52 citations · 69 across the 11 of their papers we have counts for
14 papers
ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents
Xinyue Zeng, Shivam Shandilya, Guilherme Potje +6
Interactive language-model agents increasingly solve complex tasks through long-horizon, multi-call reasoning, where errors in beliefs or actions can compound across tool interacti…
Self-Designed Evaluators and Warm Memory for Long-Horizon Agents
Saeid Asgari, Emre Kiciman, Leonardo de Oliveira Nunes +1
A tool-using language-model agent deployed over a long stream of tasks receives no reward, so it cannot tell whether it succeeded, cannot safely retry, and cannot label the experie…
Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising
Tianci Liu, Zihan Dong, Linjun Zhang +5
Slide design requires personalizing both deck themes and page layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespeci…
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
Garvita Allabadi, Matteo Sodano, Roberto Estevão +4
In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Mod…
Diagnosing Capability Gaps in Fine-Tuning Data
Saeid Asgari Taghanaki, Rakshanda Agarwal, Bruce Sun +10
Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a practitioner needs. Yet identifyi…
SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, Guilherme Potje, Shivam Shandilya +9
Designing aligned and robust rewards for open-ended generation remains a key barrier to RL post-training. Rubrics provide structured, interpretable supervision, but scaling rubric…
Enterprise AI Must Enforce Participant-Aware Access Control
Shashank Shreedhar Bhatt, Tanmay Rajore, Khushboo Aggarwal +10
Large language models (LLMs) are increasingly deployed in enterprise settings where they interact with multiple users and are trained or fine-tuned on sensitive internal data. Whil…
Direct Reasoning Optimization: Token-Level Reasoning Reflectivity Meets Rubric Gates for Unverifiable Tasks
Yifei Xu, Tusher Chakraborty, Srinagesh Sharma +6
Reinforcement learning (RL) training of large language models (LLMs) on unverifiable tasks is challenging even when a reasonable-quality reference answer is available. We propose a…
RLTHF: Targeted Human Feedback for LLM Alignment
Yifei Xu, Tusher Chakraborty, Emre Kıcıman +11
Fine-tuning large language models (LLMs) to align with user preferences is challenging due to the high cost of quality human annotations in Reinforcement Learning from Human Feedba…
Enabling Adoption of Regenerative Agriculture through Soil Carbon Copilots
Margaret Capetz, Swati Sharma, Rafael Padilha +4
Mitigating climate change requires transforming agriculture to minimize environ mental impact and build climate resilience. Regenerative agricultural practices enhance soil organic…
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
Nick Mecklenburg, Yiyou Lin, Xiaoxiao Li +9
In recent years, Large Language Models (LLMs) have shown remarkable performance in generating human-like text, proving to be a valuable asset across various applications. However,…
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha +13
There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented…
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Somya Sharma, Swati Sharma, Rafael Padilha +2
Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fideli…
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
Bruno Silva, Leonardo Nunes, Roberto Estevão +2
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding across various domains, including healthcare and finance. For some tasks, L…