Search: “models”

Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation

arXiv:2510.07926v2 Announce Type: replace Abstract: Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce o

researcharxiv-cs-cl

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Local Ai

DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection

DGX agent

arXiv:2604.02753v2 Announce Type: replace Abstract: Open-vocabulary Object Detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in

local-aiarxiv-cs-cv

11 May 2026

Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

arXiv:2605.07940v1 Announce Type: new Abstract: Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pai

tutorialsarxiv-cs-cv

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation

arXiv:2605.07694v1 Announce Type: cross Abstract: Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which com

researcharxiv-cs-ai

Differentially Private Auditing Under Strategic Response

arXiv:2605.07674v1 Announce Type: cross Abstract: Regulatory audits of AI systems increasingly rely on differential privacy (DP) to protect training data and model internals. We study audit design whe

safetyarxiv-cs-lg

DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentation

arXiv:2605.07221v1 Announce Type: new Abstract: Adapting foundation models to medical segmentation typically requires either backbone fine-tuning or high-capacity task-specific decoders, both of which

Does Your Neural Network Extrapolate? Feature Engineering as Identifiability Bias for OOD Generalization

arXiv:2605.07483v1 Announce Type: cross Abstract: Successful deep neural networks discover salient features of data. We show when and why they fail to learn out-of-distribution (OOD)-relevant represen

Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow

arXiv:2605.07727v1 Announce Type: cross Abstract: We propose Drifting Field Policy (DFP), a non-ODE one-step generative policy built on the drifting model paradigm. We frame the policy update as a rev

DVD: Discrete Voxel Diffusion for 3D Generation and Editing

arXiv:2605.07971v1 Announce Type: new Abstract: We introduce Discrete Voxel Diffusion (DVD), a discrete diffusion framework to generate, assess, and edit sparse voxels for SLat (Structured LATent) bas

Entropy-Regularized Adjoint Matching for Offline Reinforcement Learning

arXiv:2605.06156v2 Announce Type: replace-cross Abstract: Integrating expressive generative policies, such as flow-matching models, into offline reinforcement learning (RL) allows agents to capture co

Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation

arXiv:2605.07381v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to brid

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

arXiv:2605.07097v1 Announce Type: cross Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed fin

tutorialsarxiv-cs-lg

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

arXiv:2510.16079v2 Announce Type: replace-cross Abstract: Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from th

Exposing and Mitigating Temporal Attack in Deepfake Video Detection

arXiv:2605.07398v1 Announce Type: cross Abstract: While spatiotemporal deepfake detectors achieve high AUC, our experiments reveal their susceptibility to evasion attacks. These models tend to overfit

researcharxiv-cs-ai

f-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses

arXiv:2605.06977v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large language models. While most existing appr

From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes

arXiv:2605.06684v1 Announce Type: new Abstract: Tree-involved crashes represent a critical subset of run-off-road (ROR) collisions, often resulting in fatal or severe injuries due to high-energy impac

From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms

arXiv:2605.06716v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. Whi

From Time Series Analysis to Question Answering: A Survey in the LLM Era

arXiv:2506.11512v2 Announce Type: replace-cross Abstract: Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities

Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension

arXiv:2510.01105v2 Announce Type: replace Abstract: Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned represen

applicationsarxiv-cs-lg

GRAPE: Let GRPO Supervise Query Rewriting by Ranking for Retrieval

arXiv:2509.23370v2 Announce Type: replace Abstract: The CLIP model has established itself as a cornerstone of large-scale retrieval systems. However, its performance often degrades under distributiona

safetyarxiv-cs-cv

GraphDC: A Divide-and-Conquer Multi-Agent System for Scalable Graph Algorithm Reasoning

arXiv:2605.06671v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong potential for many mathematical problems. However, their performance on graph algorithmic tasks is

GRaSp: Automatic Example Optimization for In-Context Learning in Low-Data Tasks

arXiv:2605.07454v1 Announce Type: new Abstract: In-context learning enables large language models to adapt to new tasks, but their performance is highly sensitive to the selected examples. Finding eff

researcharxiv-cs-cl

Tools

Here's the full TIL https://til.simonwillison.net/llms/llm-shebang

Simon Willison shares a technique for using Large Language Models directly from the command line using a shebang (#!) syntax, allowing scripts to be executed with LLM processing without explicit comma

toolssimon-willison--x

Local Ai

HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization

arXiv:2605.07214v1 Announce Type: new Abstract: Large Language Models have recently emerged as a promising paradigm for automated heuristic design for NP-hard combinatorial optimization problems. Desp

local-aiarxiv-cs-ai

How to Compress KV Cache in RL Post-Training? Shadow Mask Distillation for Memory-Efficient Alignment

arXiv:2605.06850v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a crucial paradigm for unlocking the advanced reasoning capabilities of Large Language Models (LLMs), encom

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training

arXiv:2605.07316v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning

InsHuman: Towards Natural and Identity-Preserving Human Insertion

arXiv:2605.07402v1 Announce Type: new Abstract: Human insertion aims to naturally place specific individuals into a target background. Although existing image editing models may have such ability, the

InterCoG: Towards Spatially Precise Image Editing with Interleaved Chain-of-Grounding Reasoning

arXiv:2603.01586v3 Announce Type: replace Abstract: Emerging unified editing models have demonstrated strong capabilities in general object editing tasks. However, it remains a significant challenge t

safetyarxiv-cs-cv

KL for a KL: On-Policy Distillation with Control Variate Baseline

arXiv:2605.07865v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OP

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting

arXiv:2602.02832v3 Announce Type: replace Abstract: Forecasting physical systems over long horizons from irregularly sampled observations demands models that are stable, computationally efficient, and

LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

arXiv:2605.06676v1 Announce Type: cross Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compress

tutorialsarxiv-cs-cl

LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments

arXiv:2605.06839v1 Announce Type: cross Abstract: Autonomous experimentation has transformed microscopy and materials discovery by enabling closed-loop optimization including imaging and spectroscopy

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

arXiv:2605.07723v1 Announce Type: cross Abstract: Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and c

applicationsarxiv-cs-ai

Local Ai

LoHGNet: Infrared Small Target Detection through Lorentz Geometric Encoding with High-Order Relation Learning

arXiv:2605.07213v1 Announce Type: new Abstract: Infrared small target detection (IRSTD) remains challenging due to the scarcity of useful target cues and the presence of severe background clutter. Mos

local-aiarxiv-cs-cv

MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference

arXiv:2605.05225v2 Announce Type: replace-cross Abstract: Mixture-of-Experts Multimodal Large Language Models (MoE MLLMs) suffer from a significant efficiency bottleneck during Expert Parallelism (EP)

researcharxiv-cs-ai

MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems

arXiv:2605.06623v1 Announce Type: cross Abstract: Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orch

agentsarxiv-cs-lg

MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments

arXiv:2605.07058v1 Announce Type: cross Abstract: Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and co

MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes

arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. W

applicationsarxiv-cs-ai

MoCoTalk: Multi-Conditional Diffusion with Adaptive Router for Controllable Talking Head Generation

arXiv:2605.08050v1 Announce Type: new Abstract: Talking-head generation requires joint modeling of identity, head pose, facial expression, and mouth dynamics. Existing methods typically address only a

safetyarxiv-cs-cv

Not All Tokens Learn Alike: Attention Entropy Reveals Heterogeneous Signals in RL Reasoning

arXiv:2605.07660v1 Announce Type: new Abstract: Reinforcement-learning-based post-training has become a key approach for improving the reasoning ability of large language models, but its token-level l

tutorialsarxiv-cs-cl

Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation

arXiv:2605.06892v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have achieved state-of-the-art video generation quality, but they incur immense computational cost because standard infere

OASES: Outcome-Aligned Search-Evaluation Co-Training for Agentic Search

arXiv:2604.03675v2 Announce Type: replace Abstract: Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforceme

Industry

On theCUBE Pod: IBM Think goes AI first and Musk buries the hatchet with Anthropic

IBM Think saw Big Blue grab its place in the artificial intelligence spotlight. In a week full of notable, and surprising, AI news, IBM Think saw CEO Arvind Krishna emphasize AI as the operating model

industrysiliconangle

OneViewAll: Semantic Prior Guided One-View 6D Pose Estimation for Novel Objects

arXiv:2605.07023v1 Announce Type: new Abstract: In many practical 6D object pose estimation scenarios, we often have access to only a single real-world RGB-D reference view per object, typically witho

applicationsarxiv-cs-cv

Online Allocation with Unknown Shared Supply

arXiv:2605.07080v1 Announce Type: new Abstract: Many real-world resource allocation systems, such as humanitarian logistics and vaccine distribution, must preposition limited supply across multiple lo

Online Localized Conformal Prediction

arXiv:2605.05497v2 Announce Type: replace Abstract: Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online

Physics-Based Benchmarking Metrics for Multimodal Synthetic Images

arXiv:2511.15204v3 Announce Type: replace-cross Abstract: Current state of the art measures like BLEU, CIDEr, VQA score, SigLIP-2 and CLIPScore are often unable to capture semantic or structural accur

Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

arXiv:2605.07738v1 Announce Type: cross Abstract: Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simul