2 citations · 3 across the 14 of their papers we have counts for
19 papers
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck
Shubham Agarwal, Akshat Gupta, Mike Heddes +21
Evaluation has been central to advances in AI, and it is equally critical to autonomous discovery. Yet scaling autonomous discovery to real-world engineering challenges faces a fun…
SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
Mingtian Tan, Palash Goyal, Mihir Parmar +6
Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insuffi…
AIM: Agentic Idea Management for Automated Research
Hyeong Kyu Choi, Bhavana Dalvi Mishra, Jiefeng Chen +7
Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven search and identify three core…
ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI
Jaehyun Nam, Jinsung Yoon, Yanzhou Pan +4
Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is prob…
AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
Zhangchen Xu, Junda Chen, Yue Huang +16
Scientific and engineering progress is fundamentally a long-horizon iterative process: proposing changes, running experiments, measuring outcomes, and continuously refining artifac…
LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks
Po-Nien Kung, Linfeng Song, Dawsen Hwang +10
Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean. We present LEAP,…
T2J-BENCH: Benchmarking Coding Agents on Execution-Grounded PyTorch-to-JAX Repository Migration
Tao Feng, Linxin Song, Jiefeng Chen +9
Migrating a machine-learning codebase across frameworks--for example from PyTorch to JAX--is a routine but costly engineering task that is fundamentally cross-paradigm: eager, stat…
ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen +10
Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua…
Nexus : An Agentic Framework for Context-Aware Time Series Forecasting
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6
Language models have emerged as highly capable reasoners, yet their performance in time series forecasting remains uneven across domains and levels of contextual grounding. This is…
LEAF: A Living Benchmark for Event-Augmented Forecasting
Mingtian Tan, Mihir Parmar, Palash Goyal +5
Large Language Models (LLMs) are increasingly applied to real-world forecasting tasks, yet evaluating their true predictive capability remains compromised by pre-training data cont…
STRIDE: Reasoning-Aware Training for Time Series Forecasting
Md Atik Ahamed, Mihir Parmar, Palash Goyal +4
Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data i…
PolicyBank: Evolving Policy Understanding for LLM Agents
Jihye Choi, Jinsung Yoon, Long T. Le +2
LLM agents operating under organizational policies must comply with authorization constraints typically specified in natural language. In practice, such specifications inevitably c…
TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
Md Atik Ahamed, Mihir Parmar, Palash Goyal +7
We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely…
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
Yiwen Song, Yale Song, Tomas Pfister +1
Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidl…
MARS: Modular Agent with Reflective Search for Automated AI Research
Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam +3
A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computati…
PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu, Rui Meng, Yale Song +4
Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workf…
ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
Palash Goyal, Mihir Parmar, Yiwen Song +3
The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for authors and an immense burden on revie…
DocLens : A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding
Dawei Zhu, Rui Meng, Jiefeng Chen +3
Comprehending long visual documents, where information is distributed across extensive pages of text and visual elements, is a critical but challenging task for modern Vision-Langu…
Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +7
Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seaso…