Search: “context”

TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data

arXiv:2605.08111v1 Announce Type: cross Abstract: The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust c

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Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views

DGX agent

arXiv:2511.12878v4 Announce Type: replace Abstract: Forecasting how human hands move in egocentric views is critical for applications like augmented reality and human-robot policy transfer. Recently,

safetyarxiv-cs-cv

12 May 2026

Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2605.10445v1 Announce Type: new Abstract: Unified Multimodal Models (UMMs) excel in general tasks but struggle to bridge the gap between personalized understanding and generation. Prior works la

Uniform Inductive Spatio-Temporal Kriging

arXiv:2603.05301v2 Announce Type: replace Abstract: Inductive spatio-temporal kriging infers signals at unobserved locations from observed sensors, but real-world observations are often incomplete and

Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning

arXiv:2605.08765v1 Announce Type: cross Abstract: Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing metho

Agents

WebTrap: Stealthy Mid-Task Hijacking of Browser Agents During Navigation

arXiv:2605.08310v1 Announce Type: cross Abstract: Browser agents are increasingly deployed in long-horizon tasks, which require executing extended action chains to accomplish user goals. However, this

agentsarxiv-cs-ai

When Agents Overtrust Environmental Evidence: An Extensible Agentic Framework for Benchmarking Evidence-Grounding Defects in LLM Agents

arXiv:2605.08828v1 Announce Type: new Abstract: Large language model agents increasingly operate through environment-facing scaffolds that expose files, web pages, APIs, and logs. These observations i

When More Parameters Hurt: Foundation Model Priors Amplify Worst-Client Disparity Under Extreme Federated Heterogeneity

arXiv:2605.08992v1 Announce Type: new Abstract: Federated learning (FL) is increasingly used to fine-tune foundation models (FMs) on distributed private data. The community largely assumes that large-

safetyarxiv-cs-lg

When Prompts Become Payloads: A Framework for Mitigating SQL Injection Attacks in Large Language Model-Driven Applications

arXiv:2605.10176v1 Announce Type: cross Abstract: Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that ena

When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews

arXiv:2605.10171v1 Announce Type: cross Abstract: Scientific peer reviews frequently contain conflicting expert judgments, and the increasing scale of conference submissions makes it challenging for A

When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation

arXiv:2512.08875v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice,

Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish

arXiv:2605.10714v1 Announce Type: cross Abstract: Cross-lingual transfer has become a central paradigm for extending natural language processing (NLP) technologies to low-resource languages. By levera

A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset

arXiv:2509.12047v2 Announce Type: replace-cross Abstract: Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. Howev

Agents

A Multi-Memory Segment System for Generating High-Quality Long-Term Memory Content in Agents

arXiv:2508.15294v4 Announce Type: replace Abstract: In the current field of agent memory, extensive explorations have been conducted in the area of memory retrieval, yet few studies have focused on ex

agentsarxiv-cs-ai

A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion

Activation Differences Reveal Backdoors: A Comparison of SAE Architectures

arXiv:2605.07324v1 Announce Type: cross Abstract: Backdoor attacks on language models pose a significant threat to AI safety, where models behave normally on most inputs but exhibit harmful behavior w

Agentic Coding Needs Proactivity, Not Just Autonomy

arXiv:2605.06717v1 Announce Type: cross Abstract: Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories,

An abstract effective convergence theorem for stochastic processes, with applications to stochastic approximation

arXiv:2504.12922v3 Announce Type: replace-cross Abstract: We provide a general theorem on the asymptotic behavior of stochastic processes that conform to a relaxed supermartingale condition. The disti

Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?

model-releasesarxiv-cs-cl

arXiv:2605.07937v1 Announce Type: new Abstract: Long-horizon AI agents execute complex workflows spanning hundreds of sequential actions, yet a single wrong assumption early on can cascade into irreve

Automated Evaluation can Distinguish the Good and Bad AI Responses to Patient Questions about Hospitalization

arXiv:2510.00436v2 Announce Type: replace Abstract: Automated approaches to answer patient-posed health questions are rising, but selecting among systems requires reliable evaluation. The current gold

Black-box model classification under the discriminative factorization

arXiv:2605.07878v1 Announce Type: new Abstract: Access to modern generative systems is often restricted to querying an API (the ``black-box' setting) and many properties of the system are unknown to t

Bridging Textual Profiles and Latent User Embeddings for Personalization

arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall

researcharxiv-cs-cl

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

arXiv:2605.08057v1 Announce Type: cross Abstract: While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform wel

Tutorials

Characterizing and Correcting Effective Target Shift in Online Learning

arXiv:2605.07886v1 Announce Type: cross Abstract: Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, espec

tutorialsarxiv-cs-lg

CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs

arXiv:2605.06115v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs), trained primarily on English-centric data, frequently generate culturally inappropriate or misaligned resp

Edge Detection for Organ Boundaries via Top Down Refinement and SubPixel Upsampling

arXiv:2508.06805v2 Announce Type: replace Abstract: Accurate localization of organ boundaries is critical in medical imaging for segmentation, registration, surgical planning, and radiotherapy. While

EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting

arXiv:2605.07642v1 Announce Type: new Abstract: Forecasting future 3D hand pose sequences from egocentric video is essential for understanding human intention and enabling embodied applications such a

EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams

arXiv:2605.07299v1 Announce Type: cross Abstract: Existing Multimodal Large Language Models (MLLMs) remain primarily reactive, failing to continuously perceive environments or proactively assist users

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems

arXiv:2506.04565v2 Announce Type: replace-cross Abstract: Compound AI Systems (CAIS) are an emerging paradigm that integrates large language models (LLMs) with external components, including retriever

researcharxiv-cs-cl

Local Ai

Future Validity is the Missing Statistic: From Impossibility to Phi-Estimation for Grammar-Faithful Speculative Decoding

arXiv:2605.07698v1 Announce Type: new Abstract: Grammar-constrained generation is often combined with local vocabulary masking and speculative decoding, but the resulting sampling law is not the gramm

local-aiarxiv-cs-lg

GraphFusion3D: Dynamic Graph Attention Convolution with Adaptive Cross-Modal Transformer for 3D Object Detection

arXiv:2512.02991v2 Announce Type: replace Abstract: Despite significant progress in 3D object detection, point clouds remain challenging due to sparse data, incomplete structures, and limited semantic

Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs

model-releasesarxiv-cs-lg

arXiv:2605.05957v2 Announce Type: replace Abstract: LLMs reliably correct false claims when presented in isolation, yet when the same claims are embedded in task-oriented requests, they often comply r

Agents

Learning CLI Agents with Structured Action Credit under Selective Observation

arXiv:2605.08013v1 Announce Type: new Abstract: Command line interface (CLI) agents are emerging as a practical paradigm for agent-computer interaction over evolving filesystems, executable command li

agentsarxiv-cs-ai

Lightweight Unpaired Smartphone ISP Transfer with Semantic Pseudo-Pairing

arXiv:2605.07495v1 Announce Type: new Abstract: Unpaired smartphone ISP is a challenging problem due to the lack of scene and color alignment between RAW and target RGB images. Many existing methods e

safetyarxiv-cs-cv

Lossy Common Information in a Learnable Gray-Wyner Network

arXiv:2601.21424v3 Announce Type: replace-cross Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and in

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)

Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views

arXiv:2605.07550v1 Announce Type: new Abstract: 3D vision systems are fundamentally constrained by their reliance on visual overlap: reconstruction methods require it for geometric alignment, while ge

safetyarxiv-cs-cv

Mitigating Cognitive Bias in RLHF by Altering Rationality

arXiv:2605.06895v1 Announce Type: new Abstract: How can we make models robust to even imperfect human feedback? In reinforcement learning from human feedback (RLHF), human preferences over model outpu

Multimodal Latent Reasoning via Hierarchical Visual Cues Injection

arXiv:2602.05359v2 Announce Type: replace Abstract: The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often r

Neural Neural Scaling Laws

model-releasesarxiv-cs-cl

arXiv:2601.19831v2 Announce Type: replace-cross Abstract: Neural scaling laws predict how language model performance improves with increased training inputs. While aggregate metrics like validation lo

Tutorials

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

Local Ai

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

arXiv:2605.07860v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT)

local-aiarxiv-cs-ai

Tutorials

Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network

arXiv:2602.03490v2 Announce Type: replace Abstract: Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such w

tutorialsarxiv-cs-lg

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

Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation

arXiv:2605.07765v1 Announce Type: new Abstract: In this work, we study TabPFN as a training-free, modular summary network for simulation-based Bayesian inference (SBI). Tabular foundation models such

PRIMED: Adaptive Modality Suppression for Referring Audio-Visual Segmentation via Biased Competition

model-releasesarxiv-cs-cv

arXiv:2605.07154v1 Announce Type: new Abstract: Referring Audio-Visual Segmentation (Ref-AVS) seeks to localize and segment target objects in video frames based on visual, auditory, and textual referr

Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning

arXiv:2605.07804v1 Announce Type: cross Abstract: On-policy distillation (OPD) leverages dense teacher rewards to enhance reasoning models. However, scaling OPD to long-horizon tasks exposes a critica