44 citations · 94 across the 22 of their papers we have counts for
6 papers · 1 filter
D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models
Marius Memmel, Shawn Yin, Urvi Rutia +9
Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, halluci…
Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability
Ankit Goyal, Jaideep Ray
Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forget: a new model may interpret old notes differently, mixed embedding ve…
Mind the Cap: Output-Budget Regimes Change the Measured Multilingual Reasoning Gap
Ankit Goyal, Jaideep Ray
Multilingual evaluations report accuracy at a single output-token cap, but languages need different numbers of tokens to express the same content, so the cap is a hidden experiment…
From Agent Failures to Text Policies: What Works and What Breaks
Jaideep Ray, Ankit Goyal
TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gradient for optimizing text components with…
Structured Feedback Improves Repair in an LLM Agent Loop
Jaideep Ray, Ankit Goyal
LLM agents often retry after external validation rejects a candidate, but the interface between validation and the next model call remains underspecified. We introduce VeriHarness,…
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
Jenai Xuning Yang, Rishit Dagli, Alex Zook +5
The pursuit of general-purpose robotics has yielded impressive foundation models, yet simulation-based benchmarking remains a bottleneck due to rapid performance saturation and a l…