EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management

• URL: http://arxiv.org/abs/2606.15532v1
• Date: Sun, 14 Jun 2026 01:22:03 GMT
• Title: EIBench: A Simulator-Based Benchmark and Turn-Credit RL for Emotion Management
• Abstract summary: We introduce EIBench, a simulator-based benchmark for interactive emotion management.<n> EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing.<n>Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure.
• Score: 78.71074071187753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive: a good model should not only recognize a user's emotion, but also improve the user's emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2x2 taxonomy covering Support, Defense, Repair, and Charm, which together capture different forms of support, boundary maintenance, trust repair, and rapport building. In each scenario, an LLM simulator plays the user, updates an emotion-relation state after each turn, and maps the final state to an anchor-based score. This design makes EIBench both an evaluation benchmark and a training environment: the final state gives the outcome reward, while the per-turn state updates provide dense feedback for RL. We evaluate 15 open- and closed-source LLMs. Current models perform well on support and rapport-building scenes, but struggle with boundary maintenance under user pressure. To improve the EI ability of LLMs, we propose Centered Turn-Credit GRPO (CTC-GRPO), a GRPO extension that reuses the simulator's per-turn state updates as dense turn-level feedback while preserving the final outcome reward. CTC-GRPO improves Qwen3-8B from -22.4 to +22.4 on EIBench and also improves on out-of-distribution evaluations including SAGE (+12.4) and EQBench3 (+20.9%). Our results show that simulator-tracked user states can support both evaluation and training for multi-turn emotion management.

Related papers

• Disclosure-Gated User Simulation for Companion-Agent Evaluation [14.962552394114894]
  We use a large language model to play the user and train a user simulator against that specification.<n>Gating behaviour is learned from the training corpus's synthetic branch, while the real branch supplies how people speak and react.<n>We state two acceptance criteria: a ranking must be order-preserving, and absolute scores must be scale-stable.
  arXiv  Detail & Related papers  (2026-09-01T09:39:41Z)
• Reinforcing Human Behavior Simulation via Verbal Feedback [105.68319269895653]
  We present DITTO, a model trained by treating verbal feedback as a first-class signal in reinforcement learning.<n>We also introduce SOUL, a unified benchmark and training data suite spanning 10 tasks across six categories: Theory of Mind, character role play, social skill, learner simulation, user simulation, and persona simulation.<n>DitTO achieves an average 36% improvement over the base model and exceeds GPT-5.4 on 6 of 10 SOUL benchmarks.
  arXiv  Detail & Related papers  (2026-05-19T21:23:24Z)
• EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents [3.0301675282070577]
  EVA-Bench is an end-to-end evaluation framework for voice agents.<n>It orchestrates bot-to-bot audio conversations over dynamic multi-turn dialogues.<n>It captures task completion, faithfulness, and audio-level speech fidelity.<n>It also captures conversation progression, spoken conciseness, and turn-taking timing.
  arXiv  Detail & Related papers  (2026-05-13T17:58:52Z)
• Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators [55.617099475539305]
  Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI tutors and human educators.<n>Yet such simulators are typically evaluated by output similarity to real students, not by whether they behave like students with coherent misconceptions during interaction.<n>We introduce a controlled framework for evaluating misconception faithfulness, whether a simulator maintains a misconception-driven belief state and updates selectively when feedback addresses the underlying misconception.
  arXiv  Detail & Related papers  (2026-05-12T20:55:23Z)
• Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents [0.0]
  RLVER has produced language models with strong empathetic performance, evaluated on benchmarks that assume cooperative, honest users.<n>We construct the Adversarial Empathy Benchmark AEB and introduce the Emotional Consistency Score ECS to evaluate empathetic robustness under adversarial conditions.<n>We interpret the ECS--FS (Final Score) gap as a behavioral/legibility dissociation inside this simulator family, not as evidence about internal understanding or clinical readiness.
  arXiv  Detail & Related papers  (2026-05-08T02:14:12Z)
• SimEval-IR: A Unified Toolkit and Benchmark Suite for Evaluating User Simulators and Search Sessions [1.1105673928718571]
  We present SimEval-IR, an open-source toolkit and benchmark suite that makes the distinction measurable.<n>SimEval-IR provides: (1) a canonical session schema unifying session search and conversational interactions, with validated dataset adapters and explicit loss accounting; (2) three executable benchmarks covering behavioral realism, tester reliability with RATE-style estimation, and an analysis linking the two; and (3) baseline results across four real datasets in two languages and four simulator families.
  arXiv  Detail & Related papers  (2026-04-30T13:56:18Z)
• SimulatorArena: Are User Simulators Reliable Proxies for Multi-Turn Evaluation of AI Assistants? [61.07963107032645]
  Large language models (LLMs) are increasingly used in interactive applications.<n>Human evaluation remains the gold standard for assessing their performance in multi-turn conversations.<n>We introduce SimulatorArena, a benchmark of 909 annotated human-LLM conversations on two interactive tasks.
  arXiv  Detail & Related papers  (2025-10-06T23:17:44Z)
• RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents [67.46032287312339]
  Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess.<n>We introduce RLVER, the first end-to-end reinforcement learning framework that leverages verifiable emotion rewards from simulated users.<n>Our results show that RLVER is a practical route toward emotionally intelligent and broadly capable language agents.
  arXiv  Detail & Related papers  (2025-07-03T18:33:18Z)
• Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems [80.77917437785773]
  Task-oriented dialogue systems ( TDSs) are assessed mainly in an offline setting or through human evaluation. We propose a metaphorical user simulator for end-to-end TDS evaluation, where we define a simulator to be metaphorical if it simulates user's analogical thinking in interactions with systems. We also propose a tester-based evaluation framework to generate variants, i.e., dialogue systems with different capabilities.
  arXiv  Detail & Related papers  (2022-04-02T05:11:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.