7 citations · 10 across the 10 of their papers we have counts for
11 papers
FlowMesh: A Service Fabric for Composable LLM Workflows
Junyi Shen, Noppanat Wadlom, Lingfeng Zhou +4
AI deployment increasingly resembles a pipeline of data transformation, fine-tuning, and agent interactions rather than a monolithic LLM job; recent examples include RLHF/RLAIF tra…
Social learning moderates the tradeoffs between efficiency, stability, and equity in group foraging
Zexu Li, M. Amin Rahimian, Lei Fang
Collective foragers, from animals to robotic swarms, must balance exploration and exploitation to locate sparse resources efficiently. While social learning is known to facilitate…
Analyzing and Mitigating Object Hallucination: A Training Bias Perspective
Yifan Li, Kun Zhou, Wayne Xin Zhao +2
As scaling up training data has significantly improved the general multimodal capabilities of Large Vision-Language Models (LVLMs), they still suffer from the hallucination issue,…
Towards Effective Code-Integrated Reasoning
Fei Bai, Yingqian Min, Beichen Zhang +6
In this paper, we investigate code-integrated reasoning, where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire thi…
R1-Searcher++: Incentivizing the Dynamic Knowledge Acquisition of LLMs via Reinforcement Learning
Huatong Song, Jinhao Jiang, Wenqing Tian +7
Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-Augmented Generation (RAG) helps by injecting external information, but cur…
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability
Han Peng, Jinhao Jiang, Zican Dong +2
Advancements in Large Language Models (LLMs) have extended their input context length, yet they still struggle with retrieval and reasoning in long-context inputs. Existing methods…
Rethinking Invariance in In-context Learning
Lizhe Fang, Yifei Wang, Khashayar Gatmiry +2
In-Context Learning (ICL) has emerged as a pivotal capability of auto-regressive large language models, yet it is hindered by a notable sensitivity to the ordering of context examp…
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
Shuang Sun, Huatong Song, Yuhao Wang +10
Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information re…
R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
Huatong Song, Jinhao Jiang, Yingqian Min +5
Existing Large Reasoning Models (LRMs) have shown the potential of reinforcement learning (RL) to enhance the complex reasoning capabilities of Large Language Models~(LLMs). While…
An Empirical Study on Eliciting and Improving R1-like Reasoning Models
Zhipeng Chen, Yingqian Min, Beichen Zhang +10
In this report, we present the third technical report on the development of slow-thinking models as part of the STILL project. As the technical pathway becomes clearer, scaling RL…
Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
Haoxiang Sun, Yingqian Min, Zhipeng Chen +2
The rapid advancement of large reasoning models has saturated existing math benchmarks, underscoring the urgent need for more challenging evaluation frameworks. To address this, we…