13 citations · 26 across the 41 of their papers we have counts for
10 papers · 1 filter
EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
Runxuan Liu, Jingqi Shi, Baoxin Wang +10
Multi-modal agents have made significant progress in interacting with open worlds. However, their ability to self-evolve remains limited. Supervised fine-tuning enables imitation b…
GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL
Zike Yuan, Han Zhang, Jianzhi Yan +10
Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formulation. While deterministic…
LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?
HuiMing Fan, Xiao Wang, Zheng Chu +5
Are LLM-based search agents genuinely searching, or using the web to verify what they already know? We study this question on BrowseComp with three diagnostics. Our analysis reveal…
EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents
Zike Yuan, Yukun Cao, Han Zhang +7
Graph reasoning agents operating from natural-language inputs must solve a coupled problem: they must reconstruct a structured graph instance from text, decide whether existing com…
Parameter- and Bandwidth-Efficient Edge--cloud Many-to-Many Speech-to-Text Translation
Yexing Du, Kaiyuan Liu, Youcheng Pan +5
Multimodal large language models (MLLMs) have demonstrated significant potential for speech-to-text translation (S2TT). However, existing deployment paradigms face critical challen…
From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities
Shixin Jiang, Jiafeng Liang, Jiyuan Wang +6
To tackle complex tasks in real-world scenarios, more researchers are focusing on Omni-MLLMs, which aim to achieve omni-modal understanding and generation. Beyond the constraints o…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Zike Yuan, Ming Liu, Hui Wang +1
Evaluating the graph comprehension and reasoning abilities of Large Language Models (LLMs) is challenging and often incomplete. Existing benchmarks focus primarily on pure graph un…
KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
Haojie Pan, Zepeng Zhai, Hao Yuan +5
Driven by curiosity, humans have continually sought to explore and understand the world around them, leading to the invention of various tools to satiate this inquisitiveness. Desp…
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
Tao He, Ming Liu, Yixin Cao +4
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increas…
BigCilin: An Automatic Chinese Open-domain Knowledge Graph with Fine-grained Hypernym-Hyponym Relations
Ming Liu, Yaojia LV, Jingrun Zhang +2
This paper presents BigCilin, the first Chinese open-domain knowledge graph with fine-grained hypernym-hyponym re-lations which are extracted automatically from multiple sources fo…