Emotional Support Conversation (ESC) systems aim to provide holistic support by balancing professional therapeutic competence with natural empathy. However, existing methods struggle to simultaneously achieve structured, stage-aware reasoning and seamless empathy-expertise alignment, often resulting in an artificial splicing of clinical strategies and generic reassurance. To overcome these limitations, we propose ESCRAG-R1, a unified framework that integrates retrieval-based psychological guidance into Group Relative Policy Optimization (GRPO). By incorporating retrieval into the reinforcement learning loop, ESCRAG-R1 transforms external knowledge into a robust learning signal that stimulates explicit internal reasoning prior to generation and fundamentally reshapes the model’s internal policy. To provide the reliable supervision required for this optimization, we construct ESC-Preference, a high-quality dataset based on a Client–Counselor–Judge evaluation framework that delivers precise, empathy-aware reward signals. Extensive experiments demonstrate that ESCRAG-R1 significantly outperforms existing baselines by mitigating superficial splicing and realizing a natural integration of professional guidance and empathetic expression. Code and datasets will be publicly released at https://anonymous.4open.science/r/ESCRAG-R1-83F1/.

LLM agents rely on long-term interaction histories to tailor their responses to individual users. However, historical personalization signals may become stale or misaligned with expectations implied by the current context, leading agents to follow outdated preferences during reasoning. We refer to this failure mode as personalization inertia, which shifts the agent’s behavior away from the user’s current intent. Existing methods mainly optimize final responses or memory retrieval, leaving such failures underexplored. In this paper, we propose Personalization Inertia VerificatiOn and RegulaTion (Pivot), a framework for mitigating personalization inertia in long-term interactions with agents. Concretely, we construct a trajectory-independent evidence condition as a diagnostic reference, model prefix-evidence consistency to assess compatibility with the current personalization evidence, and learn a rollback policy to determine whether and where to intervene. The selected intervention triggers local repair, preserving the reliable prefix while regenerating only the potentially inertia-affected suffix under evidence-guided constraints. Experiments on benchmark datasets demonstrate consistent gains in personalization accuracy and trajectory faithfulness with targeted intervention overhead.

In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still violate logical or evidential constraints, allowing unsupported beliefs repeatedly stored and propagated across decision steps, leading to systematic behavioral drift in long-horizon agentic systems. Most existing strategies rely on the consensus mechanism, conflating agreement with faithfulness. In this paper, inspired by the vulnerability of unfaithful intermediate reasoning trajectories, we propose Self-Audited Verified Reasoning (SAVeR), a novel framework that enforces verification over internal belief states within the agent before action commitment, achieving faithful reasoning. Concretely, we structurally generate persona-based diverse candidate beliefs for selection under a faithfulness-relevant structure space. To achieve reasoning faithfulness, we perform adversarial auditing to localize violations and repair through constraint-guided minimal interventions under verifiable acceptance criteria. Extensive experiments on six benchmark datasets demonstrate that our approach consistently improves reasoning faithfulness while preserving competitive end-task performance.