2 citations · 8 across the 11 of their papers we have counts for
16 papers
FreeEvolve: Learning to Evolve Beyond Fixed Loops
Lecheng Kong, Like Hui, Nikos Kanakaris +3
Agent evolvers automate the design of the prompts, skills and workflows around language model agents, yet the optimization process they follow is still designed by hand: a fixed se…
On the Off-Policy Teacher in On-Policy Distillation
Langlin Huang, Hao Liu, Mononito Goswami +6
On-policy distillation (OPD) has recently emerged as a promising post-training paradigm in which the student learns from trajectories generated by its own policy under dense teache…
MILO: Automated Harness Discovery via Orchestrated Multi-Agent Evolution
Prithwish Jana, Mononito Goswami, Hao Liu +9
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its…
Hermes: Learning Contextual Reasoning Unlocks Test-Time Scaling
Xinyu Li, Mononito Goswami, Hao Liu +5
Test-time scaling improves model performance by allocating additional compute during inference. Using this compute effectively across multiple context windows requires deciding how…
HyperLabel: Multi-Label Classification via Hypergraph-Based Label Correlation Modeling
Peiyu Zhang, Heng Ping, Nikos Kanakaris +5
Multi-label classification (MLC) requires predicting multiple relevant labels for each instance, where a central challenge is modeling complex label dependencies arising from co-oc…
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts
Anwesan Pal, Karen Hovsepian, Tinghao Guo +5
Recent investigations into effective context lengths of modern flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reason…
HGFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs
Trung-Kien Nguyen, Heng Ping, Shixuan Li +4
The growing interests and applications of graph learning in diverse domains have propelled the development of a unified model generalizing well across different graphs and tasks, k…
ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
Alejandro Ariza-Casabona, Nikos Kanakaris, Daniele Malitesta
Relational deep learning (RDL) settles among the most exciting advances in machine learning for relational databases, leveraging the representational power of message passing graph…
HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDL
Heng Ping, Shixuan Li, Peiyu Zhang +8
Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, when applied to hardware description languages (HDL), t…
Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance
Nikos Kanakaris, Heng Ping, Xiongye Xiao +4
Detecting organized political campaigns is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized…
Exploiting Application-to-Architecture Dependencies for Designing Scalable OS
Yao Xiao, Nikos Kanakaris, Anzhe Cheng +5
With the advent of hundreds of cores on a chip to accelerate applications, the operating system (OS) needs to exploit the existing parallelism provided by the underlying hardware r…
Personalized Graph-Based Retrieval for Large Language Models
Steven Au, Cameron J. Dimacali, Ojasmitha Pedirappagari +7
As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. Existing p…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Shukai Duan, Heng Ping, Nikos Kanakaris +9
Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks.…
Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
Xiongye Xiao, Heng Ping, Chenyu Zhou +6
In recent years, there has been increasing attention on the capabilities of large models, particularly in handling complex tasks that small-scale models are unable to perform. Nota…
PerfRL: A Small Language Model Framework for Efficient Code Optimization
Shukai Duan, Nikos Kanakaris, Xiongye Xiao +8
Code optimization is a challenging task requiring a substantial level of expertise from developers. Nonetheless, this level of human capacity is not sufficient considering the rapi…