1 citations · 2 across the 9 of their papers we have counts for
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GlobeBench: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations
Nicholas Saban, Charvi Bannur, Mariya Vasileva +1
Text-based world models have recently shown potential to simulate complex RL environment state transitions for a diverse set of use cases. However, no standardized benchmark exists…
Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL
Darshan Deshpande
Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments. Hand-curated environments with fixed task and reward difficulties b…
MEMTRACK: Evaluating Long-Term Memory and State Tracking in Multi-Platform Dynamic Agent Environments
Darshan Deshpande, Varun Gangal, Hersh Mehta +3
Recent works on context and memory benchmarking have primarily focused on conversational instances but the need for evaluating memory in dynamic enterprise environments is crucial…
TRAIL: Trace Reasoning and Agentic Issue Localization
Darshan Deshpande, Varun Gangal, Hersh Mehta +3
The increasing adoption of agentic workflows across diverse domains brings a critical need to scalably and systematically evaluate the complex traces these systems generate. Curren…
Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning
Sky CH-Wang, Darshan Deshpande, Smaranda Muresan +2
We introduce Browsing Lost Unformed Recollections, a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants. BLUR introduces a set of 573 real-world…