arXiv AI
Aug 7
EcoAgent-Bench: Evaluating Economic Decision-Making in Budget-Constrained LLM Agents
arXiv:2608. 05519v1 Announce Type: new Abstract: Agent benchmarks usually measure task completion and treat resource use as an auxiliary statistic.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Aug 7
SkillTV-Bench: Benchmarking How Well Judges Perform on Skill-Augmented Agentic Execution
arXiv:2608. 05573v1 Announce Type: new Abstract: LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions.
arXiv AI
Aug 7
Measuring and Detecting Harmful AI Sycophancy
arXiv:2608. 05624v1 Announce Type: new Abstract: Sycophantic responses are becoming pervasive in large language models (LLMs), and prior work has pointed out that some of them could be harmful.
arXiv AI
Aug 7
Cautious Context Steering for Language Model Personalization
arXiv:2608. 05813v1 Announce Type: new Abstract: Personalizing language models (LMs) to individual user preferences is essential for aligning responses with diverse goals and backgrounds.
arXiv AI
Aug 7
VLMs for Videogame Data Annotation
arXiv:2608. 05949v1 Announce Type: new Abstract: Vision Language Models (VLMs) and Artificial Intelligence (AI) agents have revolutionized how engineers approach complex problems in real-world applications.
arXiv AI
Aug 7
Training a Conditioned Video Game Agent on a VLM Annotated Dataset
arXiv:2608. 05954v1 Announce Type: new Abstract: Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning.
arXiv AI
Aug 7
Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents
arXiv:2608. 06108v1 Announce Type: new Abstract: Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, portfolios, and risk boundaries.
arXiv AI
Aug 7
ECHO: A Locally-Deployable Agentic Health Assistant with Temporal Memory, Safety Guardrails, and Speech Assessment
arXiv:2608. 06110v1 Announce Type: new Abstract: This paper presents ECHO (Enhanced Care \& Health Observer), a locally-deployable conversational health assistant for long-term chronic care management.
arXiv AI
Aug 7
Contextual Information Policy Optimization for Search Agents
arXiv:2608. 06128v1 Announce Type: new Abstract: Search agents extend large language models beyond static parametric memory by enabling them to acquire and use ex ternal evidence during multi-step reasoning.
arXiv AI
Aug 7
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
arXiv:2608. 06144v1 Announce Type: new Abstract: Most agent benchmarks evaluate tasks independently and cannot measure whether experience from one task helps with later tasks.
arXiv AI
Aug 7
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
arXiv:2608. 06294v1 Announce Type: new Abstract: Cardiac arrest remains one of the most lethal conditions encountered in intensive care units.
arXiv AI
Aug 7
TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories
arXiv:2608. 06346v1 Announce Type: new Abstract: LLM-based agentic systems have shown remarkable capabilities in complex domains, while suffering from cascading errors and difficulty in debugging.
arXiv AI
Aug 7
Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
arXiv:2608. 06366v1 Announce Type: new Abstract: Electronic health record (EHR) feature engineering is a major bottleneck in clinical research and AI, accounting for 39-45% of data scientists' workload.
arXiv AI
Aug 7
HoloCount: A Holistic Visual Counting Benchmark for MLLMs
arXiv:2607. 06420v1 Announce Type: cross Abstract: Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning.
arXiv AI
Aug 7
Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support
arXiv:2608. 05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, when asking causal questions such as "why is N2O rising?
arXiv AI
Aug 7
Beyond Sentiment: Comparing Traditional NLP and LLM-Based Multi-Dimensional Analysis for Political News Evaluation
arXiv:2608. 05155v1 Announce Type: cross Abstract: Traditional sentiment analysis (SA) models, while effective for polarity classification, provide limited insight into the rhetorical, ideological, and framing dimensions of political discourse -- dimensions that are central to research in the social sciences and humanities (SSH).
arXiv AI
Aug 7
Who Gets Access? Global Region and Academic Status Bias in AI-Generated Academic Gatekeeping Scenarios
arXiv:2608. 05178v1 Announce Type: cross Abstract: Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, datasets, or professional materials such as curriculum vitae (CV) must be shared selectively.
arXiv AI
Aug 7
Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap
arXiv:2608. 05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review.
arXiv AI
Aug 7
Post-Hoc Trajectory-Risk Certification for Modular LLM-Based Security Agents
arXiv:2608. 05199v1 Announce Type: cross Abstract: Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique.
arXiv AI
Aug 7
Position: It's Time to Optimize LLMs for Self-Consistency
arXiv:2608. 05188v1 Announce Type: cross Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition on user framing ("sycophancy"), exhibit incomplete logical generalization, and produce confident but incorrect responses.