Search: “evaluation”

An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer

arXiv:2608.09142v1 Announce Type: new Abstract: Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure gui

safetyarxiv-cs-cl

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An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism

DGX agent

arXiv:2607.15511v2 Announce Type: replace-cross Abstract: Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because o

researcharxiv-cs-ai

11 Aug 2026

Tutorials

Anatomically Consistent Cross-Contrast Super-Resolution of Anisotropic Brain T2w MRI

arXiv:2608.08401v1 Announce Type: new Abstract: T2-weighted (T2w) brain MRI provides fluid-sensitive soft-tissue contrast that is important for neuro-oncology and radiotherapy planning. However, T2w s

tutorialsarxiv-cs-cv

AnchorFold: A Focus-Then-Fold Framework via Recursive Attention Propagation for Efficient Multi-Vector Visual Document Retrieval

arXiv:2608.08732v1 Announce Type: cross Abstract: Multi-vector vision-language retrievers enable fine-grained Visual Document Retrieval (VDR) through late interaction, but storing and scoring hundreds

researcharxiv-cs-cl

AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions

model-releasesarxiv-cs-cv

arXiv:2506.17455v3 Announce Type: replace Abstract: Robust visual recognition in underwater environments remains a significant challenge due to complex distortions such as turbidity, low illumination,

ArchAgent v2: A Case Study with the Data Prefetching Championship

arXiv:2608.09874v1 Announce Type: new Abstract: Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture dis

Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models

arXiv:2608.08086v1 Announce Type: new Abstract: Diffusion language models (DLMs) iteratively refine a sequence, allowing earlier predictions to be revised as context evolves. This rollback capability

researcharxiv-cs-cl

Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory

arXiv:2406.14373v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science

ATLAS: Agentic Taxonomy of Large-Scale Software Ecosystems

model-releasesarxiv-cs-cl

arXiv:2606.21597v2 Announce Type: replace-cross Abstract: The open-source ecosystem on GitHub lacks a systematic hierarchical taxonomy of software repositories. GitHub Topics, the dominant organizatio

Automated Generation of Complexity-Validated Decision Scenarios Using Large Language Models

arXiv:2608.08822v1 Announce Type: new Abstract: Cognitive decision-making research depends on diverse scenarios with carefully controlled complexity, yet manual production is slow, inconsistent, and b

Automating Deception: Scalable Multi-Turn LLM Jailbreaks

arXiv:2511.19517v3 Announce Type: replace-cross Abstract: Multi-turn conversational attacks, which leverage psychological principles like Foot-in-the-Door (FITD), where a small initial request paves t

AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts

arXiv:2601.22758v2 Announce Type: replace Abstract: Large language model agents repeatedly encounter related tasks, yet systems that learn from trajectories commit every lesson to one predefined artif

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.08191v1 Announce Type: new Abstract: Multi-organ ultrasound segmentation remains challenging when anatomically adjacent structures must be delineated jointly, as localized boundary errors c

Benchmarking In-context Experiential Learning Through Repeated Product Recommendations

arXiv:2511.22130v2 Announce Type: replace Abstract: To navigate ever-shifting real-world environments, agents must grapple with incomplete knowledge and adapt their strategies through experience. Howe

Benchmarking the Robustness of Agentic Systems to Adversarially-Induced Harms

arXiv:2508.16481v3 Announce Type: replace Abstract: Ensuring the safe use of agentic systems requires a thorough understanding of the range of malicious behaviors these systems may exhibit. In this pa

Beyond cognacy

arXiv:2507.03005v3 Announce Type: replace Abstract: Computational phylogenetics has become an established tool in historical linguistics, with many language families now analyzed using likelihood-base

safetyarxiv-cs-cl

Beyond Pixels: Exploring DOM Downsampling for LLM-Based Web Agents

arXiv:2508.04412v3 Announce Type: replace Abstract: The advent of large language models (LLMs) has sparked an evolution of autonomous web browsing agents: given a web browsing task and serialised user

Beyond Routing: Decoupling Expert Dispatch and Aggregation in Sparse Mixture-of-Experts

arXiv:2608.08853v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) routers commonly use the same scores both to select experts and to weight their already-computed outputs. We study wheth

Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training

arXiv:2608.09217v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform tas

Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages

arXiv:2603.12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge an

Beyond Tables: Doc2DB-Bench for Relationally Faithful Document-to-Database Construction

arXiv:2608.08459v1 Announce Type: cross Abstract: Practical AI systems increasingly need to turn long, heterogeneous documents into queryable relational databases, not isolated spreadsheets. In domain

Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents

arXiv:2608.09555v1 Announce Type: new Abstract: External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks. Yet their effe

Biologically Informed Representation Learning for Robust Cross-Center Generalization of MALDI-TOF Mass Spectrometry

arXiv:2608.08182v1 Announce Type: cross Abstract: Machine learning models for MALDI-TOF mass spectrometry have shown considerable promise for clinical microbiology tasks such as microbial identificati

Local Ai

Bootstrapping Vision-Language Model for Hysteroscopic Surgical Scene Segmentation

arXiv:2608.09302v1 Announce Type: new Abstract: Hysteroscopic surgical scene segmentation plays a pivotal role in understanding the hysteroscopic intraoperative environment as well as computer-assiste

local-aiarxiv-cs-cv

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

arXiv:2512.11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic

agentsarxiv-cs-ai

Local Ai

Bright-Channel Retinex Enhancement with a Conditional Overdispered-Noise Analysis

arXiv:2608.09137v1 Announce Type: new Abstract: I present a training-free low-light enhancement method that combines local bright-channel illumination estimation, Retinex division, and edge-preserving

local-aiarxiv-cs-cv

CableDex: Cable Length Estimation on Industrial Reels Using a Handheld Device

arXiv:2608.09392v1 Announce Type: new Abstract: CableDex is a computer vision system that addresses the time-consuming and inaccurate manual measurement of cable length on industrial reels from a sing

researcharxiv-cs-cv

Calling the Bluff: Detecting Ever-Shifting Harmful Chat Dialogue via Ordered Reasoning Chain Regularization

arXiv:2608.08451v1 Announce Type: cross Abstract: Harmful chat dialogues are ever-shifting through type-shifting and lexical evasion, yet we find they share invariant principles, i.e., an Ordered Reas

researcharxiv-cs-ai

Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations

arXiv:2608.09268v1 Announce Type: cross Abstract: Visual modality has recently been explored as a way to compress textual tokens, including rendering code as images for static code understanding. We s

agentsarxiv-cs-ai

Can Graph Learning Learn Circuits?

arXiv:2608.08536v1 Announce Type: new Abstract: Circuit localization is a mechanistic interpretability task whose goal is to identify a sparse subgraph of a transformer's computation graph sufficient

Can Open-Weight Models Compete on Financial Text Comprehension?

arXiv:2608.08634v1 Announce Type: new Abstract: Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability

Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs

arXiv:2608.08744v1 Announce Type: cross Abstract: The carbon footprint of any deployed Large Language Model (LLM) accumulates during inference, where repeated use of the model substantially exceeds th

CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation

arXiv:2608.09790v1 Announce Type: new Abstract: Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions r

agentsarxiv-cs-ai

CausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift

arXiv:2608.07809v1 Announce Type: new Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong. We study both ha

CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals

arXiv:2608.07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture,

researcharxiv-cs-ai

CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment

arXiv:2608.09164v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human privacy preferences requires capturing individuals' disclosure boundaries beyond general privacy norms.

CLEAR: A Semantic-Geometric Terrain Abstraction for Large-Scale Unstructured Environments

arXiv:2601.13361v2 Announce Type: replace Abstract: Long-horizon navigation in unstructured environments demands terrain abstractions that scale to tens of square kilometers while preserving semantic

agentsarxiv-cs-ro

CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR

arXiv:2608.07796v1 Announce Type: new Abstract: Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investiga

Closing the loop in learning with missing data

arXiv:2608.09030v1 Announce Type: cross Abstract: What should a machine learning model learn when data is missing during training? We look at the learning process from a dynamical systems perspective,

CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models

arXiv:2608.07621v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primari

CoCoNav: Conformal Control for Safe Robot Navigation in Crowds

arXiv:2608.07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors. Ex

researcharxiv-cs-lg

CODS: Iterative Bellman-Residual Data Selection for Reusable Offline Reinforcement Learning

arXiv:2608.07719v1 Announce Type: new Abstract: Offline reinforcement learning repeatedly trains policies from a fixed transition pool, making redundant data costly across seeds and hyperparameters, w

researcharxiv-cs-lg

Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP

arXiv:2604.01653v2 Announce Type: replace Abstract: Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states e

researcharxiv-cs-lg

Local Ai

ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners

arXiv:2608.09732v1 Announce Type: cross Abstract: Agent skills are emerging as an important attack surface in LLM-based agent systems. Through an empirical study of existing skill scanners, we find th

local-aiarxiv-cs-ai

COMEX: A Composition-Grounded Benchmark and Learning Framework for Explainable Aesthetic Image Cropping

arXiv:2608.07570v1 Announce Type: cross Abstract: Explainable aesthetic image cropping requires not only localizing a visually pleasing crop but also explaining why it is preferred. Existing crop-and-

Communication-efficient distributed hazard difference estimation for heterogeneous multi-site survival data

arXiv:2601.14609v2 Announce Type: replace-cross Abstract: Multi-site collaboration can power survival models that no single hospital could fit alone, but privacy rules and protected computing environm

researcharxiv-cs-ai

ComplexityWorld: Benchmarking Vision-Language Models on Verifiable Visual Decision Making

model-releasesarxiv-cs-cv

arXiv:2608.07584v1 Announce Type: new Abstract: Vision-language models (VLMs) have made rapid progress in visual perception and increasingly support real-world tasks that depend on images. Many such t

Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training

arXiv:2608.09688v1 Announce Type: cross Abstract: Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head class