直播预告 | 俄亥俄州立大学朱志辉教授：通过上下文实现推理时学习
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TL;DR - A PaperWeekly livestream announcement for a talk by Ohio State assistant professor Zhihui Zhu (朱志辉) on "Inference-Time Learning Through Context," scheduled for Aug 8, 2026, 10:00–11:00 via Tencent Meeting (ID 398 029 567). It matters as a preview of a research agenda framing context as an actively constructed, evolving memory rather than passive input.
- Analyzes in-context learning from a geometric perspective, examining how task-relevant representations emerge and evolve across model layers without parameter updates.
- Proposes an "inference-time learning" paradigm where context is actively constructed and treated as dynamically evolving memory storing hypotheses, intermediate solutions, and feedback — drawing on iterative optimization frameworks like AlphaEvolve.
- Combines optimization theory with sequential Monte Carlo methods to propose a theoretically grounded framework for context design and updating.
- Content is an event promo only: no experimental results, benchmarks, or papers are provided, so technical claims are as-announced rather than demonstrated.
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直播预告 | 俄亥俄州立大学朱志辉教授：通过上下文实现推理时学习
TL;DR - A PaperWeekly livestream announcement for a talk by Ohio State assistant professor Zhihui Zhu (朱志辉) on "Inference-Time Learning Through Context," scheduled for Aug 8, 2026, 10:00–11:00 via Tencent Meeting (ID 398 029 567). It matters as a preview of a research agenda framing context as an actively constructed, evolving memory rather than passive input.
- Analyzes in-context learning from a geometric perspective, examining how task-relevant representations emerge and evolve across model layers without parameter updates.
- Proposes an "inference-time learning" paradigm where context is actively constructed and treated as dynamically evolving memory storing hypotheses, intermediate solutions, and feedback — drawing on iterative optimization frameworks like AlphaEvolve.
- Combines optimization theory with sequential Monte Carlo methods to propose a theoretically grounded framework for context design and updating.
- Content is an event promo only: no experimental results, benchmarks, or papers are provided, so technical claims are as-announced rather than demonstrated.