Search: “banking”

arXiv:2505.16819v4 Announce Type: replace Abstract: Recent advances in scene-based video generation enable coherent visual narratives from structured prompts, yet a key aspect of storytelling -- chara

tutorialsarxiv-cs-cv

Directed Acyclic Graph Convolutional Networks

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arXiv:2506.12218v2 Announce Type: replace-cross Abstract: Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architec

safetyarxiv-cs-lg

20 May 2026

DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables

model-releasesarxiv-cs-cv

arXiv:2605.19688v1 Announce Type: new Abstract: Document manipulation localization models achieve strong performance on public benchmarks yet fail to generalize to operational document workflows. We i

Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support

arXiv:2605.19826v1 Announce Type: new Abstract: Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry c

France is one of the few European countries that truly understood sovereignty. It built its own nuclear deterrent, with around 290 nuclear w…

applicationsyann-lecun--x

France is one of the few European countries that truly understood sovereignty. It built its own nuclear deterrent, with around 290 nuclear warheads and full sovereign control over their use. It remain

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treat…

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treating the long horizon agent optimization problem as one that

agentsharrison-chase--x

Stoked to work closely with @Vtrivedy10 on this. I've spoken about it before but an emerging trend i'm seeing with many of the ai-native com…

Stoked to work closely with @Vtrivedy10 on this. I've spoken about it before but an emerging trend i'm seeing with many of the ai-native companies I work with (shoutout @larsen_weigle_ ) is a focus on

agentsharrison-chase--x

Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentation

arXiv:2605.17904v1 Announce Type: new Abstract: Few-Shot Medical Image Segmentation (FSMIS) aims to delineate novel anatomical targets from one or a few annotated support images, addressing the annota

Causal Intervention-Based Memory Selection for Long-Horizon LLM Agents

arXiv:2605.17641v1 Announce Type: new Abstract: Long-horizon LLM agents rely on persistent memory to support interactions across sessions, yet existing memory systems often retrieve context using sema

Darwinium pushes mobile fraud detection beyond the login moment

model-releasessiliconangle

Artificial intelligence fraud prevention platform company Darwinium UK Ltd. today released updates to its Android and iOS mobile software development kits, adding continuous in-session detection of re

Embodied Task Planning via Graph-Informed Action Generation with Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.21841v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning capabilities, their deployment as embodied agents still faces fundam

Estimating Item Difficulty with Large Language Models as Experts

arXiv:2605.18562v1 Announce Type: cross Abstract: Accurate estimates of item difficulty are essential for valid assessment and effective adaptive learning. However, for newly created tasks, response d

safetyarxiv-cs-ai

Local Ai

LAST-RAG: Literature-Anchored Stochastic Trajectory Retrieval-Augmented Generation for Knowledge-Conditioned Degradation Model Selection

arXiv:2605.17902v1 Announce Type: new Abstract: Stochastic-process-based degradation modeling is a core approach for estimating the distribution of remaining useful life (RUL); however, the selection

local-aiarxiv-cs-ai

One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception

arXiv:2605.17907v1 Announce Type: cross Abstract: By sharing intermediate features, collaborative perception extends each agent's sensing beyond standalone limits, but real-world feature modality hete

PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models

arXiv:2605.17044v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-pl

SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation

arXiv:2605.17630v1 Announce Type: new Abstract: Here's a trimmed version under 1920 characters: Open-vocabulary segmentation models such as SAM3 achieve strong performance through concept-level text p

SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents

arXiv:2605.18636v1 Announce Type: new Abstract: Long-horizon multimodal agents in open-world games must stay goal-directed across many low-level interactions under tight token and latency budgets. Exi

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2605.17577v1 Announce Type: new Abstract: Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to impercepti

Hardware

A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation

arXiv:2605.15475v1 Announce Type: new Abstract: We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-F

hardwarearxiv-cs-cv

18 May 2026

Graph-Regularized Sparse Autoencoders for LLM Safety Steering

arXiv:2512.06655v3 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are increasingly used to extract activation directions for inference-time steering, but their standard sparsity obj

18 May 2026

Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents

arXiv:2510.02837v2 Announce Type: replace Abstract: Although recent tool-augmented benchmarks involve complex requests, evaluation remains limited to answer matching, neglecting critical trajectory as

EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation

arXiv:2605.15199v1 Announce Type: cross Abstract: Multi-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and location

Evidential Reasoning Advances Interpretable Real-World Disease Screening

arXiv:2605.15171v1 Announce Type: cross Abstract: Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical ima

applicationsarxiv-cs-ai

Mechanical Enforcement for LLM Governance:Evidence of Governance-Task Decoupling in Financial Decision Systems

arXiv:2605.14744v1 Announce Type: cross Abstract: Large language models in regulated financial workflows are governed by natural-language policies that the same model interprets, creating a principal-

safetyarxiv-cs-ai

MemLineage: Lineage-Guided Enforcement for LLM Agent Memory

arXiv:2605.14421v1 Announce Type: cross Abstract: We introduce MemLineage, a defense for LLM agent memory that attaches both cryptographic provenance and LLM-mediated derivation lineage to every entry

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track

arXiv:2605.14808v1 Announce Type: new Abstract: Visual anomaly detection (AD) for industrial inspection is a highly relevant task in modern production environments. The problem becomes particularly ch

applicationsarxiv-cs-cv

Action Emergence from Streaming Intent

model-releasesarxiv-cs-cv

arXiv:2605.12622v1 Announce Type: cross Abstract: We formalize action emergence as a target capability for end-to-end autonomous driving: the ability to generate physically feasible, semantically appr

ConRetroBert: EMA Stabilized Dual Encoders for Template-Based Single-Step Retrosynthesis

arXiv:2605.12736v1 Announce Type: new Abstract: Template based single step retrosynthesis predicts reactants by selecting and applying an explicit reaction template, making each prediction traceable t

McCast: Memory-Guided Latent Drift Correction for Long-Horizon Precipitation Nowcasting

arXiv:2605.13197v1 Announce Type: cross Abstract: Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. Howe

researcharxiv-cs-ai

Mechanistic Evidence for Spectral Structures in Prior-Data Fitted Networks

arXiv:2601.21731v2 Announce Type: replace Abstract: Prior-Data Fitted Networks (PFNs) enable amortized Bayesian inference in a single forward pass, yet their internal representations remain opaque. It

Multi-Quantile Regression for Extreme Precipitation Downscaling

arXiv:2605.12762v1 Announce Type: cross Abstract: Deep super-resolution networks for precipitation downscaling achieve strong bulk skill yet systematically under-predict the heavy-tail events that dri

researcharxiv-cs-ai

Useful Memories Become Faulty When Continuously Updated by LLMs

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

AgentDisCo: Towards Disentanglement and Collaboration in Open-ended Deep Research Agents

model-releasesarxiv-cs-cl

arXiv:2605.11732v1 Announce Type: cross Abstract: In this paper, we present AgentDisCo, a novel Disentangled and Collaborative agentic architecture that formulates deep research as an adversarial opti

Google Named a Leader in the Gartner® Magic Quadrant™ for AI Application Development Platforms: Mid-cycle update

model-releasesgoogle-cloud-ai

May 2026 update: We’ve refreshed this post to reflect our mid-cycle positioning and the evolution of our platform since the report was first published last November. Last fall, Google was recognized a

M^4-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection

arXiv:2605.11760v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has emerged as a foundation model for universal segmentation. Owing to its generalizable visual representations, SAM

applicationsarxiv-cs-cv

Sparse Attention Remapping with Clustering for Efficient LLM Decoding on PIM

arXiv:2505.05772v2 Announce Type: replace Abstract: Transformer-based models are the foundation of modern machine learning, but their execution, particularly during autoregressive decoding in large la

researcharxiv-cs-cl

StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models

model-releasesarxiv-cs-cl

arXiv:2605.11483v1 Announce Type: new Abstract: While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints rem

Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol

arXiv:2605.11599v1 Announce Type: new Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior. Studies of prompt

researcharxiv-cs-lg

Dynamic Skill Lifecycle Management for Agentic Reinforcement Learning

arXiv:2605.10923v1 Announce Type: cross Abstract: Large language model agents increasingly rely on external skills to solve complex tasks, where skills act as modular units that extend their capabilit

safetyarxiv-cs-cl

Follow the Mean: Reference-Guided Flow Matching

arXiv:2605.10302v1 Announce Type: new Abstract: Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models

arXiv:2512.21815v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, as a measure of model uncertaint

Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs

arXiv:2605.06225v2 Announce Type: replace-cross Abstract: Steering large language models (LLMs) is usually done by either instruction prompting or activation steering. Prompting often gives strong con

safetyarxiv-cs-ai

MIDUS: Memory-Infused Depth Up-Scaling

arXiv:2512.13751v2 Announce Type: replace-cross Abstract: Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scalin

Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale

arXiv:2605.09790v1 Announce Type: cross Abstract: Detecting orbital anomalies, such as maneuvers, atmospheric decay, and attitude upsets, across the rapidly growing population of low-Earth-orbit (LEO)

NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation

arXiv:2605.10813v1 Announce Type: new Abstract: LLM-powered multi-agent systems can now automate the full research pipeline from ideation to paper writing, but a fundamental question remains: automati

Pay attention to this one if you build research or knowledge-work agents. Most research-agent systems produce uniform outputs regardless of …

Pay attention to this one if you build research or knowledge-work agents. Most research-agent systems produce uniform outputs regardless of who is driving them. This new work, NanoResearch, argues tha

safetydair-ai--x

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark

model-releasesarxiv-cs-ro

arXiv:2605.10921v1 Announce Type: new Abstract: Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partial

RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement

arXiv:2605.09730v1 Announce Type: new Abstract: Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structur