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arXiv 最近完成扫描：2026-11-15 03:37 · 全站最近入库：2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.11560

LiveAsset: Learning Intent Assets from Corrective Interactions for Agent Runtime Control

Guanming Liu, Weihang Wang, Peng Zhang, Meng Wu, Kainan Tu, Jiahao Liu, Yu Zhang, Liuxin Zhang 等 11 位作者

原文摘要

Large language model (LLM) agents acting across web applications and online services often need multi-turn corrections to align their work with users' underspecified goals and preferences. In practice, users may identify problems in the current outcome without specifying the desired result, so learning user intentions requires interpreting each correction against the agent's preceding and revised outcomes. These expectations are often connected to a particular context, making it hard to know when they apply and how to ensure the agent later acts as the user expects. To this end, we propose LiveAsset, an intent harness that learns reusable intent assets from corrective interactions and uses them for runtime control of agent execution. LiveAsset jointly interprets each correction with the agent's original and revised results to infer user intent, incrementally updating the assets while preserving earlier requirements that remain applicable. The learned requirements guide agent execution from task entry and provide criteria for runtime verification, which uses the explanations to assess applicability before each reply is delivered and directs the agent to revise the result to address unmet requirements while preserving those already satisfied. Following a learning phase in which simulated users progressively reveal requirements through multi-turn corrections, we evaluate LiveAsset using same-task replay of the original requests on π-Bench, PERMA, and Vita and cross-task transfer on PAST-Bench and SkillEvolBench. Experiments show that LiveAsset better fulfills requirements learned from corrective interactions and improves performance on these benchmarks compared with memory, skill, and rule-based harness baselines. Code is available at github.com/landian60/LiveAsset.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.03325

IntPro: Context-Aware Intent Inference via Selective Retrieval from User History

Guanming Liu, Meng Wu, Peng Zhang, Yu Zhang, Yubo Shu, Xianliang Huang, Kainan Tu, Ning Gu 等 11 位作者

原文摘要

Accurately understanding user intent in context is essential for large language models (LLMs) to provide effective assistance. However, observed behavior may support multiple plausible intents, making the current context alone insufficient for identifying the user's underlying goal. A user's interaction history may contain recurring context-to-intent patterns that help disambiguate the current intent. Leveraging such historical patterns requires deciding whether to invoke retrieval for the current context and, if so, forming queries that express plausible intents rather than relying on contextual similarity alone. We present IntPro, a proxy agent that learns to either infer intent directly from the current context or generate candidate intent explanations to retrieve intent-aligned records from the user's history. We design intent explanations as a shared intent-oriented natural-language form that expresses the observed action, its functional goal, and a cue-to-intent rationale. Label-conditioned explanations represent historical records, while candidate explanations generated from the current context serve as retrieval queries in the same form. IntPro first learns direct and retrieval-conditioned inference through Direct-and-Retrieval supervised fine-tuning (SFT). IntPro then uses multi-turn GRPO with a retrieval-decision reward to jointly optimize intent prediction and selective retrieval mechanism. Experiments across three scenarios and three model backbones demonstrate that IntPro consistently improves context-aware intent inference.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.14571

StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance

Guanming Liu, Yuqi Ren, Hansu Gu, Peng Zhang, Weihang Wang, Jiahao Liu, Ning Gu, Tun Lu

原文摘要

A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two-step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow-up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow-up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow-up behavior, even when evidence is stored or feedback is incorporated locally. StreamMemBench is publicly available at https://github.com/landian60/StreamMemBench.

cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.77ACL Anthology · ACL 2026

LLMs for Now, Fine-Tuning for Later: An Ensemble Approach to Data Drift in Domain-Specific Tasks

Yuxuan Lu, Bingsheng Yao Northeastern University, Shao Zhang Shanghai Artificial Intelligence Laboratory, Yisi Sang Amazon, Yun Wang Microsoft Research, Hansu Gu Amazon, Peng Zhang, Tun Lu Fudan University 等 10 位作者

原文摘要

Deploying machine learning models in real-world domain-specific scenarios is challenged by the scarcity of expert annotations and by data drift, where the statistical properties of incoming data continuously evolve. Active Learning (AL) iteratively improves compact models with expert annotations but suffers from recurring cold-start degradation, while LLMs provide strong off-the-shelf performance yet cannot leverage newly accumulated labels, raising the question: how can we better leverage LLMs to assist the active learning process? Through an empirical study on five legal and biomedical datasets, we reveal a complementary temporal dynamic: LLMs excel during early and post-drift stages, while AL-assisted compact models eventually surpass them as annotations accumulate. Motivated by this finding, we propose an ensemble system that combines an LLM, an AL-assisted compact model, and an automatic switch module that routes predictions to the better-performing model in real time. Evaluated under simulated data drift on two mental health datasets, our system achieves 96–98% switch accuracy and consistently outperforms either model used alone.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表