2 citations · 2 across the 11 of their papers we have counts for
12 papers
Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning
Peng Du, Kiran Kamble, Rakshith Vasudev +5
Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model with Anchored Su…
The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI
Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally +29
Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger replayed contexts -- so tokens…
Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens
Zhenyu Zhao, Sander Land, Daniel M. Bikel +1
Reasoning in Large Language Models incurs significant inference-time compute, yet the token-level information structure of reasoning traces remains underexplored. We observe that r…
The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications
Zhenyu Zhao, Aparna Balagopalan, Adi Agrawal +3
Given the increased use of LLMs in financial systems today, it becomes important to evaluate the safety and robustness of such systems. One failure mode that LLMs frequently displa…
Accurate Failure Prediction in Agents Does Not Imply Effective Failure Prevention
Rakshith Vasudev, Melisa Russak, Dan Bikel +1
Proactive interventions by LLM critic models are often assumed to improve reliability, yet their effects at deployment time are poorly understood. We show that a binary LLM critic…
Towards Outcome-Oriented, Task-Agnostic Evaluation of AI Agents
Waseem AlShikh, Muayad Sayed Ali, Brian Kennedy +1
As AI agents proliferate across industries and applications, evaluating their performance based solely on infrastructural metrics such as latency, time-to-first-token, or token thr…
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Shelly Bensal, Umar Jamil, Christopher Bryant +5
We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-re…
Expect the Unexpected: FailSafe Long Context QA for Finance
Kiran Kamble, Melisa Russak, Dmytro Mozolevskyi +3
We propose a new long-context financial benchmark, FailSafeQA, designed to test the robustness and context-awareness of LLMs against six variations in human-interface interactions…
Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Melisa Russak, Umar Jamil, Christopher Bryant +4
In this paper, we introduce Writing in the Margins (WiM), a new inference pattern for Large Language Models designed to optimize the handling of long input sequences in retrieval-o…
Comparative Analysis of Retrieval Systems in the Real World
Dmytro Mozolevskyi, Waseem AlShikh
This research paper presents a comprehensive analysis of integrating advanced language models with search and retrieval systems in the fields of information retrieval and natural l…
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web
Raghav Kapoor, Yash Parag Butala, Melisa Russak +4
For decades, human-computer interaction has fundamentally been manual. Even today, almost all productive work done on the computer necessitates human input at every step. Autonomou…
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
Waseem AlShikh, Manhal Daaboul, Kirk Goddard +4
In this paper, we introduce the Instruction Following Score (IFS), a metric that detects language models' ability to follow instructions. The metric has a dual purpose. First, IFS…