2 citations · 2 across the 8 of their papers we have counts for
10 papers
-Multilingual: Benchmarking Voice Agents Across Languages
Soham Ray, Edgard dos Santos Paiva, Ruben Valenzuela +3
English-only benchmarks expose only a narrow slice of voice-agent behavior. We introduce -Multilingual, extending -Voice to Spanish, Brazilian Portuguese, Hindi, Korean, and…
-Elicitation: Benchmarking multi-turn entity extraction in voice agents
Soham Ray, Victor Barres
Voice agents often need to collect names, addresses, identifiers, dates, and times exactly, yet end-to-end benchmarks obscure where capture fails. We introduce -Elicitation, a 2…
-Bench: An Environment for End-To-End, Realistic Agent Construction
Quan Shi, Keshav Dhandhania, Karthik Narasimhan +1
LLM agents are rapidly becoming production software, deployed to handle customer service, adjudicate disputes, and operate internal systems. Notably, the work of building them is i…
AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility
Xiaoyuan Liu, Jianhong Tu, Yuqi Chen +26
Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, cr…
-Voice: Benchmarking Full-Duplex Voice Agents on Real-World Domains
Soham Ray, Keshav Dhandhania, Victor Barres +1
Full-duplex voice agents--systems that listen and speak simultaneously--are rapidly moving from research to production. However, existing evaluations address conversational dynamic…
-Knowledge: Evaluating Conversational Agents over Unstructured Knowledge
Quan Shi, Alexandra Zytek, Pedram Razavi +2
Conversational agents are increasingly deployed in knowledge-intensive settings, where correct behavior depends on retrieving and applying domain-specific knowledge from large, pro…
-Bench: Evaluating Conversational Agents in a Dual-Control Environment
Victor Barres, Honghua Dong, Soham Ray +2
Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a pa…
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning
Mrinal Rawat, Hithesh Sankararaman, Victor Barres
Deriving value from a conversational AI system depends on the capacity of a user to translate the prior knowledge into a configuration. In most cases, discovering the set of releva…
Multi-step Inference over Unstructured Data
Aditya Kalyanpur, Kailash Karthik Saravanakumar, Victor Barres +8
The advent of Large Language Models (LLMs) and Generative AI has revolutionized natural language applications across various domains. However, high-stakes decision-making tasks in…
LLM-ARC: Enhancing LLMs with an Automated Reasoning Critic
Aditya Kalyanpur, Kailash Karthik Saravanakumar, Victor Barres +3
We introduce LLM-ARC, a neuro-symbolic framework designed to enhance the logical reasoning capabilities of Large Language Models (LLMs), by combining them with an Automated Reasoni…