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20222026
most citedMeasuring Reliability of Large Language Models through Semantic Consistency

9 citations · 16 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

Harsh Raj, David Lee, Anas Mahmoud +7

The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms ou…

cs.AI2026

Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation

Lin Shi, Haowei Lin, Zixuan Zhu +123

Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a…

cs.LG2026

Synthetic Persona Pretraining: Alignment from Token Zero

Julian Minder, Viktor Moskvoretskii, Raghav Singhal +12

As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today, alignment, and the assistant…

cs.AI2026

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

Harsh Raj, Vipul Gupta, Anas Mahmoud +4

Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the agent system. This creates…

cs.AI2026

OpenThoughts-Agent: Data Recipes for Agentic Models

Negin Raoof, Richard Zhuang, Marianna Nezhurina +47

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts…

cs.AI2026

Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability

Harsh Raj, Niranjan Orkat, Suvrorup Mukherjee +3

This paper establishes a rigorous measurement science for AI agent reliability, providing a foundational framework for quantifying consistency under semantically preserving perturb…