Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a task. We develop an empirical audit protocol for structured intermediate outputs: auditing dataset shortcuts, isolating bundled prompt changes, testing answer-associated labels and decomposed evidence, and retaining provider-level execution failures. A 480-example synthetic development set initially suggested large gains from a state-structured prompt bundle, but TF-IDF diagnostics showed lexical separability and no positive standalone Ignore cases. We therefore build a frozen 160-example controlled counterfactual set with 40 matched four-way families and rule-derived reference policies. There, exposing four state definitions improves accuracy, but adding an isolated explicit state-output field does not significantly improve policy accuracy for Llama-3.3-70B and gives only a marginal, non-significant gain for GPT-OSS-120B. Supplying benchmark-associated state labels shifts policy predictions; because those labels deterministically map to policies, this is label-conditioning, not evidence of a faithful mechanism. Family-level and seed-stability analyses show that example accuracy overstates counterfactual consistency. A decomposed semantic-evidence follow-up does not improve routing for the cleanly evaluated endpoint, while the GPT-OSS condition fails provider-side request validation. We evaluate policy classification only.

Recent progress in large vision-language models (LVLMs) has shown substantial potential across a broad spectrum of third-person tasks. However, adapting these LVLMs to egocentric scenarios remains challenging due to their third-person training bias. Existing methods that adapt LVLMs for first-person tasks often overlook critical agent-environment interactions, limiting their ability to perform egocentric reasoning. To address these challenges, we propose a novel zero-shot paradigm termed Front-Door Adjustments with Uncertainty Calibration (FRUIT) to enhance the egocentric reasoning abilities of LVLMs by simulating human causal reasoning. Specifically, the FRUIT operates in two stages: observation and understanding. Unlike conventional prompting techniques, we formalize egocentric reasoning using a structural causal model. Then, we ground interaction regions and expand them into hierarchical visual cues, augmented with corresponding captions, to form the initial observations. To reduce noise in these observations, we employ uncertainty calibration to filter out unreliable information. These refined observations as mediators are then incorporated into the prompt template, guiding the model to understand semantics from a first-person perspective. Extensive experiments conducted on the EgoThink benchmark demonstrate that our FRUIT method consistently enhances the performance of existing LVLMs on six distinct tasks. Our code is available at https://github.com/Mrshenshen/FRUIT.

As large language models (LLMs) become widely adopted, ensuring their alignment with human values is crucial to prevent jailbreaks where adversaries manipulate models to produce harmful content. While most defenses target single-turn attacks, real-world usage often involves multi-turn dialogues, exposing models to attacks that exploit conversational context to bypass safety measures. We introduce MUSE, a comprehensive framework tackling multi-turn jailbreaks from both attack and defense angles. For attacks, we propose MUSE-A, a method that uses frame semantics and heuristic tree search to explore diverse semantic trajectories. For defense, we present MUSE-D, a fine-grained safety alignment approach that intervenes early in dialogues to reduce vulnerabilities. Extensive experiments on various models show that MUSE effectively identifies and mitigates multi-turn vulnerabilities. Code is available at https://anonymous.4open.science/r/MUSE-75F7.

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper, we decompose the language model confidence into the Uncertainty about the question and the Fidelity to the answer generated by language models. Then, we propose a plug-and-play method, UF Calibration, to estimate the confidence of language models. Our method has shown good calibration performance by conducting experiments with 6 RLHF-LMs on four MCQA datasets. Moreover, we propose two novel metrics, IPR and CE, to evaluate the calibration of the model, and we have conducted a detailed discussion on Truly Well-Calibrated Confidence for large language models. Our method could serve as a strong baseline, and we hope that this work will provide some insights into the model confidence calibration.