Search: “framework”

HAP: Head-Adaptive Visual Token Pruning via Cross-Modal Alignment

arXiv:2608.23921v1 Announce Type: new Abstract: Recent Vision-Language Models encode high-resolution images into long visual token sequences, incurring prohibitive prefill costs. To compress them, exi

safetyarxiv-cs-cv

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HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA

DGX agent

arXiv:2402.01767v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) significantly improves document-based question answering by integrating external documents during generat

model-releasesarxiv-cs-ai

26 Aug 2026

Human-Inspired Social Engagement Analysis via Interpretable Mutual Visual Attention

arXiv:2608.24580v1 Announce Type: new Abstract: Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable co

InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis

arXiv:2608.23574v1 Announce Type: cross Abstract: Each WSI slide contains thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction s

Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features

arXiv:2608.24723v1 Announce Type: new Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent rep

Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

arXiv:2608.24087v1 Announce Type: cross Abstract: Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and

Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency

arXiv:2608.23831v1 Announce Type: cross Abstract: While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist

safetyarxiv-cs-lg

LLM Agents Perform Controlled Experiments Using Simulation Models

arXiv:2608.23622v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more

Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks

model-releasesarxiv-cs-lg

arXiv:2505.11239v4 Announce Type: replace Abstract: Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning,

Meta^n: Recursive Self-Improvement through Emergent Depth

arXiv:2608.24735v1 Announce Type: new Abstract: Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those th

MetaRAG: Belief-Action Aligned Policy Optimization for Agentic RAG

arXiv:2608.24214v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) requires language models to decide when to continue searching and when to answer. Existing RL-based methods

Mind the Student: Behavioral and Contextual Cues for Automated Engagement Prediction in Online Learning

arXiv:2608.24340v1 Announce Type: cross Abstract: The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distin

MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences

arXiv:2608.23936v1 Announce Type: new Abstract: We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly 40K rs-fMR

researcharxiv-cs-lg

Model Effect or Label Effect? Refined Annotations and a Human-Referenced Benchmark for Pulmonary Embolism Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.24486v1 Announce Type: cross Abstract: Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes

More Rejective, Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight

arXiv:2608.23941v1 Announce Type: new Abstract: Pre-execution oversight is core to trusted monitoring in AI control: a fallible LLM monitor vets planned actions before irreversible execution. Over-blo

Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLMs

arXiv:2608.23897v1 Announce Type: cross Abstract: When a code generating language model fabricates a Python package name, an adversary who has pre-registered that name on PyPI can convert that halluci

Native-Space 3D CarveMix for Multi-Site T1w Stroke Segmentation

arXiv:2608.23882v1 Announce Type: cross Abstract: Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is diff

OmniJudge or OmniBias? Diagnosing Multimodal Judges through Balanced, Decoupled Lenses

arXiv:2608.24160v1 Announce Type: new Abstract: Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly use

Opponent Aware Reinforcement Learning

arXiv:1908.08773v3 Announce Type: replace Abstract: In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit.

agentsarxiv-cs-lg

PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents

arXiv:2608.24509v1 Announce Type: new Abstract: LLM agents increasingly solve tasks by invoking multiple tools, where parallel execution is essential for low latency but difficult to manage safely. Ex

PlaceSeek: Human-Centered Geospatial Retrieval of Urban Outdoor Places via Semantic Grounding and Affective Alignment

arXiv:2608.24133v1 Announce Type: cross Abstract: People search for urban outdoor places not only by category or function, but also by what activities a place can support and how it is perceived. Exis

Quantifying System-Level Harms from AI Adoption in Complex Sociotechnical Systems

arXiv:2608.23906v1 Announce Type: new Abstract: Artificial Intelligence (AI) is increasingly integrated into complex sociotechnical systems, including Critical National Infrastructure (CNI), where har

RAGSentinel: Certifiable Geometric Consensus for Robust Retrieval-Augmented Generation

arXiv:2608.23965v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also expose

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

arXiv:2608.24876v1 Announce Type: new Abstract: Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We in

Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling

arXiv:2608.24470v1 Announce Type: new Abstract: Heterogeneous agile Earth observation satellite (AEOS) scheduling requires task selection, satellite assignment, and observation sequencing under satell

Restoring Without Forgetting: Continual Learning Across Image Degradations

arXiv:2608.23799v1 Announce Type: cross Abstract: Recent progress in image restoration has converged on all-in-one architectures that jointly handle multiple degradations within a single network. Thes

Retrieval-augmented generation vs. deterministic tax computation in multi-agent financial advisory: A 2x2 factorial experiment

arXiv:2608.23908v1 Announce Type: new Abstract: Tax-loss harvesting demonstrates consistent benefits to long-term portfolio growth; yet implementing it efficiently often involves complex consideration

agentsarxiv-cs-ai

Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping

arXiv:2608.24135v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a pivotal technique for enhancing the code generation capabilities of Large Languag

Robust Slip Detection and Material Classification via Spatiotemporal Transformers on a Uniformly-Illuminated Visuo-Tactile Sensor

arXiv:2608.24162v1 Announce Type: new Abstract: Tactile sensing is central to robotic manipulation, among which slip detection stands out as a quintessential and critical task. However, existing slip

researcharxiv-cs-ro

ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection

arXiv:2608.23923v1 Announce Type: new Abstract: Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles.

safetyarxiv-cs-cv

SA-Bench: Evaluating Semantic Alignment in LLM-Based Paper Reproduction

arXiv:2608.24252v1 Announce Type: new Abstract: LLM agents can generate paper reproduction code, yet often produce scientifically unfaithful implementations. We define this failure mode as semantic dr

Hardware

SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

arXiv:2608.24516v1 Announce Type: cross Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereb

hardwarearxiv-cs-lg

Selective Regenerative Decoding: Trajectory-Level Intervention for Inference-Time Reasoning

arXiv:2608.24338v1 Announce Type: new Abstract: Inference-time decoding methods improve LLM reasoning by exploring multiple candidate trajectories, yet treat each trajectory as atomic: either retainin

Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

arXiv:2608.23879v1 Announce Type: cross Abstract: Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying

StateTune: Transforming LLM-Assisted EDA Flow Tuning into a Stateful, Closed-Loop Process

model-releasesarxiv-cs-lg

arXiv:2608.23601v1 Announce Type: cross Abstract: EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohib

StepGuard: Learning Step-Level Guardrails with Scalable Supervision and Safety-Utility Balancing

arXiv:2608.24777v1 Announce Type: new Abstract: LLM-based agents can interact with external environments through tool invocation, but this capability also introduces security risks such as file modifi

Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection

arXiv:2608.24113v1 Announce Type: cross Abstract: Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits

model-releasesarxiv-cs-lg

arXiv:2605.22330v1 Announce Type: cross Abstract: Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide gui

SyPS: Measuring Sycophancy Prompt Sensitivity in Large Language Models

arXiv:2608.23837v1 Announce Type: new Abstract: Large language models (LLMs) are known to exhibit social sycophancy, often validating or agreeing with users in socially sensitive contexts. Existing ev

The Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering, Probability Placement, and the Attribution Problem in Deployed Language Models

arXiv:2608.24662v1 Announce Type: new Abstract: Large language models (LLMs) are commonly evaluated under the assumption that their observable behavior is primarily determined by model weights, traini

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library

arXiv:2512.16715v3 Announce Type: replace-cross Abstract: In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the fu

TrustShiftProbe: Characterizing, Benchmarking, and Defending Staged Trust Attacks on MCP Servers

arXiv:2608.23763v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as the standard layer connecting Large Language Model agents to external tool backends. This openness int

agentsarxiv-cs-ai

VIP: Variation-based Iterative-learning Planning for Robotic Navigation

arXiv:2608.24618v1 Announce Type: new Abstract: Over the past decade, autonomous robotic systems have been increasingly deployed in applications such as surveying, search and rescue, and last-mile del

agentsarxiv-cs-ro

A Data-Driven Approach to State Construction in Markov Models

arXiv:2608.21480v1 Announce Type: cross Abstract: A Markov chain is a widely used stochastic process modelling random events over time. These models are built on subsets of the entire dataset, referre

researcharxiv-cs-lg

Action-Aligned Retrieval with Pairwise Multimodal Reranking for Text-Based Person Anomaly Search

arXiv:2608.23503v1 Announce Type: new Abstract: Text-based person anomaly search requires distinguishing individuals based on fine-grained, context-dependent behaviors rather than mere appearance. Exi

Adaptive Multilevel Twisted Sequential Monte Carlo for Rare Events Estimation in Language Models

arXiv:2608.21736v1 Announce Type: new Abstract: Rare unsafe behaviors in large language models can remain practically significant even when their probability is extremely small, particularly at deploy

safetyarxiv-cs-lg

AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation

arXiv:2608.21926v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) navigation in modern low-altitude environments requires more accurate pose alignment in the final approach stage for targe

safetyarxiv-cs-cv

An offline approach to fNIRS-guided reinforcement learning for robot behavior

arXiv:2607.14393v2 Announce Type: replace-cross Abstract: Human-in-the-loop Reinforcement Learning has become a popular approach for training, finetuning, and aligning robot behavior with user prefere