27 citations · 87 across the 24 of their papers we have counts for
7 papers · 1 filter
On the Off-Policy Teacher in On-Policy Distillation
Langlin Huang, Hao Liu, Mononito Goswami +6
On-policy distillation (OPD) has recently emerged as a promising post-training paradigm in which the student learns from trajectories generated by its own policy under dense teache…
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Prithwish Jana, Mononito Goswami, Hao Liu +9
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its…
Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling
Xinyu Li, Mononito Goswami, Hao Liu +5
Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context windows requires deciding how…
MICA: Multivariate Infini Compressive Attention for Time Series Forecasting
Willa Potosnak, Nina Żukowska, Michał Wiliński +4
Multivariate forecasting with Transformers faces a core scalability challenge: modeling cross-channel dependencies via attention compounds attention's quadratic sequence complexity…
ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response
Stephan Xie, Ben Cohen, Mononito Goswami +6
Time series question-answering (TSQA), in which we ask natural language questions to infer and reason about properties of time series, is a promising yet underexplored capability o…
TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
Malgorzata Gwiazda, Yifu Cai, Mononito Goswami +2
Large Language Models (LLMs) have shown promising performance in time series modeling tasks, but do they truly understand time series data? While multiple benchmarks have been prop…
Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting
Azul Garza, Renée Rosillo, Rodrigo Mendoza-Smith +5
Recent advances in time-series forecasting increasingly rely on pre-trained foundation-style models. While these models often claim broad generalization, existing evaluation protoc…