ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
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
Record details
Published: 18 May 2026
Source: arXiv
Category: Research
Topics: Privacy · Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents
arXiv · 18 May 2026
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
arXiv · 18 May 2026
From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG
arXiv · 18 May 2026
Synthesis and Evaluation of Long-term History-aware Medical Dialogue
arXiv · 19 May 2026
AI Agents May Always Fall for Prompt Injections
arXiv · 17 May 2026
Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system security
arXiv · 17 May 2026
How to cite this record
ethics.ai (18 May 2026), “ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking,” evidence record 4083, https://ethics.ai/record/4083 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.