits in Language Models

arXiv:2602.13483v2 Announce Type: replace-cross Abstract: Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. A cru

applicationsarxiv-cs-ai

Agents

Fine, you all want to code like this I guess. (Runway's new Agent mode is quite impressive, doing fairly complex story building from just a …

Fine, you all want to code like this I guess. (Runway's new Agent mode is quite impressive, doing fairly complex story building from just a short text description of what you want. Not error free obvi

agentsethan-mollick--x

Finite Sample Bounds for Learning with Score Matching

arXiv:2605.14168v1 Announce Type: new Abstract: Learning of continuous exponential family distributions with unbounded support remains an important area of research for both theory and applications in

Tools

First talk in our AI track at #PyConUS is coming up at 11am

First talk in our AI track at #PyConUS is coming up at 11am Today at 11:00 AM I’ll be speaking at PyCon US in Grand Ballroom A at the Long Beach Convention Center about AI-assisted contributions and t

toolssimon-willison--x

FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching

model-releasesarxiv-cs-cv

arXiv:2604.06757v2 Announce Type: replace Abstract: Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We chal

FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing

arXiv:2602.01664v4 Announce Type: replace Abstract: In recent years, agentic workflows have been widely applied to solve complex human tasks. However, existing workflow construction still faces key ch

safetyarxiv-cs-ai

Focused PU learning from imbalanced data

arXiv:2605.14467v1 Announce Type: new Abstract: We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as disease g

applicationsarxiv-cs-lg

ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing

arXiv:2605.14285v1 Announce Type: cross Abstract: Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulati

applicationsarxiv-cs-lg

Forgetting That Sticks: Quantization-Permanent Unlearning via Circuit Attribution

model-releasesarxiv-cs-cl

arXiv:2605.15138v1 Announce Type: cross Abstract: Standard unlearning evaluations measure behavioral suppression in full precision, immediately after training, despite every deployed language model be

Tools

free to start. fast on day zero. proud to power the default model for LangSmith Fleet. happy building.

free to start. fast on day zero. proud to power the default model for LangSmith Fleet. happy building. LangSmith Fleet now has a free model powered by @FireworksAI_HQ for Developer and Plus plans. It’

toolsfireworks-ai--x

Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems

arXiv:2508.15198v2 Announce Type: replace Abstract: Tensor neural networks (TNNs) have demonstrated their superiority in solving high-dimensional problems. However, similar to conventional neural netw

From Data to Action: Accelerating Refinery Optimization with AI

arXiv:2605.15085v1 Announce Type: cross Abstract: Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting

From Descriptive to Prescriptive: Uncover the Social Value Alignment of LLM-based Agents

arXiv:2605.14034v1 Announce Type: new Abstract: Wide applications of LLM-based agents require strong alignment with human social values. However, current works still exhibit deficiencies in self-cogni

Local Ai

From Local Matches to Global Masks: Template-Guided Instance Detection and Segmentation in Open-World Scenes

arXiv:2603.03577v2 Announce Type: replace Abstract: Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of

local-aiarxiv-cs-cv

Agents

From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing

arXiv:2605.15181v1 Announce Type: new Abstract: Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., ``make this advertisement more vegetari

agentsarxiv-cs-cv

From Ranking to Reasoning: Explainable Web API Recommendation via Semantic Reasoning

arXiv:2511.05820v2 Announce Type: replace-cross Abstract: The rapid growth of Web APIs has made automated Web API recommendation essential for efficient mashup development. However, existing approache

safetyarxiv-cs-ai

From Scenes to Elements: Multi-Granularity Evidence Retrieval for Verifiable Multimodal RAG

model-releasesarxiv-cs-cl

arXiv:2605.15019v1 Announce Type: new Abstract: Multimodal Retrieval-Augmented Generation (RAG) systems retrieve evidence at coarse granularities (entire images or scenes), creating a mismatch with fi

From Sparse to Dense: Spatio-Temporal Fusion for Multi-View 3D Human Pose Estimation with DenseWarper

arXiv:2605.14525v1 Announce Type: new Abstract: In multi-view 3D human pose estimation, models typically rely on images captured simultaneously from different camera views to predict a pose at a speci

applicationsarxiv-cs-cv

From Street View to Visual Network: Mapping the Visibility of Urban Landmarks with Vision-Language Models

arXiv:2505.11809v3 Announce Type: replace Abstract: Visibility analysis in urban planning has traditionally relied on line-of-sight (LoS) simulations, which capture geometric occlusion. However, these

applicationsarxiv-cs-cv

From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement

arXiv:2605.14912v1 Announce Type: new Abstract: Pluralistic alignment is typically operationalised as preference aggregation: producing responses that span (Overton), steer toward (Steerable), or prop

From Table to Cell: Attention for Better Reasoning with TABALIGN

arXiv:2605.14465v1 Announce Type: new Abstract: Multi-step LLM reasoning over structured tables fails because planning and execution share no explicit cell-grounding contract. Existing methods constra

From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents

model-releasesarxiv-cs-cl

arXiv:2605.15104v1 Announce Type: new Abstract: Voice agents increasingly require reliable tool use from speech, whereas prominent tool-calling benchmarks remain text-based. We study whether verified

From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)

arXiv:2602.14674v4 Announce Type: replace Abstract: Gradual argumentation is a field of symbolic AI which is attracting attention for its ability to support transparent and contestable AI systems. It

FrontierSmith: Synthesizing Open-Ended Coding Problems at Scale

arXiv:2605.14445v1 Announce Type: new Abstract: Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent progress in LLM coding has focused on well-defined tas

applicationsarxiv-cs-lg

FU-MPC: Frontier- and Uncertainty-Aware Model Predictive Control for Efficient and Accurate UAV Exploration with Motorized LiDAR

arXiv:2605.14920v1 Announce Type: new Abstract: Efficient UAV exploration in unknown environments requires rapid coverage expansion while maintaining accurate and reliable localization, since safe nav

researcharxiv-cs-ro

Agents

Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning

arXiv:2605.14501v1 Announce Type: cross Abstract: This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations

agentsarxiv-cs-ai

Fun interview with Jacob Effron on the Unsupervised Learning podcast.

Fun interview with Jacob Effron on the Unsupervised Learning podcast. It’s hard to imagine more of a dream Unsupervised Learning guest than @ylecun. Yann is one of the godfathers of AI, and he has som

researchyann-lecun--x

Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs

arXiv:2410.06431v5 Announce Type: replace Abstract: Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often becom

Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability

researchmicrosoft-research

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want

Fusion-fission forecasts when AI will shift to undesirable behavior

arXiv:2605.14218v1 Announce Type: new Abstract: The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self