arXiv subjects

William Fishell

Publications and source records attributed to William Fishell.

5 recordsLinked to original sources

arXiv2026

Learning GR(1) Specifications from Traces

Constrained specification mining enables the automatic discovery of desired properties from system traces. Generalized Reactivity of Rank 1, or GR(1), is a fragment of LTL with polynomial-time synthesis that natively encodes assume-guarantee properties present in most hardware and robotics domains. In this paper, we present GR1MINE, a SAT-based tool for efficiently learning GR(1) formulas from examples. We exploit the GR(1) temporal skeleton to incrementally enumerate formula candidates, leveraging learnt clauses to avoid recomputation. On the Boolean GR(1) Syntech suite, GR1MINE learns a realizable formula for all 60 benchmarks over 30X faster than generic and constrained LTL mining tools. On non-GR(1) specifications from SYNTCOMP, GR1MINE is still able to recover >2X more realizable specifications than baselines within the timeout.

cs.LO↗

arXiv2026

Warm Starting State-Space Models with Automata Learning

We prove that Moore machines can be exactly realized as state-space models (SSMs), establishing a formal correspondence between symbolic automata and these continuous machine learning architectures. These Moore-SSMs preserve both the complete symbolic structure and input-output behavior of the original Moore machine, but operate in Euclidean space. With this correspondence, we compare the training of SSMs with both passive and active automata learning. In recovering automata from the SYNTCOMP benchmark, we show that SSMs require orders of magnitude more data than symbolic methods and fail to learn state structure. This suggests that symbolic structure provides a strong inductive bias for learning these systems. We leverage this insight to combine the strengths of both automata learning and SSMs in order to learn complex systems efficiently. We learn an adaptive arbitration policy on a suite of arbiters from SYNTCOMP and show that initializing SSMs with symbolically-learned approximations learn both faster and better. We see 2-5 times faster convergence compared to randomly initialized models and better overall model accuracies on test data. Our work lifts automata learning out of purely discrete spaces, enabling principled exploitation of symbolic structure in continuous domains for efficiently learning in complex settings.

cs.LG↗

arXiv2026

Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications

Mining specifications from execution traces presents an automated way of capturing characteristic system behaviors. However, existing approaches are largely restricted to Boolean abstractions of events, limiting their ability to express data-aware properties. In this paper, we extend mining procedures to operate over richer datatypes. We first establish candidate functions in our domain that cover the set of traces by leveraging Syntax Guided Synthesis (SyGuS) techniques. To capture these function applications temporally, we formalize the semantics of TSL$_f$, a finite-prefix interpretation of Temporal Stream Logic (TSL) that extends LTL$_f$ with support for first-order predicates and functional updates. This allows us to unify a corresponding procedure for learning the data transformations and temporal specifications of a system. We demonstrate our approach synthesizing reactive programs from mined specifications on the OpenAI-Gymnasium ToyText environments, finding that our method is more robust and orders of magnitude more sample-efficient than passive learning baselines on generalized problem instances.

cs.LO↗

arXiv2025

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoning, but underperform at counterfactual causal understanding and reasoning. We introduce TempoBench, the first formally verifiable temporal benchmark that isolates counterfactual causal attribution over execution trajectories, and we show that LLMs categorically fall back to brute-force simulation-based reasoning to solve causal reasoning problems. Built from synthesized deterministic Mealy machines, TempoBench provides an infinitely scalable corpus of trajectory-based causal reasoning problems with controllable complexity and provably correct causal labels. Frontier models reach 96% step accuracy simulating a system forward, and fall to 32% when asked which inputs were necessary for an observed output, displaying what we call the SIM/MIN gap. Our findings show that LLMs cannot reliably identify minimal necessary causes, often confusing ``possible inputs'' with ``necessary causes,'' demonstrating an inability to understand which inputs were not needed. This failure is critical for deployment in causal inference tasks such as debugging, root cause analysis, and task planning where agents must use counterfactual reasoning to plan for specific desired outcomes. We show that training on TempoBench yields a targeted gain on causal benchmarks in open-source models while matching general-purpose, math, and code reasoning datasets on standard benchmarks. This indicates counterfactual causal reasoning is a learnable, architecturally distinct capability that sits on top of existing reasoning competencies.

cs.AI↗

arXiv2025

Research Vision: Multi-Agent Path Planning for Cops And Robbers Via Reactive Synthesis

We propose the problem of multi-agent path planning for a generalization of the classic Cops and Robbers game via reactive synthesis. Specifically, through the application of LTLt and Coordination Synthesis, we aim to check whether various Cops and Robbers games are realizable (a strategy exists for the cops which guarantees they catch the robbers). Additionally, we construct this strategy as an executable program for the multiple system players in our games. In this paper we formalize the problem space, and propose potential directions for solutions. We also show how our formalization of this generalized cops and robbers game can be mapped to a broad range of other problems in the reactive program synthesis space.

cs.LO↗