activity
20222026
most citedMeasuring Reliability of Large Language Models through Semantic Consistency

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

collaborators
Showing cs.AIShow all

5 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.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…