1 citations · 3 across the 18 of their papers we have counts for
19 papers
Reflective Prompt Optimization of a Deep-Research Agent: A Topic-Disjoint, Multi-Seed Protocol for Measuring Transferable Gains and Prompt-Level Overfitting
Elias Lumer, Sahil Sen, Vamse Kumar Subbiah +1
Prompt optimization is an attractive alternative to reinforcement learning for adapting language-model agents, since it is cheap and produces human-readable artifacts. On factual t…
The Bitter Lesson of Tool Calling
Ishan Patel, Sahil Sen, Elias Lumer +1
Tool use transforms LLMs into agents that act beyond their training data, and for code-capable models, programmatic tool calling extends this further by replacing rigid JSON calls…
Do You Need a Frontier Model as a Citation Verifier? Benchmarking Rubric LLMs for Deep-Research Source Attribution
Ethan Leung, Elias Lumer, Corey Feld +3
Reinforcement learning increasingly relies on an LLM judge to score each rubric criterion, and that judge acts as the reward model during training. Before such a signal can be trus…
Recursive Agent Harnesses
Elias Lumer, Sahil Sen, Kevin Paul +1
Recursive language models (RLMs) showed that recursion over model calls is an effective strategy for long-context reasoning, and production coding agents have begun to write code t…
Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Sahil Sen, Akhil Kasturi, Elias Lumer +2
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpor…
Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents?
Anmol Gulati, Hariom Gupta, Elias Lumer +2
Long-horizon AI agents execute complex workflows spanning hundreds of sequential actions, yet a single wrong assumption early on can cascade into irreversible errors. When instruct…
Cited but Not Verified: Parsing and Evaluating Source Attribution in LLM Deep Research Agents
Hailey Onweller, Elias Lumer, Austin Huber +3
Large language models (LLMs) power deep research agents that synthesize information from hundreds of web sources into cited reports, yet these citations cannot be reliably verified…
Agentic Jackal: Live Execution and Semantic Value Grounding for Text-to-JQL
Vishnu Murali, Anmol Gulati, Elias Lumer +3
Translating natural language into Jira Query Language (JQL) requires resolving ambiguous field references, instance-specific categorical values, and complex Boolean predicates. Sin…
Chronos: Temporal-Aware Conversational Agents with Structured Event Retrieval for Long-Term Memory
Sahil Sen, Elias Lumer, Anmol Gulati +1
Recent advances in Large Language Models (LLMs) have enabled conversational AI agents to engage in extended multi-turn interactions spanning weeks or months. However, existing memo…
Don't Break the Cache: An Evaluation of Prompt Caching for Long-Horizon Agentic Tasks
Elias Lumer, Faheem Nizar, Akshaya Jangiti +4
Recent advancements in Large Language Model (LLM) agents have enabled complex multi-turn agentic tasks requiring extensive tool calling, where conversations can span dozens of API…
Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems
Elias Lumer, Alex Cardenas, Matt Melich +5
Recent advancements in Retrieval-Augmented Generation (RAG) have enabled Large Language Models (LLMs) to access multimodal knowledge bases containing both text and visual informati…
Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems
Faheem Nizar, Elias Lumer, Anmol Gulati +2
Recent advances in Large Language Model Multi-Agent Systems enable scalable orchestration and retrieval of specialized, parallelized subagents, each equipped with hundreds or thous…
Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models
Elias Lumer, Matt Melich, Olivia Zino +6
Recent advancements in Retrieval-Augmented Generation (RAG) have enabled Large Language Models to answer financial questions using external knowledge bases of U.S. SEC filings, ear…
Tool-to-Agent Retrieval: Bridging Tools and Agents for Scalable LLM Multi-Agent Systems
Elias Lumer, Faheem Nizar, Anmol Gulati +2
Recent advances in LLM Multi-Agent Systems enable scalable orchestration of sub-agents, each coordinating hundreds or thousands of tools or Model Context Protocol (MCP) servers. Ho…
Jackal: A Real-World Execution-Based Benchmark Evaluating Large Language Models on Text-to-JQL Tasks
Kevin Frank, Anmol Gulati, Elias Lumer +2
Enterprise teams rely on the Jira Query Language (JQL) to retrieve and filter issues from Jira. Yet, to our knowledge, there is no open, real-world, execution-based benchmark for m…
MemTool: Optimizing Short-Term Memory Management for Dynamic Tool Calling in LLM Agent Multi-Turn Conversations
Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah +2
Large Language Model (LLM) agents have shown significant autonomous capabilities in dynamically searching and incorporating relevant tools or Model Context Protocol (MCP) servers f…
ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools for LLM Agents
Elias Lumer, Anmol Gulati, Vamse Kumar Subbiah +2
Recent advancements in Large Language Models (LLMs) and the introduction of the Model Context Protocol (MCP) have significantly expanded LLM agents' capability to interact dynamica…
Graph RAG-Tool Fusion
Elias Lumer, Pradeep Honaganahalli Basavaraju, Myles Mason +2
Recent developments in retrieval-augmented generation (RAG) for selecting relevant tools from a tool knowledge base enable LLM agents to scale their complex tool calling capabiliti…
Toolshed: Scale Tool-Equipped Agents with Advanced RAG-Tool Fusion and Tool Knowledge Bases
Elias Lumer, Vamse Kumar Subbiah, James A. Burke +2
Recent advancements in tool-equipped Agents (LLMs) have enabled complex tasks like secure database interactions and multi-agent code development. However, scaling tool capacity bey…