AI That Reasons About Its Past and Calibrates Its Conversation

Here are summaries of two recent papers at the intersection of AI, Machine Learning, and Systems, with a focus on their practical implications.

1. Calibrating LLM Chatbots to Be Better Recommenders

• Paper Title and Authors: On Overcoming Miscalibrated Conversational Priors in LLM-based Chatbots (Christine Herlihy, Jennifer Neville, Tobias Schnabel, Adith Swaminathan)
• Publication Venue/Source and Date: arXiv preprint (UAI'24 Conference), June 2024
• Quick Summary: LLM-based chatbots often fail at recommendation tasks (e.g., “suggest a movie”) because their general conversational training makes them perform poorly on under-specified requests. The paper introduces a method to “re-calibrate” the LLM by providing it with learned control messages, which significantly improves its ability to respond usefully.
• Why it matters: This research provides a practical method for improving the performance of LLMs in specialized roles like recommender systems. Instead of expensive fine-tuning, this approach guides the model’s existing knowledge, making it cheaper and faster to adapt chatbots for specific business use cases where user queries might be vague.
• Key Technical Insight: The core idea is to treat the LLM’s conversational tendencies as a “prior” belief that can be updated. By learning a “control message” (a specific instruction or piece of context), they can guide the LLM to a better response strategy for the specific task of recommendation, overcoming its unhelpful, generalist conversational habits.
• Link to the paper: https://arxiv.org/abs/2406.01633

2. Automating Proofs for Safer Distributed Systems

• Paper Title and Authors: Basilisk: Using Provenance Invariants to Automate Proofs of Undecidable Protocols (Tony Nuda Zhang, Keshav Singh)
• Publication Venue/Source and Date: OSDI 2025 (Best Paper Award)
• Quick Summary: Proving that distributed systems (like databases and consensus protocols) are free of bugs is incredibly difficult and often manual. Basilisk is a new tool that automates a significant part of this process by analyzing the “provenance” (the origin or cause) of a system’s state.
• Why it matters: This work could dramatically reduce the effort required to build reliable and safe distributed systems. By automating the verification process, developers can catch critical bugs in complex protocols like Multi-Paxos with less manual effort, leading to more robust and trustworthy software infrastructure.
• Key Technical Insight: Basilisk introduces “Provenance Invariants,” which are rules that connect a variable’s current value back to the specific protocol step that set it. By automatically analyzing the code to derive these invariants, the tool can prove safety properties for a wide range of complex, real-world protocols that were previously difficult to verify automatically.
• Link to the paper: While the direct OSDI link may be behind a paywall, the work is based on research available through university and public archives. A good starting point is searching for the title on Google Scholar or arXiv.