BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Events - ECPv6.18.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Events
X-ORIGINAL-URL:https://live-events-ucsc.pantheonsite.io
X-WR-CALDESC:Events for Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20261030T103000
DTEND;TZID=America/Los_Angeles:20261030T120000
DTSTAMP:20261009T184943Z
CREATED:20261009T184943Z
LAST-MODIFIED:20261009T184943Z
UID:10017768-1793356200-1793361600@live-events-ucsc.pantheonsite.io
SUMMARY:ECE Special Seminar: "Bilevel Optimization for Agentic AI: From Theory to Retrieval-augmented Agentic Reinforcement Learning"
DESCRIPTION:Presented by: Quan Xiao\, PhD Candidate @ Cornell University \nDescription: “Agentic AI systems combine large language models with system-level design choices such as post-training data selection\, retrieval\, tool use\, and memory management in augmented inference. Training these systems involves multiple coupled objectives beyond task success\, including safety\, retrieval quality\, and adaptation efficiency. Sequential tuning can cause catastrophic forgetting\, while direct objective mixing training can introduce unit mismatch and obscure credit assignments across components. \nIn this talk\, I will present bilevel optimization as a framework for jointly learning LLM parameters and agentic-system design variables. I will introduce an efficient first-order penalty formulation that replaces lower-level optimality with a merit constraint\, avoiding Hessian inverses and differentiation through full inner trajectories. By turning the bilevel problem into a penalized objective with analyzable structure\, this formulation enables landscape analysis that leads to global convergence guarantees. \nI will conclude with an application to bilevel retrieval optimization for tool-using agents\, where retrieval enables LLMs to access up-to-date information and manage external memory. In this setting\, landscape analysis also guides the design of memory-efficient variants of the penalty method tailored to the application. Experiments on multi-hop and medical question-answering tasks demonstrate the effectiveness of jointly optimizing retrieval and the LLM. Using only a 3B LLM backbone\, the proposed approach outperforms direct inference with GPT-5.6 Luna\, as well as methods based solely on agentic reinforcement learning or retrieval optimization.” \nBio: Quan Xiao (https://jenniferquanxiao.github.io) is a Ph.D. candidate in the Department of Electrical and Computer Engineering at Cornell University. She received her M.S. in Electrical Engineering from Rensselaer Polytechnic Institute in 2023\, and her B.S. in statistics from the University of Science and Technology of China in 2020. Her research focuses on the theoretical foundations of bilevel and multi-objective optimization\, as well as their applications to generative\, agentic AI and hardware-algorithm co-design. Her work has appeared in leading machine learning and signal processing venues\, including NeurIPS\, ICML\, and IEEE Transactions on Signal Processing\, with papers selected for oral and spotlight presentations. \nQuan has received several awards\, including the IEEE Signal Processing Society Scholarship in 2024 and the Belsky Award for Computational Sciences and Engineering in Rensselaer Polytechnic Institute in 2023. Her research has also received supports from industries\, including Google and Lambda Research. \n  \nHosted by: Professor Yu Zhang\, Electrical & Computer Engineering Department \nWhen: Friday\, October 30th from 10:30AM to 12:00PM \nLocation: Engineering 2\, Room 506
URL:https://live-events-ucsc.pantheonsite.io/event/ece-special-seminar-bilevel-optimization-for-agentic-ai-from-theory-to-retrieval-augmented-agentic-reinforcement-learning/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Seminars
ATTACH;FMTTYPE=image/jpeg:https://live-events-ucsc.pantheonsite.io/wp-content/uploads/2026/10/headshot_quan.jpg
GEO:37.0009723;-122.0632371
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Engineering 2 Engineering 2 1156 High Street Santa Cruz CA 95064;X-APPLE-RADIUS=500;X-TITLE=Engineering 2 1156 High Street:geo:-122.0632371,37.0009723
END:VEVENT
END:VCALENDAR