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From the 1 of 13 linked papers with an AI index.

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20242026
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cs.AI2026

Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation

Sarthak Harne, Chinmay Karkar, Yash Pandya +2

Self-distillation (SD) has emerged as a compute-efficient alternative to reinforcement learning with verifiable rewards: a self-teacher, conditioned on privileged information (PI)…

cs.AI2026

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

Yash Pandya, Sahil Gupta, Sarthak Harne +10

Echoverse introduces a pipeline that compiles specifications into deep, stateful synthetic applications for training computer-use agents, using a co‑evolution loop that repairs env…

cs.AI2026

Fara-1.5: Scalable Learning Environments for Computer Use Agents

Ahmed Awadallah, Sahil Gupta, Yash Lara +12

Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environment…

cs.AI2026

Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception

Ashutosh Bajpai, Tamal Majumder, Akshay Nambi +1

Small Vision-Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typically require expensive supervise…

cs.AI2025

Fara-7B: An Efficient Agentic Model for Computer Use

Ahmed Awadallah, Yash Lara, Raghav Magazine +9

Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thri…