8 citations · 9 across the 22 of their papers we have counts for
22 papers
InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval
Tsz Ting Chung, Jiangnan Li, Jie Zhou +1
Self-improving agents accumulate reusable insights from prior trajectories, making retrieval increasingly important for turning accumulated experience into actionable guidance. At…
A New Role for Relevance: Guiding Corpus Interaction in Agentic Search
Jiangnan Li, Yuqing Li, Mo Yu +2
Relevance is a query-dependent estimate of whether a document or excerpt contains useful evidence. Existing retrieval agents use relevance to select top- content, but document r…
BAMI: Training-Free Bias Mitigation in GUI Grounding
Borui Zhang, Bo Zhang, Bo Wang +6
GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot-Pro benchmark, exis…
MiA-Signature: Approximating Global Activation for Long-Context Understanding
Yuqing Li, Jiangnan Li, Mo Yu +3
A growing body of work in cognitive science suggests that reportable conscious access is associated with \emph{global ignition} over distributed memory systems, while such activati…
Query-focused and Memory-aware Reranker for Long Context Processing
Yuqing Li, Jiangnan Li, Mo Yu +5
Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage-query relevance u…
How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning
Liyan Xu, Mo Yu, Fandong Meng +1
Chain-of-thought (CoT) reasoning has become a central mechanism for eliciting multi-step reasoning in Large Language Models (LLMs). Yet recent evidence presents a tension: hidden s…
Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding
Yuqing Li, Jiangnan Li, Zheng Lin +5
Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior knowledge, interpret new information…
Structuring Reasoning for Complex Rules Beyond Flat Representations
Zhihao Yang, Ancheng Xu, Jingpeng Li +11
Large language models (LLMs) face significant challenges when processing complex rule systems, as they typically treat interdependent rules as unstructured textual data rather than…
PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
Mo Yu, Tsz Ting Chung, Chulun Zhou +8
We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical nar…
ComoRAG: A Cognitive-Inspired Memory-Organized RAG for Stateful Long Narrative Reasoning
Juyuan Wang, Rongchen Zhao, Wei Wei +5
Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters an…
SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension
Junjie Wu, Jiangnan Li, Yuqing Li +6
Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to de…
Dense Retrievers Can Fail on Simple Queries: Revealing The Granularity Dilemma of Embeddings
Liyan Xu, Zhenlin Su, Mo Yu +3
This work stems from an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within encoded semantics, resulting in failed…
EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection
Ancheng Xu, Zhihao Yang, Jingpeng Li +9
E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision Language Models (VLMs) to detect illicit or misleading product content. However, these models rema…
Understanding LLMs' Fluid Intelligence Deficiency: An Analysis of the ARC Task
Junjie Wu, Mo Yu, Lemao Liu +2
While LLMs have exhibited strong performance on various NLP tasks, it is noteworthy that most of these tasks rely on utilizing the vast amount of knowledge encoded in LLMs' paramet…
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding
Mo Yu, Lemao Liu, Junjie Wu +5
In a systematic way, we investigate a widely asked question: Do LLMs really understand what they say?, which relates to the more familiar term Stochastic Parrot. To this end, we pr…
ReFreeKV: Towards Threshold-Free KV Cache Compression
Xuanfan Ni, Liyan Xu, Chenyang Lyu +6
To reduce memory consumption during LLM inference, a handful of methods have been proposed for KV cache pruning. While these techniques can accomplish lossless memory reduction on…
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters
Chulun Zhou, Qiujing Wang, Mo Yu +7
Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others. Humans infer others' thoughts by integrat…
A Survey on the Honesty of Large Language Models
Siheng Li, Cheng Yang, Taiqiang Wu +12
Honesty is a fundamental principle for aligning large language models (LLMs) with human values, requiring these models to recognize what they know and don't know and be able to fai…
Think out Loud: Emotion Deducing Explanation in Dialogues
Jiangnan Li, Zheng Lin, Lanrui Wang +6
Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to re…
Previously on the Stories: Recap Snippet Identification for Story Reading
Jiangnan Li, Qiujing Wang, Liyan Xu +5
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand…
Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions
Liyan Xu, Jiangnan Li, Mo Yu +1
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more co…
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
Liyan Xu, Zhenlin Su, Mo Yu +4
Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stron…