AlpsBench supplies 2500 real-dialogue sequences with verified memories to benchmark LLM extraction, updating, retrieval, and utilization of personalized information.
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A survey of personalized large language models: Progress and future directions
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Test-time scaling for personalized LLMs follows a logarithmic utility curve under oracle selection but standard reward models suffer user-level collapse and query-level hacking; a probabilistic reward model with learned variance enables consistent scaling.
Personalized LLM-generated plain language summaries improve lay readers' comprehension and quality ratings but increase risks of reinforcing biases and introducing hallucinations compared to static expert summaries.
RUMS selects LLM user memory via mutual information with model outputs to reduce response uncertainty, outperforming similarity-based methods in human alignment and response quality with up to 95% lower cost.
The work develops a reflective LLM-based storytelling agent for older adults that integrates argumentation schemes and argument mining with knowledge graphs and user modeling to generate and inspect personalized health narratives, evaluated through expert design and user studies showing recognition,
An extended annotation scheme with new categories and attributes plus a Gemma-300M-based multi-head classifier achieves 81.6% macro F1 on personal fact classification, outperforming few-shot LLM baselines by nearly 9 points with lower compute.
A survey that introduces a taxonomy for LLM-based conversational user simulation, analyzes core techniques and evaluation methods, and identifies open challenges in the field.
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
P-MLLM augments a frozen LLM with selective fusion modules to incorporate visual information in a profile-conditioned manner for competitive zero-shot PIAA performance.
TIPO applies preference-intensity weighting and padding gating to stabilize preference optimization for privacy personalization in mobile GUI agents, yielding higher alignment and distinction metrics than prior methods.
TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
PersonaVLM adds memory extraction, multi-turn retrieval-based reasoning, and personality inference to multimodal LLMs, yielding 22.4% gains on a new long-term personalization benchmark and outperforming GPT-4o.
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
Personalization in an agentic RAG advising system boosts reasoning quality and grounding while reducing semantic metric scores due to the inability of current metrics to accommodate user-specific responses.
The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent personalization.
citing papers explorer
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AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment
AlpsBench supplies 2500 real-dialogue sequences with verified memories to benchmark LLM extraction, updating, retrieval, and utilization of personalized information.
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Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures
Test-time scaling for personalized LLMs follows a logarithmic utility curve under oracle selection but standard reward models suffer user-level collapse and query-level hacking; a probabilistic reward model with learned variance enables consistent scaling.
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ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
Personalized LLM-generated plain language summaries improve lay readers' comprehension and quality ratings but increase risks of reinforcing biases and introducing hallucinations compared to static expert summaries.
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Response-Aware User Memory Selection for LLM Personalization
RUMS selects LLM user memory via mutual information with model outputs to reduce response uncertainty, outperforming similarity-based methods in human alignment and response quality with up to 95% lower cost.
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A Reflective Storytelling Agent for Older Adults: Integrating Argumentation Schemes and Argument Mining in LLM-Based Personalised Narratives
The work develops a reflective LLM-based storytelling agent for older adults that integrates argumentation schemes and argument mining with knowledge graphs and user modeling to generate and inspect personalized health narratives, evaluated through expert design and user studies showing recognition,
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An Annotation Scheme and Classifier for Personal Facts in Dialogue
An extended annotation scheme with new categories and attributes plus a Gemma-300M-based multi-head classifier achieves 81.6% macro F1 on personal fact classification, outperforming few-shot LLM baselines by nearly 9 points with lower compute.
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A Survey on LLM-based Conversational User Simulation
A survey that introduces a taxonomy for LLM-based conversational user simulation, analyzes core techniques and evaluation methods, and identifies open challenges in the field.
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Alignment has a Fantasia Problem
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
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Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
P-MLLM augments a frozen LLM with selective fusion modules to incorporate visual information in a profile-conditioned manner for competitive zero-shot PIAA performance.
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Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization
TIPO applies preference-intensity weighting and padding gating to stabilize preference optimization for privacy personalization in mobile GUI agents, yielding higher alignment and distinction metrics than prior methods.
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TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
TSUBASA improves long-horizon personalization in LLMs via dynamic memory evolution for writing and context-distillation self-learning for reading, outperforming Mem0 and Memory-R1 on Qwen-3 benchmarks while reducing token use.
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PersonaVLM: Long-Term Personalized Multimodal LLMs
PersonaVLM adds memory extraction, multi-turn retrieval-based reasoning, and personality inference to multimodal LLMs, yielding 22.4% gains on a new long-term personalization benchmark and outperforming GPT-4o.
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AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
13 participants became convinced AI understands human values after chatbot interactions evaluated with the VAPT toolkit.
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The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&A
Personalization in an agentic RAG advising system boosts reasoning quality and grounding while reducing semantic metric scores due to the inability of current metrics to accommodate user-specific responses.
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Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems
The authors propose a conceptual framework integrating stakeholder-LLM alignment methods, social choice-based aggregation for collective decisions, and stakeholder-centric evaluations to achieve fair multi-agent personalization.