94 citations · 449 across the 53 of their papers we have counts for
57 papers
New Lower and Upper Bounds for the Grothendieck Constant
Rahul Saha, Alan Li, Anton Xue +4
We establish new bounds on the Grothendieck constant : \[ \frac{6π}{11} \le K_G \le \fracπ{2\log(1+\sqrt2)} - 10^{-4}. \] Methodologically, our lower bound approach differs fr…
Advancing Mathematics Research with AI-Driven Formal Proof Search
George Tsoukalas, Anton Kovsharov, Sergey Shirobokov +18
Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to gener…
The Grothendieck Constant is Less Than
Alan Li, Rahul Saha, Anton Xue +4
We prove that the Grothendieck constant . This improves on the work of Braverman, Makarychev, Makarychev, and Naor (2011), who proved…
Do CFLOBDDs Actually Make Use of Linear Structure?
Meghana Aparna Sistla, Swarat Chaudhuri, Thomas W. Reps
Binary Decision Diagrams (BDDs) are a widely used data structure for efficient Boolean function representation. Context-Free-Language Ordered Binary Decision Diagrams (CFLOBDDs) ar…
Man-Made Heuristics Are Dead. Long Live Code Generators!
Rohit Dwivedula, Divyanshu Saxena, Aditya Akella +2
Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the po…
Efficient Tree-Structured Deep Research with Adaptive Resource Allocation
Lunyiu Nie, Nedim Lipka, Ryan A. Rossi +1
Deep research agents, which synthesize information across diverse sources, are significantly constrained by the sequential nature of reasoning. This bottleneck results in high late…
Resource-efficient Inference with Foundation Model Programs
Lunyiu Nie, Zhimin Ding, Kevin Yu +3
The inference-time resource costs of large language and vision models present a growing challenge in production deployments. We propose the use of foundation model programs, i.e.,…
Self-Evolving Visual Concept Library using Vision-Language Critics
Atharva Sehgal, Patrick Yuan, Ziniu Hu +3
We study the problem of building a visual concept library for visual recognition. Building effective visual concept libraries is challenging, as manual definition is labor-intensiv…
Canopy: Property-Driven Learning for Congestion Control
Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula +3
Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based contr…
Learning Quantitative Automata Modulo Theories
Eric Hsiung, Swarat Chaudhuri, Joydeep Biswas
Quantitative automata are useful representations for numerous applications, including modeling probability distributions over sequences to Markov chains and reward machines. Active…
Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes +2
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using…
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
Abhinav Jain, Swarat Chaudhuri, Thomas Reps +1
Parameter-Efficient Fine-Tuning (PEFT) has become the standard for customising Foundation Models (FMs) to user-specific downstream tasks. However, typical PEFT methods require stor…
LTL-Constrained Policy Optimization with Cycle Experience Replay
Ameesh Shah, Cameron Voloshin, Chenxi Yang +3
Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many settings where both satisfaction and optimality…
Online Cascade Learning for Efficient Inference over Streams
Lunyiu Nie, Zhimin Ding, Erdong Hu +2
Large Language Models (LLMs) have a natural role in answering complex queries about data streams, but the high computational cost of LLM inference makes them infeasible in many suc…
On a Foundation Model for Operating Systems
Divyanshu Saxena, Nihal Sharma, Donghyun Kim +15
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that…
MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning
Zayne Sprague, Xi Ye, Kaj Bostrom +2
While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to rea…
Neurosymbolic Grounding for Compositional World Models
Atharva Sehgal, Arya Grayeli, Jennifer J. Sun +1
We introduce Cosmos, a framework for object-centric world modeling that is designed for compositional generalization (CompGen), i.e., high performance on unseen input scenes obtain…
Automata Learning from Preference and Equivalence Queries
Eric Hsiung, Joydeep Biswas, Swarat Chaudhuri
Active automata learning from membership and equivalence queries is a foundational problem with numerous applications. We propose a novel variant of the active automata learning pr…
Deductive Additivity for Planning of Natural Language Proofs
Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri +1
Current natural language systems designed for multi-step claim validation typically operate in two phases: retrieve a set of relevant premise statements using heuristics (planning)…
A Probabilistic Framework for Modular Continual Learning
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri +1
Modular approaches that use a different composition of modules for each problem are a promising direction in continual learning (CL). However, searching through the large, discrete…
Coarse-Tuning Models of Code with Reinforcement Learning Feedback
Abhinav Jain, Chima Adiole, Swarat Chaudhuri +2
Large Language Models (LLMs) pre-trained on code have recently emerged as the dominant approach to program synthesis. However, these models are trained using next-token prediction,…
Weighted Context-Free-Language Ordered Binary Decision Diagrams
Meghana Sistla, Swarat Chaudhuri, Thomas Reps
This paper presents a new data structure, called \emph{Weighted Context-Free-Language Ordered BDDs} (WCFLOBDDs), which are a hierarchically structured decision diagram, akin to Wei…
Symbolic Quantum Simulation with Quasimodo
Meghana Sistla, Swarat Chaudhuri, Thomas Reps
The simulation of quantum circuits on classical computers is an important problem in quantum computing. Such simulation requires representations of distributions over very large se…
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation
Chenxi Yang, Greg Anderson, Swarat Chaudhuri
We present a reinforcement learning (RL) framework in which the learned policy comes with a machine-checkable certificate of provable adversarial robustness. Our approach, called C…