@inproceedings{lin-etal-2026-reactod,
title = "{R}eac{TOD}: Bounded Neuro-Symbolic Agentic {NLU} for Zero-Shot Dialogue State Tracking",
author = "Lin, Yanjun and
Xiao, Zimo and
Natarajan, Kartik and
Sankaranarayanan, Mahesh and
Nawanit, Niraj and
Parashar, Rakshit and
Zhang, Austin and
Konaraddi, Karthik and
Mote, Rishita and
Niu, Wei",
editor = "Chang, Kai-Wei and
Mehrabi, Ninareh and
Krishna, Satyapriya and
Das, Anubrata and
Dhamala, Jwala and
Cao, Yang Trista and
Kumarage, Tharindu and
Ramakrishna, Anil and
Christodoulopoulos, Christos and
Wan, Yixin and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul",
booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.trustnlp-main.21/",
pages = "342--352",
ISBN = "979-8-89176-418-7",
abstract = "Task-oriented dialogue systems{---}handling transactions, reservations, and service requests{---}require predictable behavior, yet the moderately-sized LLMs needed for practical latency are prone to hallucination and format errors that cascade into incorrect actions (e.g., a hotel booked for the wrong date). We propose ReacTOD, a bounded neuro-symbolic architecture that reformulates NLU as discrete tool calls within a self-correcting ReAct loop governed by deterministic validation. A bounded ReAct loop enables iterative self-correction, improving accuracy by up to 9.3 percentage points over single-pass inference on MultiWOZ. A symbolic validator enforces action compliance, schema conformance, and coreference consistency on every dialogue state update, achieving a 93.1{\%} self-correction rate on intercepted errors and producing structured execution traces. Incremental state prediction and on-demand history retrieval keep prompts compact, empirically improving instruction adherence in parameter-constrained models. On MultiWOZ 2.1, ReacTOD achieves a new zero-shot state-of-the-art: gpt-oss-20B reaches 52.71{\%} joint goal accuracy, surpassing the previous best by 14 percentage points, while Qwen3-8B achieves 47.34{\%} with only 8B parameters. On the Schema-Guided Dialogue (SGD) benchmark, ReacTOD with Claude-Opus-4.6 achieves 80.68{\%} JGA under fully end-to-end evaluation with predicted domains, and Qwen3-32B reaches 64.09{\%}{---}demonstrating cross-benchmark generalization without task-specific training data."
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<abstract>Task-oriented dialogue systems—handling transactions, reservations, and service requests—require predictable behavior, yet the moderately-sized LLMs needed for practical latency are prone to hallucination and format errors that cascade into incorrect actions (e.g., a hotel booked for the wrong date). We propose ReacTOD, a bounded neuro-symbolic architecture that reformulates NLU as discrete tool calls within a self-correcting ReAct loop governed by deterministic validation. A bounded ReAct loop enables iterative self-correction, improving accuracy by up to 9.3 percentage points over single-pass inference on MultiWOZ. A symbolic validator enforces action compliance, schema conformance, and coreference consistency on every dialogue state update, achieving a 93.1% self-correction rate on intercepted errors and producing structured execution traces. Incremental state prediction and on-demand history retrieval keep prompts compact, empirically improving instruction adherence in parameter-constrained models. On MultiWOZ 2.1, ReacTOD achieves a new zero-shot state-of-the-art: gpt-oss-20B reaches 52.71% joint goal accuracy, surpassing the previous best by 14 percentage points, while Qwen3-8B achieves 47.34% with only 8B parameters. On the Schema-Guided Dialogue (SGD) benchmark, ReacTOD with Claude-Opus-4.6 achieves 80.68% JGA under fully end-to-end evaluation with predicted domains, and Qwen3-32B reaches 64.09%—demonstrating cross-benchmark generalization without task-specific training data.</abstract>
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%0 Conference Proceedings
%T ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
%A Lin, Yanjun
%A Xiao, Zimo
%A Natarajan, Kartik
%A Sankaranarayanan, Mahesh
%A Nawanit, Niraj
%A Parashar, Rakshit
%A Zhang, Austin
%A Konaraddi, Karthik
%A Mote, Rishita
%A Niu, Wei
%Y Chang, Kai-Wei
%Y Mehrabi, Ninareh
%Y Krishna, Satyapriya
%Y Das, Anubrata
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%Y Kumarage, Tharindu
%Y Ramakrishna, Anil
%Y Christodoulopoulos, Christos
%Y Wan, Yixin
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%S Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-418-7
%F lin-etal-2026-reactod
%X Task-oriented dialogue systems—handling transactions, reservations, and service requests—require predictable behavior, yet the moderately-sized LLMs needed for practical latency are prone to hallucination and format errors that cascade into incorrect actions (e.g., a hotel booked for the wrong date). We propose ReacTOD, a bounded neuro-symbolic architecture that reformulates NLU as discrete tool calls within a self-correcting ReAct loop governed by deterministic validation. A bounded ReAct loop enables iterative self-correction, improving accuracy by up to 9.3 percentage points over single-pass inference on MultiWOZ. A symbolic validator enforces action compliance, schema conformance, and coreference consistency on every dialogue state update, achieving a 93.1% self-correction rate on intercepted errors and producing structured execution traces. Incremental state prediction and on-demand history retrieval keep prompts compact, empirically improving instruction adherence in parameter-constrained models. On MultiWOZ 2.1, ReacTOD achieves a new zero-shot state-of-the-art: gpt-oss-20B reaches 52.71% joint goal accuracy, surpassing the previous best by 14 percentage points, while Qwen3-8B achieves 47.34% with only 8B parameters. On the Schema-Guided Dialogue (SGD) benchmark, ReacTOD with Claude-Opus-4.6 achieves 80.68% JGA under fully end-to-end evaluation with predicted domains, and Qwen3-32B reaches 64.09%—demonstrating cross-benchmark generalization without task-specific training data.
%U https://aclanthology.org/2026.trustnlp-main.21/
%P 342-352
Markdown (Informal)
[ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking](https://aclanthology.org/2026.trustnlp-main.21/) (Lin et al., TrustNLP 2026)
ACL
- Yanjun Lin, Zimo Xiao, Kartik Natarajan, Mahesh Sankaranarayanan, Niraj Nawanit, Rakshit Parashar, Austin Zhang, Karthik Konaraddi, Rishita Mote, and Wei Niu. 2026. ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking. In Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026), pages 342–352, San Diego, California. Association for Computational Linguistics.