Abstract Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and healthcare that operate under strict policies. To address this limitation, we launched Automated Reasoning checks (ARc) : a public service that (1) uses LLMs with optional human guidance to formalize natural language policies, allowing fine-grained control of the formalization process, and (2) uses inference-time autoformalization to validate logical correctness of natural language statements against those policies. ARc performs multiple redundant formalization steps at inference time, checking the formalizations for semantic equivalence. Our benchmarks show that ARc exceeds 99% soundness and achieves a near-zero false positive rate in identifying logical validity. Our approach produces auditable artifacts that substantiate the verification outcomes and can be used to improve the original text. ARc is the first commercial offering from a major cloud provider to integrate automated reasoning into a generative AI guardrail.

Abstract We introduce , a new procedure that employs the quantified symmetric minimization algorithm from [12] to systematically derive quantified formulas that precisely capture the onset of cutoff and saturation in distributed protocols. performs symmetry-aware forward reachability to enumerate the reachable states of a finite protocol instance, and applies symmetry-preserving logic minimization to express these states as a minimum-cost finitely-quantified reachability formula. repeats this finite analysis process to derive a sequence of reachability formulas $$R_1, R_2, R_3, \cdots $$ R 1 , R 2 , R 3 , ⋯ at increasing protocol sizes. This process terminates at size k when $$R_k$$ R k is a unique solution to symmetric minimization that yields the exact set of reachable states when evaluated at size $$k+1$$ k + 1 . We define $$c:=k$$ c : = k as the cutoff size and $$R_c:=R_k$$ R c : = R k as the cutoff formula . Empirically, $$R_c$$ R c is shown to be a reachability invariant that encodes the reachable states for any protocol size. extends the finite analysis process in [12] by introducing two algorithmic enhancements: a depth-first search algorithm that enumerates the reachable states of a finite protocol by searching only for their symmetric quotient, and an extended quantification pattern inference algorithm that expresses explicit clause orbits of finite instances by logically equivalent quantified formulas. Empirical results demonstrate that, compared to the techniques used in [12], is able to analyze a larger corpus of protocols, derive more compact quantified inductive invariants, and converge at smaller cutoffs. In contrast to previous scholarship, offers a new angle for understanding the notions of cutoff and saturation of distributed protocols. In particular, it raises intriguing questions about the unexpected role of symmetric logic minimization in this much-researched area and opens new directions for further research.

Abstract Synthesizing multi‐character interactions is a challenging task due to the complex and varied interactions between the characters. In particular, precise spatiotemporal alignment between characters is required in generating close interactions such as dancing and fighting. Existing work in generating multi‐character interactions focuses on generating a single type of reactive motion for a given sequence which results in a lack of variety of the resultant motions. In this paper, we propose a novel way to create realistic human reactive motions which are not presented in the given dataset by mixing and matching different types of close interactions. We propose a Conditional Hierarchical Generative Adversarial Network with Multi‐Hot Class Embedding to generate the Mix and Match reactive motions of the follower from a given motion sequence of the leader. Experiments are conducted on both noisy (depth‐based) and high‐quality (MoCap‐based) interaction datasets. The quantitative and qualitative results show that our approach outperforms the state‐of‐the‐art methods on the given datasets. We also provide an augmented dataset with realistic reactive motions to stimulate future research in this area.

Lamport's celebrated Paxos consensus protocol is generally viewed as a complex hard-to-understand algorithm. Notwithstanding its complexity, in this paper, we take a step towards automatically proving the safety of Paxos by taking advantage of three structural features in its specification: spatial regularity in its unordered domains, temporal regularity in its totally-ordered domain, and its hierarchical composition. By carefully integrating these structural features in IC3PO, a novel model checking algorithm, we were able to infer an inductive invariant that identically matches the human-written one previously derived with significant manual effort using interactive theorem proving. While various attempts have been made to verify different versions of Paxos, to the best of our knowledge, this is the first demonstration of an automatically-inferred inductive invariant for Lamport's original Paxos specification. We note that these structural features are not specific to Paxos and that IC3PO can serve as an automatic general-purpose protocol verification tool.

Synthesis of long-term human motion skeleton sequences is essential to aid human-centric video generation [8] with potential applications in Augmented Reality, 3D character animations, pedestrian trajectory prediction, etc. Long-term human motion synthesis is a challenging task due to multiple factors like, long-term temporal dependencies among poses, cyclic repetition across poses, bi-directional and multi-scale dependencies among poses, variable speed of actions, and a large as well as partially overlapping space of temporal pose variations across multiple class/types of human activities. This paper aims to address these challenges to synthesize a long-term (> 6000 ms) human motion trajectory across a large variety of human activity classes (> 50). We propose a two-stage activity generation method to achieve this goal, where the first stage deals with learning the long-term global pose dependencies in activity sequences by learning to synthesize a sparse motion trajectory while the second stage addresses the generation of dense motion trajectories taking the output of the first stage. We demonstrate the superiority of the proposed method over SOTA methods using various quantitative evaluation metrics on publicly available datasets.

We present AVR, a push-button model checker for verifying state transition systems directly at the source-code level. AVR uses information embedded in the word-level syntax of the design representation to automatically perform scalable model checking by combining a novel syntax-guided abstraction-refinement technique with a word-level implementation of the IC3 algorithm. AVR provides independently-verifiable certificates that offer provable assurance and are easy to relate to the word-level system. Moreover, proof certificates can be further used in innovative ways to extract key design information and are useful in a growing number of applications.

IC3-based algorithms have emerged as effective scalable approaches for hardware model checking. In this paper we evaluate six implementations of IC3-based model checkers on a diverse set of publicly-available and proprietary industrial Verilog RTL designs. Four of the six verifiers we examined operate at the bit level and two employ abstraction to take advantage of word-level RTL semantics. Overall, the word-level verifier employing data abstraction outperformed the others, especially on the large industrial designs. The analysis helped us identify several key insights on the techniques underlying these tools, their strengths and weaknesses, differences and commonalities, and opportunities for improvement.

Distributed systems are notoriously difficult to design and implement correctly. Formal verification provides correctness proofs, and has recently been successfully applied to various distributed systems. At the heart of a typical formal verification is a computer-checked proof with an inductive invariant. Finding this inductive invariant is the hardest part of the proof: a part that is currently undertaken manually by the developer and is responsible for most of the effort associated with formal verification.

Designing and implementing distributed systems correctly is a very challenging task. Recently, formal verification has been successfully used to prove the correctness of distributed systems. At the heart of formal verification lies a computer-checked proof with an inductive invariant. Finding this inductive invariant, however, is the most difficult part of the proof. Alas, current proof techniques require inductive invariants to be found manually---and painstakingly---by the developer.

We describe a timing analysis engine for efficient processing of incremental changes to a circuit. The engine uses a block-based approach for incremental slack propagation. Logic cones affected by incremental changes to the design are identified and used to restrict the scope of the computation. Incremental block-based clock-pessimism removal and reporting of worst paths in the circuit is implemented using a novel dynamic path reduction technique. The engine is very efficient in memory usage compared to other known academic timers while maintaining a very high accuracy of reported path slacks when compared to a standard industrial timing engine. Certain paths are intentionally omitted from reporting in order to save on runtime, while ensuring that all paths with highest criticality are covered. Our timer (iitRACE) placed overall third in TAU 2015 contest on incremental timing analysis. Experimental results on industrial benchmarks from TAU 2015 contest have justified that iitRACE has average memory requirement 2X and 30X lower than that of first and second place timers respectively.

Automatic semantic annotation of structured data enables unsupervised integration of data from heterogeneous sources but is difficult to perform accurately due to the presence of many numeric fields and proper-noun fields that do not allow reference-based approaches and the absence of natural language text that prevents the use of language-based approaches. In addition, several of these semantic types have multiple heterogeneous representations, while sharing syntactic structure with other types. In this work, we propose a new approach to use conditional random fields (CRFs) to perform semantic annotation of structured data that takes advantage of the structure and labels of the tokens for higher accuracy of field labeling, while still allowing the use of exact inference techniques. We compare our approach with a linear-CRF based model that only labels fields and also with a regular-expression based approach.