5 papers · 1 filter
AgentEscapeBench: Evaluating Out-of-Domain Tool-Grounded Reasoning in LLM Agents
Zhengkang Guo, Yiyang Li, Lin Qiu +7
As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar workflows and short-range inte…
TRIP-Bench: A Benchmark for Long-Horizon Interactive Agents in Real-World Scenarios
Yuanzhe Shen, Zisu Huang, Zhengyuan Wang +14
As LLM-based agents are deployed in increasingly complex real-world settings, existing benchmarks underrepresent key challenges such as enforcing global constraints, coordinating m…
Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction
Muzhao Tian, Zisu Huang, Xiaohua Wang +8
As LLM-based agents are increasingly used in long-term interactions, cumulative memory is critical for enabling personalization and maintaining stylistic consistency. However, most…
IntentionReasoner: Facilitating Adaptive LLM Safeguards through Intent Reasoning and Selective Query Refinement
Yuanzhe Shen, Zisu Huang, Zhengkang Guo +5
The rapid advancement of large language models (LLMs) has driven their adoption across diverse domains, yet their ability to generate harmful content poses significant safety chall…
RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
Zhengkang Guo, Wenhao Liu, Mingchen Xie +13
Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficiency in crafting sophisticated prom…