11 citations · 24 across the 20 of their papers we have counts for
24 papers
Enhancing Language Model Reasoning with Structured Multi-Level Modeling
Siheng Xiong, Ali Payani, Faramarz Fekri
Inference-time scaling enhances a model's reasoning by extending its chain-of-thought (CoT). However, existing approaches typically rely on a single policy trained with outcome-rew…
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design
Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong +1
LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an evolutionary search. Existin…
Evaluator-in-the-Loop Monte Carlo Tree Search via LLM Agents for Motif Scaffolding in Protein Design
Haotian Hu, Oguzhan Gungordo, Siheng Xiong +1
Motif-scaffolding systems commonly follow a generate-then-filter paradigm, in which candidate proteins are generated independently and structural evaluation is used primarily for t…
DIVE: Unlocking Self-Improvement in Frozen Language Models Through Diversity-Driven Skill Evolution
Siheng Xiong, Ali Payani, Oguzhan Gungordu +1
Large language models (LLMs) cannot retain post-deployment experience without parameter updates. We introduce DIVE, a diversity-driven framework that enables frozen LLMs to improve…
MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design
Oguzhan Gungordu, Siheng Xiong, Faramarz Fekri
Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs). Yet existing frameworks optimize f…
Adaptive Information Control for Search-Augmented LLM Reasoning
Siheng Xiong, Oguzhan Gungordu, James C. Kerce +1
Search-augmented reasoning agents interleave multi-step reasoning with external retrieval, but uncontrolled retrieval can introduce redundant evidence, saturate the context, and de…
PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs
Oguzhan Gungordu, Siheng Xiong, Faramarz Fekri
Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks' reliance on fixed evolutionary r…
Advancing the Pareto Frontier of Test-Time Scaling with Multi-Path Plan Aggregation
Siheng Xiong, Ali Payani, Faramarz Fekri
Test-time scaling seeks to improve reasoning through additional inference-time computation, commonly using solution sampling with majority voting to select the final answer. Howeve…
Long-Context Modeling with Dynamic Hierarchical Sparse Attention for Memory-Constrained LLM Inference
Siheng Xiong, Joe Zou, Faramarz Fekri +1
The quadratic cost of attention limits the scalability of long-context LLMs, especially under limited hardware memory budgets. While attention is often sparse, existing static spar…
Analysis of Semantic Communication for Logic-based Hypothesis Deduction
Ahmet Faruk Saz, Siheng Xiong, Faramarz Fekri
This work presents an analysis of semantic communication in the context of First-Order Logic (FOL)-based deduction. Specifically, the receiver holds a set of hypotheses about the S…
Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams
Yiwen Lu, Siheng Xiong, Zhaowei Li
We present a framework for large-scale sentiment and topic analysis of Twitter discourse. Our pipeline begins with targeted data collection using conflict-specific keywords, follow…
Positional Attention for Efficient BERT-Based Named Entity Recognition
Mo Sun, Siheng Xiong, Yuankai Cai +1
This paper presents a framework for Named Entity Recognition (NER) leveraging the Bidirectional Encoder Representations from Transformers (BERT) model in natural language processin…
Deliberate Planning in Language Models with Symbolic Representation
Siheng Xiong, Zhangding Liu, Jieyu Zhou +1
Planning remains a core challenge for large language models (LLMs), particularly in domains that require coherent multi-step action sequences grounded in external constraints. We i…
DISCD: Distributed Lossy Semantic Communication for Logical Deduction of Hypothesis
Ahmet Faruk Saz, Siheng Xiong, Faramarz Fekri
In this paper, we address hypothesis testing in a distributed network of nodes, where each node has only partial information about the State of the World (SotW) and is tasked with…
Lossy Semantic Communication for the Logical Deduction of the State of the World
Ahmet Faruk Saz, Siheng Xiong, Faramarz Fekri
In this paper, we address the problem of lossy semantic communication to reduce uncertainty about the State of the World (SotW) for deductive tasks in point to point communication.…
Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model
Siheng Xiong, Ali Payani, Yuan Yang +1
Enhancing the reasoning capabilities of language models (LMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of…
The Compressor-Retriever Architecture for Language Model OS
Yuan Yang, Siheng Xiong, Ehsan Shareghi +1
Recent advancements in large language models (LLMs) have significantly enhanced their capacity to aggregate and process information across multiple modalities, enabling them to per…
Can LLMs Reason in the Wild with Programs?
Yuan Yang, Siheng Xiong, Ali Payani +2
Large Language Models (LLMs) have shown superior capability to solve reasoning problems with programs. While being a promising direction, most of such frameworks are trained and ev…
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs
Siheng Xiong, Yuan Yang, Faramarz Fekri +1
Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general. However…
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
Siheng Xiong, Yuan Yang, Ali Payani +2
Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential t…
On The Theory of Semantic Information and Communication for Logical Inference
Ahmet Faruk Saz, Siheng Xiong, Faramarz Fekri
First-Order Logic (FOL), also called first-order predicate calculus, is a formal language that provides a framework to comprehensively represent a world and its present state, incl…
Large Language Models Can Learn Temporal Reasoning
Siheng Xiong, Ali Payani, Ramana Kompella +1
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies. Recent studies have introduced various me…
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Yuan Yang, Siheng Xiong, Ali Payani +2
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature. This paper introduces LogicLLaM…
Temporal Inductive Logic Reasoning over Hypergraphs
Yuan Yang, Siheng Xiong, Ali Payani +2
Inductive logic reasoning is a fundamental task in graph analysis, which aims to generalize patterns from data. This task has been extensively studied for traditional graph represe…