Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLM to automatically generate correctness proof for Rust code. AutoVerus is…
Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. Learning-based approaches attempt to address this challenge with advanced generative models, removing the dependency on over-engineered architectures for representation fusion. However, brute-force implementation by simply stacking transformer blocks lacks a…
MCP Interviewer is a Python CLI tool that helps you catch MCP server issues before your agents do. The MCP Interviewer ensures compliance with provider constraints and offers warnings when recommended guidance is not followed, while also supporting optional functional testing using an LLM agent. It…
PCIe devices, such as NICs and SSDs, are frequently underutilized in cloud platforms. PCIe device pools, in which multiple hosts can share a set of PCIe devices, could increase PCIe device utilization and reduce their total cost of ownership. The main way to achieve PCIe…
Juan M. Lavista Ferres, Corporate Vice President and Chief Data Scientist of the AI for Good Lab at Microsoft Corporation, presented his book Artificial Intelligence for Good. AI for Good: Applications in Sustainability, Humanitarian Action, and Health, together with Editorial TAEDA and within the framework of its 20th anniversary, where they propose…
In social settings, people display sophisticated spatial behaviors—for example, one might naturally enter into a conversation by sidling up to a group. Artificial agents will need the ability to reason about spatial representations of social information to understand not only how social groups form, but…
We seek a Principal Researcher with expertise in query execution, data storage, resource management for serverless cloud platforms, data discovery, and data preparation techniques. Candidates with experience in leveraging novel hardware, Artificial Intelligence (AI), and Machine Learning (ML) for data management challenges are of special…
This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional RAG methods usually perform a single retrieval step before the generation process, which limits their effectiveness in addressing complex…
Language model (LM) agents deployed in novel environments often exhibit poor sample efficiency when learning from sequential interactions. This significantly hinders the usefulness of such agents in environments where interaction is costly (for example, when they interact with humans or reset physical systems). While a…
E. Glen Weyl appeared on a panel at Harvard University about the implications of AI for the human soul.
Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their performance in long-horizon, interactive tasks such as web navigation and computer/phone-use. Inspired by literature on human cognition, we argue…
Non-determinism and non-reproducibility present significant challenges in deep learning, leading to inconsistent results across runs and platforms. These issues stem from two origins: random number generation and floating-point computation. While randomness can be controlled through deterministic configurations, floating-point inconsistencies remain largely unresolved. To address this,…
