Papers by Zhiyu li

4 papers
CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026.acl-long)

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Challenge: Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes.
Approach: They propose a benchmark to evaluate LLMs' ability to perform codeflow by reusing existing functions over multiple turns.
Outcome: The proposed benchmarks show that LLMs perform significantly worse in multi-turn codeflow scenarios and that their performance inversely correlates with dependency complexity.
StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding (2026.findings-acl)

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Challenge: StreamMeCo is an efficient Stream Agent Memory Compression framework for video understanding.
Approach: They propose an efficient Stream Agent Memory Compression framework that evicts redundant memory nodes and introduces a time-decay memory retrieval mechanism to mitigate performance degradation.
Outcome: The proposed framework achieves 1.87 speedup in memory retrieval while delivering an average accuracy improvement of 1.0% on three challenging benchmark datasets.
Text2Mem: A Unified Memory Operation Language for Memory Operating System (2026.findings-acl)

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Challenge: Existing memory frameworks lack a formal, executable specification for memory control.
Approach: They propose a unified memory operation language that standardizes translation of natural-language instructions into reliable execution.
Outcome: The proposed language standardizes translation of natural-language instructions into reliable execution.
Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems (2026.acl-long)

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Challenge: Existing personalized dialogue systems struggle to reconcile unbounded interactions with finite context constraints.
Approach: They propose a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling.
Outcome: The proposed framework outperforms existing systems in suppressing contextual noise and persona inconsistency.

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