AGENTIC-SYSTEMSApril 3, 20261 min read14 views

Logarithmic Scores, Power-Law Discoveries: Disentangling Measurement from Coverage in Agent-Based Evaluation

Researchers demonstrate that persona-based LLM agent judges produce evaluations indistinguishable from humans, revealing a unique dissociation between scoring accuracy and issue discovery coverage.

Computer Science > Artificial Intelligence

Title:Logarithmic Scores, Power-Law Discoveries: Disentangling Measurement from Coverage in Agent-Based Evaluation

View PDF HTML (experimental)Abstract:LLM-based agent judges are an emerging approach to evaluating conversational AI, yet a fundamental uncertainty remains: can we trust their assessments, and if so, how many are needed? Through 960 sessions with two model pairs across 15 tasks, we show that persona-based agent judges produce evaluations indistinguishable from human raters in a Turing-style validation. We then identify a score-coverage dissociation: quality scores improve logarithmically with panel size, while unique issue discoveries follow a sublinear power law-both exhibit diminishing returns, but scores saturate roughly twice as fast as discoveries. We hypothesize this reflects a power law distribution of the finding space: critical issues are discovered first by small panels, while corner cases require progressively larger panels, analogous to species accumulation curves in ecology. The mechanism traces to ensemble diversity-Big Five personality conditioning makes agents probe different quality dimensions, with expert judges acting as adversarial probes that push discovery into the tail of the finding distribution. A controlled ablation confirms that structured persona conditioning, not simple prompting, is required to produce these scaling properties.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

Researchers have introduced VeriSimpl, a novel framework that leverages LLMs and simplification-based verification to accurately translate natural language descriptions into executable optimization formulations.

agentic-systems

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

Researchers introduced SonicSampler, a unified suite of tile-aware Triton kernels designed to accelerate LLM sampling and speculative verification. By fusing the entire pipeline into a single CUDA Graph-compatible kernel, SonicSampler achieves up to 16x speedups over state-of-the-art baselines.

agentic-systems

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

Researchers have introduced PersonaTrail, a novel benchmark evaluating personalized web agents through realistic browsing histories. Alongside it, the PACMem memory framework structures raw browsing data to significantly boost agent performance in inferring user preferences.

agentic-systems

Benchmarking the Personalization Capabilities of Large Language Models

A new study introduces SDR-Bench and SDR-Arena to benchmark the personalization capabilities of LLMs in two-party persuasion scenarios, revealing a personalization plateau among frontier models.

agentic-systems

Incomplete Prompt Jailbreaks in Large Language Models

Researchers have conceptualized Incomplete Prompt Jailbreaks (IPJ), a vulnerability where incomplete harmful prompts bypass LLM safeguards due to delayed refusal behaviors. The discovery of functional neurons offers a novel path for precise neuron-level defense interventions.

agentic-systems

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

A new benchmark evaluates five leading large language models on multi-sensor physical hazard assessments, revealing a critical vulnerability when evaluating combined sensor risks below individual thresholds. Despite near-perfect accuracy on single-sensor breaches, all models failed to issue precautionary signals for multi-sensor hazards.