Search: “context”

FCPRAG: Fusion-Controller Parametric Retrieval-Augmented Generation for Stable Multi-Passage LoRA Injection

arXiv:2608.21750v1 Announce Type: new Abstract: Parametric retrieval-augmented generation (PRAG) injects retrieved evidence into a large language model (LLM) through passage-specific LoRA adapters, re

researcharxiv-cs-cl

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Research

Following Motion for Sequential Modeling in Video Frame Interpolation

DGX agent

arXiv:2608.22861v1 Announce Type: new Abstract: State Space Models (SSMs) have surfaced as a promising architecture in Video Frame Interpolation (VFI), as they can capture long-range dependencies with

researcharxiv-cs-cv

25 Aug 2026

From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Reasoning

arXiv:2608.22566v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions,

agentsarxiv-cs-cl

From Inertia to Objectivity: Improving Deep Research Agents with Noise Isolation

arXiv:2608.23045v1 Announce Type: new Abstract: Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent ha

From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment

arXiv:2506.18919v5 Announce Type: replace-cross Abstract: As a multimodal communication medium that integrates images and text, memes often convey implicit harmful content through metaphors, satire, a

GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering

arXiv:2608.22634v1 Announce Type: cross Abstract: Current work on improving reliability in large language model (LLM)- generated answers has primarily leveraged Retrieval-Augmented Generation (RAG), k

GET: Generative Embedding Translation for Medical Image Segmentation

arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-to

researcharxiv-cs-cv

GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations

arXiv:2510.03536v3 Announce Type: replace-cross Abstract: Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multi

agentsarxiv-cs-ai

Grounding Free-Form Instructions for Fashion Complementary Image Generation

arXiv:2608.23302v1 Announce Type: new Abstract: Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural mult

safetyarxiv-cs-cv

How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network

arXiv:2608.22192v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in persistent social environments, where generated claims can be posted, replied to, remembered, and reused.

safetyarxiv-cs-cl

How Does Research Evolve? Tracing Cross-Domain Trajectories in NLP, ML, and CV Through Claim-Grounded Typed Citations

model-releasesarxiv-cs-cl

arXiv:2606.22342v2 Announce Type: replace Abstract: How does research evolve, and can we trace it at the level of individual claims? Scientific progress is not simply a uniform accumulation of facts.

InjecMEM: Memory Injection Attack on LLM Agent Memory Systems

arXiv:2608.23471v1 Announce Type: cross Abstract: Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a question:

agentsarxiv-cs-ai

Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data

arXiv:2608.23256v1 Announce Type: new Abstract: Recent work proposes next-chunk reasoning RL for leveraging no-CoT data---corpora such as worked solutions and textbook derivations that contain reasoni

Last But Not Least: Boundary Attention CalibratiON for Multimodal KV Cache Compression

arXiv:2606.14782v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) achieve strong vision-language reasoning but incur large KV caches and high decoding latency with lon

researcharxiv-cs-cl

Learning to Look Again: Loss-Gap Supervision for Free-form Crop Routing in Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2608.21762v1 Announce Type: cross Abstract: Vision-language models (VLMs) fail many detail-centric questions for a concrete reason: the answer is visible in the image, yet lost after the image i

Lightweight Multi-scale Hierarchical Anomaly Detection and Localization for Geospatial Big Data Applications at the Edge

arXiv:2608.22648v1 Announce Type: cross Abstract: As an increasing number of critical applications, including environmental, emergency, meteorological, and agricultural, rely on real-time anomaly dete

researcharxiv-cs-lg

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tool

Ludi{}_{scriptscriptstyle 0.1}: An Agentic System for Socially Intelligent Robots

arXiv:2608.22035v1 Announce Type: new Abstract: Robot foundation models have substantially advanced perception and control, but natural human-robot collaboration requires more than executing isolated

agentsarxiv-cs-ro

MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning

arXiv:2608.22167v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs), but most existing RL frameworks

MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters

arXiv:2608.23473v1 Announce Type: cross Abstract: Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constra

agentsarxiv-cs-ai

Mol-JEPA: A multimodal Joint Embedding Predictive Architecture for Molecules

arXiv:2608.22642v1 Announce Type: cross Abstract: Despite recent advances in molecular foundation models, several limitations remain, such as chemically invalid augmentations, modality collapse, and i

Mover360: Controllable Object Manipulation in 360{eg} Panoramic Images

model-releasesarxiv-cs-cv

arXiv:2608.23238v1 Announce Type: new Abstract: We present Mover360, a controllable object manipulation framework for 360{eg} images. Unlike perspective images, 360{eg} images in equirectangular proje

MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents

arXiv:2608.21810v1 Announce Type: cross Abstract: Clinical decision-making is inherently experience-driven: physicians progressively refine their reasoning by synthesizing patient history, multimodal

agentsarxiv-cs-cv

Multi-Scale Fruit Capsules: Dilated Convolutions and Dynamic Routing for In-the-Wild Explainable Fruit Recognition

model-releasesarxiv-cs-cv

arXiv:2608.21454v1 Announce Type: new Abstract: The same fruit appears in a bunch, unpicked, peeled, bagged in plastic, or sliced on a dish, so automated fruit classification in the wild (AFCW) must a

On the Role of Citations in Preference Data

arXiv:2608.21376v1 Announce Type: cross Abstract: Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against m

Optimizer choice matters for the emergence of Neural Collapse

arXiv:2602.16642v4 Announce Type: replace Abstract: Neural Collapse (NC) refers to the emergence of highly symmetric geometric structures in the representations of deep neural networks during the term

researcharxiv-cs-lg

ORBIT++: Benchmarking SfM in the Wild with 360{eg} Video

model-releasesarxiv-cs-cv

arXiv:2608.22039v1 Announce Type: new Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging camer

Pointing-VLA: Typed Spatial Grounding Interfaces for Vision-Language-Action Manipulation

arXiv:2608.23138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models often expose spatial grounding through autoregressive text coordinates or opaque action tokens, creating brittle i

Proxy reliance in large language model decisions is uncalibrated to predictive evidence

arXiv:2608.22887v1 Announce Type: new Abstract: Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy

Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification

arXiv:2608.21874v1 Announce Type: cross Abstract: Deep learning has achieved strong performance in encrypted traffic classification (ETC), yet its computational cost limits deployment on resource-cons

RAD: Rule-Augmented Relational Anomaly Detection

model-releasesarxiv-cs-lg

arXiv:2608.23468v1 Announce Type: new Abstract: Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single fe

Reliability- and Anatomy-Consistency-Aware Multimodal Learning for Robust Fracture Classification from Bangladeshi Radiographs

arXiv:2608.21482v1 Announce Type: cross Abstract: Background: Multimodal fracture classifiers may benefit from patient and anatomical metadata, but they can also become brittle when contextual informa

Applications

Resilient Concurrent Causal Discovery for Topological Event Sequences

arXiv:2608.21815v1 Announce Type: new Abstract: Causal discovery on topological event sequences is crucial for ensuring the reliability of networks. However, existing methods struggle to capture the c

applicationsarxiv-cs-lg

Applications

RoboShape: Information-Theoretic Point Cloud Representations for Privacy-Aware Robot Perception

arXiv:2608.21380v1 Announce Type: cross Abstract: With the increased adoption of robotic agents operating in human environments by scanning and sharing 3D representations (e.g., for fleet learning, cl

applicationsarxiv-cs-ai

Robustness Analysis of Agentic AI to Inconsistent and Incomplete Tool Responses

arXiv:2608.22676v1 Announce Type: new Abstract: Robustness to a bad tool return means answering it in the way that return calls for, which depends on how the tool went wrong. A tool that has failed an

Applications

Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models

arXiv:2608.22597v1 Announce Type: cross Abstract: Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect es

applicationsarxiv-cs-lg

SchemaRouter: Field-Aware Tool Routing for Efficient Heterogeneous Agentic RAG

arXiv:2608.21375v1 Announce Type: new Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph

Semantic Compression Trees: Multi-Resolution Knowledge Retrieval via Hierarchical Semantic Residuals

arXiv:2608.21610v1 Announce Type: new Abstract: Retrieval-augmented generation relies mostly on flat, fixed-granularity indexes: documents are cut into uniform chunks and retrieved by similarity, disc

Semantic Slots for Video Object-Centric Learning

arXiv:2608.21636v1 Announce Type: new Abstract: Video Object-Centric Learning (OCL) has traditionally focused on refining the encoder architecture to ensure temporal consistency. In this paper, we arg

researcharxiv-cs-cv

SlideGen: Collaborative Multimodal Agents for Scientific Slide Generation

arXiv:2512.04529v3 Announce Type: replace Abstract: Creating presentation slides from scientific papers is not simply a matter of summarizing paragraphs. A presenter is required to decide what story t

Software Frameworks for Explainable AI in Time Series Classification: A Systematic Review

arXiv:2608.21449v1 Announce Type: new Abstract: Time series arise in a wide range of application domains and are analyzed using machine learning in decision-critical settings. Time series classificati

Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?

arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent,

Tabular foundation models for non-tabular tasks

arXiv:2608.22594v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize

researcharxiv-cs-lg

Tutorials

Teaching LLMs How ICU Physicians Approach Clinical Reasoning Through OMOP-Aligned Retrieval Improves Reasoning Across Clinical Domains

arXiv:2608.22622v1 Announce Type: cross Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficult

tutorialsarxiv-cs-ai

TELEVAL: A Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios

arXiv:2507.18061v4 Announce Type: replace-cross Abstract: Spoken Language Models (SLMs) are expected to support natural spoken interaction beyond task completion. However, existing SLM benchmarks prim

Hardware

The Communication Map of a Transformer

arXiv:2608.22007v1 Announce Type: cross Abstract: The components of a transformer communicate by writing to and reading from a shared residual stream, and mechanistic interpretability has mapped these

hardwarearxiv-cs-cl

To What Extent Does the Perceived Obesity Level of Humanoid Robots Affect People's Trust in Them?

arXiv:2411.06039v2 Announce Type: replace Abstract: Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field

researcharxiv-cs-ro

Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models

arXiv:2501.13976v2 Announce Type: replace-cross Abstract: The prevalence of harmful content on social media platforms poses significant risks to users and society, necessitating more effective and sca