22 citations · 37 across the 13 of their papers we have counts for
7 papers · 1 filter
CABLE: Extending the Reach of Memory Retrieval via Complementary Antecedent-Based Linking and Expansion
Zheling Tan, Jin Gao, Dequan Wang
As LLM agents operate across structured workflows and sessions, preserving long-term history does not ensure that later contexts can recover relevant evidence through a bounded mem…
LU-500: A Logo Benchmark for Concept Unlearning
Keyu Li, Jin Gao, Jialing Zhang +1
Concept unlearning is increasingly used to limit the reproduction of protected or unsafe visual concepts in text-to-image models. Existing evaluations, however, mostly study target…
The Cost of Knowing: A Resource-Aware Protocol for Benchmarking Hallucination Beyond Static Leaderboards
Keyu Li, Jin Gao, Dequan Wang
On standard factuality tasks, frontier models now cluster near the top of the scale. The question is therefore shifting from how factual a system is toward how much compute that fa…
TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering
Jin Gao, Juntu Zhao, Zirui Zeng +5
AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static measur…
Aligned Agents, Biased Swarm: Measuring Bias Amplification in Multi-Agent Systems
Keyu Li, Jin Gao, Dequan Wang
While Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remain poorly understood. Because r…
Credit-Budgeted ICPC-Style Coding: When Agents Must Pay for Every Decision
Lingfeng Zhou, Junhao Shi, Jin Gao +1
Current evaluations of autonomous coding agents assume an unrealistic, infinite-resource environment. However, real-world software engineering is a resource-bound competition. As w…
Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
Zijian Zhao, Jing Gao, Sen Li
Cooperative multi-agent reinforcement learning (MARL) is widely used to address large joint observation and action spaces by decomposing a centralized control problem into multiple…