arXiv AI
Jun 6
Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns
arXiv:2606. 05872v1 Announce Type: new Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost.
Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns
AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of agent behavior: whether an agent explores too much, repeats itself too rigidly, uses tools effectively, reduces uncertainty over time, or remains robust across repeated runs.
Related stories
arXiv AI
Oct 19
AuditKIT: A Unified Evaluation Spine for Benchmarking, Judging, and Comparing Large Language Models and Agentic Systems
AuditKIT is a source‑available evaluation library that unifies benchmark accuracy, judge‑based assessment, runtime performance, tool‑use behavior, and retrieval quality into a single result representation. It offers direction‑aware metric contracts, configuration fingerprints, and extensible metrics and backends, while providing turn‑aware tool‑call matching and distinguishing verified task outcomes from trajectory‑level diagnostics. Seven case studies demonstrate how answer extraction, runtime behavior, and metric selection can alter evaluation outcomes across model comparison, serving runtimes, retrieval, tool calling, and interoperability.
arXiv AI
Sep 12
Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks
The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.
arXiv AI
Jun 3
Towards a Science of AI Agent Reliability
arXiv:2602. 16666v3 Announce Type: replace Abstract: AI agents are increasingly deployed to execute important tasks.
arXiv AI
Aug 20
Position: Behavioral Systems Require Behavioral Tests
The paper argues that artificial agentic systems, which operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adapting over time, should be evaluated through systematic observation, perturbation, and interpretation of their actions rather than solely on performance outcomes. It draws on lessons from behavioral sciences to motivate this position and proposes a research agenda that includes methods for recovering decision strategies from action sequences, constructing environments that isolate behavioral differences, and probing emergent dynamics in multi‑agent systems. These directions aim to establish a rigorous science of AI behavior.
arXiv AI
Oct 26
Trial by Trace: Benchmarking AI Agent Harnesses for Reliability and Failure Attribution
The paper introduces TraceTrial-Bench, a benchmark that evaluates the reliability of AI agent harnesses across seven components—context, execution, governance, lifecycle, observability, tools, and verification—using 95 expert-level tasks with executable or state-based ground truth. It employs a Trajectory Tracing module to record agent execution, a two-layer evaluation framework to separately assess agent performance and harness reliability, and a Multi-source Evidence Acquisition module to gather evidence for LLM-based evaluation. The benchmark’s analysis of 3,420 runs across four harnesses and nine models shows harness scores between 59.8% and 72.0%, with 31.1% of runs succeeding despite failing at least one harness criterion, highlighting the need for auditable and reproducible harness evaluation.
arXiv Machine Learning
Oct 12
AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents
AgentTune is a source‑available Python library that offers a unified training lifecycle for tool‑using large language model agents. It introduces a two‑tier EventLog representation to preserve agent trajectories and enable consistent training, evaluation, and auditing. The framework supplies a shared runtime, reinforcement learning for tool‑calling, agentic distillation, retrieval‑augmented generation, decision workflows, evaluation utilities, and a path toward self‑healing agents, all exposed through composable interfaces for flexible combination of strategies and methods.
arXiv AI
Sep 1
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
arXiv:2602.02475v2 Announce Type: replace Abstract: AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy...
Hugging Face Trending Papers
Jul 15
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering.
arXiv AI
Jun 2
Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation
arXiv:2606. 01725v1 Announce Type: new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes.
arXiv Computation and Language
Sep 25
An Empirical Study of Automating Agent Evaluation
The paper presents EvalAgent, an AI assistant that automates agent evaluation by encoding domain expertise into evaluation skills such as procedural instructions, reusable code, and dynamic API retrieval. EvalAgent constructs a trace-based pipeline that outputs metrics, executable code, and reports, and is evaluated using a new meta-evaluation framework and AgentEvalBench. Results show that EvalAgent improves the Eval@1 metric from 17.5% to 65% and receives 79.5% human expert preference, while ablation studies confirm the importance of evaluation skills.
arXiv AI
Jun 30
Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
arXiv:2606. 29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision.