Search: “context”

Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction

arXiv:2606.04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscienc

local-aiarxiv-cs-ai

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CoPark: Learning Reactive Parking via Self-Play

DGX agent

arXiv:2606.04149v1 Announce Type: new Abstract: Learning a single policy that reaches a goal with high geometric precision while interacting safely with nearby agents poses conflicting objectives. Pre

model-releasesarxiv-cs-ro

4 Jun 2026

Covert Influence Between Language Models

arXiv:2606.04071v1 Announce Type: cross Abstract: As language models increasingly consume one another's outputs, covert influence -- a phenomenon where a sender's payload (the behavioral disposition i

safetyarxiv-cs-cl

Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization

arXiv:2606.04797v1 Announce Type: new Abstract: Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts. However, the

safetyarxiv-cs-cv

Customizing the Inductive Biases of Softmax Attention using Structured Matrices

arXiv:2509.07963v2 Announce Type: replace Abstract: The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product

safetyarxiv-cs-lg

Differentially Private Joint Independence Test

arXiv:2503.18721v3 Announce Type: replace-cross Abstract: Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may c

researcharxiv-cs-lg

Efficient Reasoning on the Edge

arXiv:2603.16867v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but th

local-aiarxiv-cs-cl

Emotion Entanglement and Bayesian Inference for Multi-Dimensional Emotion Understanding

arXiv:2604.00819v2 Announce Type: replace-cross Abstract: Understanding emotions in natural language is inherently a multi-dimensional reasoning problem, where multiple affective signals interact thro

Enhancing MedSAM with a Lightweight Box Predictor for Medical Image Segmentation

arXiv:2606.04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities. While foundat

local-aiarxiv-cs-ai

Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases

model-releasesarxiv-cs-cl

arXiv:2606.05112v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed as clinical agents, yet static, single-turn benchmarks cannot capture how a model dynamically del

Evaluating Reasoning Fidelity in Visual Text Generation

arXiv:2606.04479v1 Announce Type: cross Abstract: Recent text-to-image (T2I) models can render highly legible and well-structured text within images, enabling applications including document generatio

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction

arXiv:2602.23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI). Yet, assigning roles in real time remains challenging f

Exposing Blindspots: Cultural Bias Evaluation in Generative Image Models

model-releasesarxiv-cs-cv

arXiv:2510.20042v3 Announce Type: replace Abstract: Generative image models produce striking visuals yet often misrepresent culture. Prior work has examined cultural bias mainly in text-to-image (T2I)

Formal Semantics for Agentic Tool Protocols: A Process Calculus Approach

arXiv:2603.24747v2 Announce Type: replace Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for formal verification of agent protocols.

Agents

Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning

arXiv:2507.21892v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that l

agentsarxiv-cs-cl

HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning

arXiv:2606.04345v1 Announce Type: cross Abstract: This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture. Traditi

It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks

arXiv:2602.12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.

L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI

arXiv:2606.04419v1 Announce Type: cross Abstract: MRI provides excellent soft-tissue contrast without ionizing radiation, but long acquisition times increase patient discomfort while also raising exam

L^3: Large Lookup Layers

arXiv:2601.21461v3 Announce Type: replace-cross Abstract: Modern sparse language models typically achieve sparsity through Mixture-of-Experts (MoE) layers, which dynamically route tokens to dense MLP

LaVIDE: Language-Prompted Satellite Change Detection via Map-Image Alignment

arXiv:2411.19758v2 Announce Type: replace-cross Abstract: Remote sensing change detection based on a map reference and an up-to-date image boosts timely observation of the Earth's surface when earlier

Learning While Acting: A Skill-Enhanced Test-Time Co-Evolution Framework for Online Lifelong Learning Agents

arXiv:2606.04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learni

LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models

arXiv:2606.04485v1 Announce Type: new Abstract: Tabular foundation models (TFMs) increasingly rival tree ensembles, but their performance is often compute-inefficient: with standard affine scalar toke

LiSeCo: Linear Semantic Control for Language Generation

arXiv:2405.15454v4 Announce Type: replace Abstract: The prevalence of Large Language Models (LLMs) in critical applications highlights the need for controlled language generation methods that are both

researcharxiv-cs-cl

LLMs + Persona-Plug = Personalized LLMs

model-releasesarxiv-cs-cl

arXiv:2409.11901v2 Announce Type: replace Abstract: Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs

Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit

model-releasesarxiv-cs-cl

arXiv:2606.04274v1 Announce Type: new Abstract: As large language models (LLMs) become default tools for online information verification, an implicit assumption follows them: that scale and general ca

Tutorials

Mean-based algorithms: A lower bound and regret

arXiv:2606.04931v1 Announce Type: new Abstract: Mean-based algorithms are a class of online learning algorithms that assign low probability to actions with low average rewards. Recent work indicates t

tutorialsarxiv-cs-lg

MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers

arXiv:2606.04366v1 Announce Type: new Abstract: Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of l

MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation

arXiv:2606.04688v1 Announce Type: new Abstract: Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, exi

local-aiarxiv-cs-cv

MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments

arXiv:2606.04171v1 Announce Type: cross Abstract: File-type classification underlies many workflows like malware triage, forensic carving, packet inspection, and storage indexing. Learned systems such

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

arXiv:2606.04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing

Noisy memory encoding explains negative polarity illusions

arXiv:2606.04340v1 Announce Type: new Abstract: A sentence like 'The authors that no critics recommended have ever received acknowledgment for a best-selling novel' is sometimes rated as acceptable ev

researcharxiv-cs-cl

Path-conditioned training: a principled way to rescale ReLU neural networks

arXiv:2602.19799v2 Announce Type: replace-cross Abstract: Despite recent algorithmic advances, we still lack principled ways to leverage the well-documented rescaling symmetries in ReLU neural network

safetyarxiv-cs-lg

PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

arXiv:2602.01146v2 Announce Type: replace Abstract: Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the u

PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents

arXiv:2606.04780v1 Announce Type: new Abstract: Persistent LLM agents require memory representations that make the formation of person understanding explicit across long term interaction. Existing age

safetyarxiv-cs-cl

Policy Gradient for Continuous-Time Robust Markov Decision Processes

arXiv:2606.04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under w

safetyarxiv-cs-lg

PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay

arXiv:2603.23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact thei

ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection

arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable

researcharxiv-cs-cv

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606.04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typical

local-aiarxiv-cs-ai

SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding

arXiv:2606.04974v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional conte

researcharxiv-cs-cl

SAM 3D: 3Dfy Anything in Images

arXiv:2511.16624v2 Announce Type: replace-cross Abstract: We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single i

Scaling Self-Evolving Agents via Parametric Memory

arXiv:2606.04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model

Selective Coupling of Decoupled Informative Regions: Masked Attention Alignment for Data-Free Quantization of Vision Transformers

arXiv:2606.04373v1 Announce Type: cross Abstract: Data-Free Quantization (DFQ) addresses data security concerns by synthesizing samples, without accessing real data. It has garnered increasing attenti

SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning

arXiv:2606.04493v1 Announce Type: cross Abstract: Correspondence pruning aims to identify inliers from an initial set of correspondences. Most existing Graph Neural Network (GNN)-based methods rely on

SSA: Sparse Sparse Attention by Aligning Full and Sparse Attention Outputs in Feature Space

arXiv:2511.20102v3 Announce Type: replace Abstract: Sparse attention reduces the quadratic complexity of full self-attention but faces two challenges: (1) an attention gap, where applying sparse atten

safetyarxiv-cs-cl

Applications

SSSD: Simply-Scalable Speculative Decoding

arXiv:2411.05894v3 Announce Type: replace-cross Abstract: Speculative Decoding has emerged as a popular technique for accelerating inference in Large Language Models. However, most existing approaches

applicationsarxiv-cs-ai

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models

arXiv:2606.04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative mas

researcharxiv-cs-lg

Agents

Stateful Visual Encoders for Vision-Language Models

arXiv:2606.04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes. However, in

agentsarxiv-cs-cl

Structure-Aware Prediction of PROTAC-Mediated Protein Degradability via Graph Neural Networks

arXiv:2606.04021v1 Announce Type: cross Abstract: Proteolysis-targeting chimeras (PROTACs) can selectively degrade disease-causing proteins, yet predicting which targets are amenable to degradation re