arXiv subjects

Serdar Tasiran

Publications and source records attributed to Serdar Tasiran.

3 recordsLinked to original sources

arXiv2026

Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees

Language models (LMs) are increasingly used to interact with external services via programs written in domain-specific languages (DSLs). Unfortunately, since DSLs are often low-resource and esoteric, LMs frequently produce syntactically invalid programs in these languages. Grammar-constrained decoding can eliminate such failures, but requires syntactic constraints. These are usually in the form of a context-free grammar for the target language, an artifact that is hard to come by for third-party DSLs. In this work, we define an agent, called Autogrammar, that automatically learns context-free grammars from documentation and execution data. Autogrammar is formalized as a Kripke structure whose nondeterministic choices are resolved by a language model, enabling declarative control of agent behavior via linear temporal logic constraints. We evaluate four versions of Autogrammar on three DSLs (i.e., Amazon CloudWatch Logs Insights, Dynatrace Query Language, and Datadog Search Syntax) and find that it generates grammars that achieve near perfect precision on unseen data; that temporal restrictions reduce execution time by 3.8x without incurring statistically-significant loss in precision; that execution data is crucial while documentation is dispensable; and that grammar-constrained decoding using Autogrammar-generated grammars significantly improves end-to-end LM performance on eight out of ten real tasks, matching or exceeding the performance of a professionally-maintained grammar. In comparison, the context-free grammars generated by existing LM baselines and a state-of-the-art formal technique perform significantly worse over the same evaluation.

cs.PL↗

arXiv2018

Reasoning About TSO Programs Using Reduction and Abstraction

We present a method for proving that a program running under the Total Store Ordering (TSO) memory model is robust, i.e., all its TSO computations are equivalent to computations under the Sequential Consistency (SC) semantics. This method is inspired by Lipton's reduction theory for proving atomicity of concurrent programs. For programs which are not robust, we introduce an abstraction mechanism that allows to construct robust programs over-approximating their TSO semantics. This enables the use of proof methods designed for the SC semantics in proving invariants that hold on the TSO semantics of a non-robust program. These techniques have been evaluated on a large set of benchmarks using the infrastructure provided by CIVL, a generic tool for reasoning about concurrent programs under the SC semantics.

cs.LO↗

arXiv2008

Fast Monte Carlo Estimation of Timing Yield: Importance Sampling with Stochastic Logical Effort (ISLE)

In the nano era in integrated circuit fabrication technologies, the performance variability due to statistical process and circuit parameter variations is becoming more and more significant. Considerable effort has been expended in the EDA community during the past several years in trying to cope with the so-called statistical timing problem. Most of this effort has been aimed at generalizing the static timing analyzers to the statistical case. In this paper, we take a pragmatic approach in pursuit of making the Monte Carlo method for timing yield estimation practically feasible. The Monte Carlo method is widely used as a golden reference in assessing the accuracy of other timing yield estimation techniques. However, it is generally believed that it can not be used in practice for estimating timing yield as it requires too many costly full circuit simulations for acceptable accuracy. In this paper, we present a novel approach to constructing an improvedMonte Carlo estimator for timing yield which provides the same accuracy as the standard Monte Carlo estimator, but at a cost of much fewer full circuit simulations. This improved estimator is based on a novel combination of a variance reduction technique, importance sampling, and a stochastic generalization of the logical effort formalism for cheap but approximate delay estimation. The results we present demonstrate that our improved yield estimator achieves the same accuracy as the standard Monte Carlo estimator at a cost reduction reaching several orders of magnitude.

cs.OH↗