Xiao Peng

Lei Wei, Jinpeng Ou, Xiao Peng et al.

Large language models (LLMs) have demonstrated remarkable capabilities in function calling for autonomous agents, yet current mechanisms lack explicit reasoning transparency during parameter generation, particularly for complex functions with interdependent parameters. While existing approaches like chain-of-thought prompting operate at the agent level, they fail to provide fine-grained reasoning guidance for individual function parameters. To address these limitations, we propose Think-Augmented Function Calling (TAFC), a novel framework that enhances function calling accuracy through explicit reasoning at both function and parameter levels. Our method introduces a universal "think" parameter augmentation that enables models to articulate their decision-making process, with dynamic optimization for parameter descriptions to improve reasoning quality. For complex parameters, TAFC automatically triggers granular reasoning based on complexity scoring, ensuring appropriate justification for critical decisions. Additionally, we propose reasoning-guided optimization to align generated reasoning with human expectations. TAFC requires no architectural modifications to existing LLMs while maintaining full API compatibility. Evaluation on ToolBench across proprietary and open-source models demonstrates significant improvements in parameter generation accuracy and reasoning coherence for multi-parameter functions, while providing enhanced interpretability for debugging AI agent behaviors.

Lei Wei, Xu Dong, Xiao Peng et al.

Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context boundaries or information overload within them. While human memory naturally balances retention and forgetting through adaptive decay processes, current AI systems employ binary retention strategies that preserve everything or lose it entirely. We propose FadeMem, a biologically-inspired agent memory architecture that incorporates active forgetting mechanisms mirroring human cognitive efficiency. FadeMem implements differential decay rates across a dual-layer memory hierarchy, where retention is governed by adaptive exponential decay functions modulated by semantic relevance, access frequency, and temporal patterns. Through LLM-guided conflict resolution and intelligent memory fusion, our system consolidates related information while allowing irrelevant details to fade. Experiments on Multi-Session Chat, LoCoMo, and LTI-Bench demonstrate superior multi-hop reasoning and retrieval with 45\% storage reduction, validating the effectiveness of biologically-inspired forgetting in agent memory systems.

Xiao Peng, Liang Chen

Recently, large language models (LLMs) like ChatGPT, LLaMA, and Claude have prevailed in countless domains, including legal scenarios. With LLMs' rapid technological progress, the development of prompt engineering (PE) as an interface between the LLMs and real-world applications has drawn the attention of all developers. Various PE methods have been proposed to overcome real-world challenges, such as few-shot prompting, chain-of-thought, and retrieval-augmented generation (RAG). However, RAG for legal judgment prediction (LJP) is still underexplored. To address this, we propose "Athena", a novel framework cultivating RAG as a core preprocess component to enhance LLMs' performance on specialized tasks. Athena constructs a knowledge base for accusations, attached with a semantic retrieval mechanism through vectorization. Our experiments show that Athena's overall performance has improved significantly, achieving state-of-the-art results on the CAIL2018 dataset. Our ablation study on the in-context window size parameter further reproduces LLMs' "lost-in-the-middle" phenomenon with a relative positional variation. And with moderate hyper-parameter-tuning, we can achieve at most 95% of accuracy accordingly. We also study the impact of query rewriting and data distribution, providing possible directions for future research based on former analyses.

Xinyue Liu, Xiao Peng, Shuyue Yan et al.

Observed records of climate extremes provide an incomplete picture of risk, missing "unseen" extremes that exceed historical bounds. In parallel, neglecting spatial dependence undervalues the risk of synchronized hazards that amplify impacts. To address these challenges, we develop DeepX-GAN (Dependence-Enhanced Embedding for Physical eXtremes - Generative Adversarial Network), a knowledge-informed deep generative model designed to better capture the spatial structure of rare extremes. The zero-shot generalizability of DeepX-GAN enables simulation of unseen extremes that fall outside historical experience yet remain statistically plausible. We define two types of unseen extremes: "checkmate" extremes that directly hit targets, and "stalemate" extremes that narrowly miss. These unrealized scenarios expose latent risks in fragile systems and may reinforce a false sense of resilience if overlooked. Near misses, in particular, can prompt either proactive adaptation or dangerous complacency, depending on how they are interpreted. Applying DeepX-GAN to the Middle East and North Africa (MENA), we find that these unseen extremes disproportionately affect regions with high vulnerability and low socioeconomic readiness, but differ in urgency and interpretation. Future warming could expand and redistribute these unseen extremes, with emerging exposure hotspots in Indo-Pakistan and Central Africa. This distributional shift highlights critical blind spots in conventional hazard planning and underscores the need to develop spatially adaptive policies that anticipate emergent risk hotspots rather than simply extrapolating from historical patterns.