Mihir Parmar

CANVAS: Continuity-Aware Narrative Generation via Visual Agentic Storyboarding . Findings of Empirical Methods in Natural Language Processing , 2026.

WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting

This work introduces WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events, and reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.

Twice Upon a Time: Complementary Representations for Time-Series Reasoning

This model combines two visual views: line plots preserve absolute measurements and the observed coordinate frame, while normalized delay embeddings factor out positive amplitude rescaling and offsets while exposing temporal structure.

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high noise, missing signals,...

AIM: Agentic Idea Management for Automated Research

It is shown that explicit idea-level allocation makes semantic coverage directly controllable, and that broader coverage becomes increasingly valuable when competitive research directions are sparse among many plausible alternatives.

Locating Hidden Failures Makes Long-Horizon Agents More Reliable

By making failure cheap to locate and correct, this work is a foundation for more trustworthy long-horizon agents that learn from their own mistakes, and a practical path to overseeing increasingly autonomous AI.