The paper evaluates three approaches for emotion recognition in conversation— a low‑cost stacked ensemble, an off‑the‑shelf LLM prompt, and a confidence‑gated hybrid that escalates only uncertain ensemble predictions to the LLM. Across three datasets (IEMOCAP, MELD, CMU‑MOSI), the hybrid consistently outperforms each pure system, achieving higher weighted F1 scores while routing most traffic through the inexpensive ensemble. This results in significant cost savings (≈$10‑85 per million utterances) and provides an interpretable escalation signal tied to emotion or sentiment shifts.
Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact-center-as-a-service (CCaaS) platforms, where co...
arXiv:2609.27606v1 Announce Type: new Abstract: We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM agents that must condition on live user state (game state, sessi...
arXiv:2607. 16712v1 Announce Type: new Abstract: We describe DS@GT's submission to the eRisk 2026 Task 1 challenge on conversational depression screening, in which systems interview LLM personas that simulate individuals with varying depression profiles and produce a Beck Depression Inventory II (BDI-II) score plus four key symptoms per persona, without directly asking sensitive mental health questions.
arXiv:2606. 29495v1 Announce Type: new Abstract: Social influence dialogue changes user behavior by altering internal cognitive states.
arXiv:2606. 24941v2 Announce Type: replace-cross Abstract: Reviewing recorded interviews for affective cues such as composure and agitation is slow and subjective, and cloud services that could automate the task require sensitive audio to leave the device.
arXiv:2609.14570v1 Announce Type: cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We st...
Social influence dialogue changes user behavior by altering internal cognitive states. The central evaluation question is whether the user's beliefs, desires, intentions, and emotions measurably change over the course of conversation, a process-oriented criterion that neither surface-level text metrics (BLEU/ROUGE) nor single-score LLM judgments can capture.
The paper presents a layered framework for evaluating conversational AI by aligning offline proxy signals with online A/B experiment outcomes. It introduces a three‑step alignment chain—behavioral label to product outcome, classifier to candidate behavior, and offline signal to experiment effect—alongside an audit protocol that compares confidence intervals and rankings. In a real‑world deployment, the composite proxy achieved 81.1% F1 versus 34.3% for the raw classifier, correctly predicting direction on all 113 contrasts and enabling efficient prioritization of candidate models before costly online testing.
TalkFa introduces a unified benchmark for Farsi dialogue generation and understanding, comprising three datasets: WIKI‑FADIAL (4.2K Wikipedia‑grounded dialogues), DAILYDIALOG‑FA (6.6K dialogues with dialogue‑act and emotion annotations), and PLAYDIAL‑FA (2.1K theatrical dialogues with sentiment labels). All dialogues are curated through multi‑stage review by native speakers, ensuring high quality. Experiments show that LoRA fine‑tuning improves generation performance with less data, while specific models excel on classification tasks, and human evaluation confirms the benchmark’s reliability.
SAGE (State‑Grounded, Abstention‑Aware Evaluation) is a new framework for assessing task‑oriented dialogue agents that focuses on whether each turn correctly advances the workflow state rather than just the quality of the reply. It compiles workflow specifications and per‑turn state differences into schema‑grounded criteria, then evaluates them through a cascade of symbolic rules and encoder/NLI verifiers that abstain instead of guessing, producing a turn‑level decision with an evidence trace. In experiments across MultiWOZ, Schema‑Guided Dialogue, and ABCD datasets, SAGE‑Core—using only symbolic rules and on‑device encoders—outperforms all evaluated LLM‑based judges, including GPT‑4.1 variants, while incurring zero paid LLM cost.
arXiv:2601.14172v4 Announce Type: replace-cross Abstract: We study neural multi-label classification under severe label imbalance through sentence-level detection of the 19 refined Schwartz human val...
The paper introduces CAPA, a Collaborative Agent Predictive Architecture designed to give large language model (LLM) agents situational awareness in online meetings. CAPA uses a Perceiver to update meeting state, a Predictor to forecast conversation flow, a Controller to decide speaking actions, and a Generator to phrase contributions. Evaluated on 137 AMI meetings, CAPA reduces the silence rate from 51.4% to 2.5%, doubles credited recovery, and maintains low hallucination, demonstrating that structured state tracking is key to effective delegation.