{"ID":5938023,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T18:00:19.551824075Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03974","arxiv_id":"2607.03974","title":"TRACER: Early Failure Detection for Task-Oriented Dialogue","abstract":"Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present TRACER, a method for early failure detection in task-oriented dialogue. TRACER predicts from a partial dialogue whether the full conversation will eventually fail by combining simple trajectory signals from belief-state changes with text representations of the evolving dialogue state. We evaluate the method in both oracle and generated belief-state settings, and test how well it works when only 25%, 50%, 75%, or 100% of the dialogue is visible. Across these settings, TRACER detects useful failure signals well before the end of the conversation and outperforms heuristic, classical, and single-stream baselines. These results suggest that early failure detection can provide a practical warning signal for dialogue systems before the interaction fully breaks down.","short_abstract":"Task-oriented dialogue systems often fail before the final breakdown is obvious, but most evaluation only measures failure after the conversation has already gone wrong. We present TRACER, a method for early failure detection in task-oriented dialogue. TRACER predicts from a partial dialogue whether the full conversati...","url_abs":"https://arxiv.org/abs/2607.03974","url_pdf":"https://arxiv.org/pdf/2607.03974v1","authors":"[\"Erfan Nourbakhsh\",\"Rocky Slavin\",\"Ke Yang\",\"Anthony Rios\"]","published":"2026-07-04T18:12:25Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false}