arXiv subjects

Neil Johnson

Publications and source records attributed to Neil Johnson.

10 recordsLinked to original sources

arXiv2025

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated. However, extending this success to other reasoning-intensive domains remains challenging due to the scarcity of high-quality, verifiable datasets and the high cost of human supervision. In this work, we introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification across a diverse range of reasoning-intensive domains. The framework consists of two key components: (1) LoongBench, a curated seed dataset containing 8,729 human-vetted examples across 12 domains (e.g., Advanced Mathematics, Chemistry, Logic), each paired with executable code and rich metadata; and (2) LoongEnv, a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples. Together, these components form an agent-environment loop that enables reinforcement learning, where an LLM-based agent is rewarded for generating Chain-of-Thought (CoT) solutions that align with code-executed answers. Empirically, we benchmark LoongBench on a broad suite of both open-source and proprietary LLMs to evaluate domain coverage and reveal performance bottlenecks. In addition, we conduct a comprehensive analysis of synthetic data generated by LoongEnv, examining correctness, difficulty, and diversity. Code and documentation are available at https://github.com/camel-ai/loong.

cs.LG↗

arXiv2023

Explaining conflict violence in terms of conflict actor dynamics

We study the severity of conflict-related violence in Colombia at an unprecedented granular scale in space and across time. Splitting the data into different geographical regions and different historically-relevant eras, we uncover variations in the patterns of conflict severity which we then explain in terms of local conflict actors' different collective behaviors and/or conditions using a simple mathematical model of conflict actors' grouping dynamics (coalescence and fragmentation). Specifically, variations in the approximate scaling values of the distributions of event lethalities can be explained by the changing strength ratio of the local conflict actors for distinct conflict periods and organizational regions. In this way, our findings open the door to a new granular spectroscopy of human conflicts in terms of local conflict actor strength ratios for any armed conflict.

physics.soc-ph↗

arXiv2023

Real-time Likelihood Methods for Improved Gamma-ray Transient Detection and Localization

We present a maximum likelihood (ML) algorithm that is fast enough to detect gamma-ray transients in real time on low-performance processors often used for space applications. We validate the routine with simulations and find that, relative to algorithms based on excess counts, the ML method is nearly twice as sensitive, allowing detection of 240-280% more short gamma-ray bursts. We characterize a reference implementation of the code, estimating its computational complexity and benchmarking it on a range of processors. We exercise the reference implementation on archival data from the Fermi Gamma-ray Burst Monitor (GBM), verifying the sensitivity improvements. In particular, we show that the ML algorithm would have detected GRB 170817A even if it had been nearly four times fainter. We present an ad hoc but effective scheme for discriminating transients associated with background variations. We show that the on-board localizations generated by ML are accurate, but that refined off-line localizations require a detector response matrix with about ten times finer resolution than is current practice. Increasing the resolution of the GBM response matrix could substantially reduce the few-degree systematic uncertainty observed in the localizations of bright bursts.

astro-ph.HE↗

arXiv2022

Emergence of polarization in coevolving networks

Polarization is a ubiquitous phenomenon in social systems. Empirical studies document substantial evidence for opinion polarization across social media, showing a typical bipolarized pattern devising individuals into two groups with opposite opinions. While coevolving network models have been proposed to understand polarization, existing works cannot generate a stable bipolarized structure. Moreover, a quantitative and comprehensive theoretical framework capturing generic mechanisms governing polarization remains unaddressed. In this paper, we discover a universal scaling law for opinion distributions, characterized by a set of scaling exponents. These exponents classify social systems into bipolarized and depolarized phases. We find two generic mechanisms governing the polarization dynamics and propose a coevolving framework that counts for opinion dynamics and network evolution simultaneously. Under a few generic assumptions on social interactions, we find a stable bipolarized community structure emerges naturally from the coevolving dynamics. Our theory analytically predicts two-phase transitions across three different polarization phases in line with the empirical observations for the Facebook and blogosphere datasets. Our theory not only accounts for the empirically observed scaling laws but also allows us to predict scaling exponents quantitatively.

physics.soc-ph↗

arXiv2021

Modelling Universal Order Book Dynamics in Bitcoin Market

Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists. Most existing models, such as stochastic volatility models, focus mainly on price changes, neglecting the complex trading dynamics. Recently, there are increasing studies on order books, thanks to the availability of large-scale trading datasets, aiming to understand the underlying mechanisms governing the market dynamics. In this paper, we collect order-book datasets of Bitcoin platforms across three countries over millions of users and billions of daily turnovers. We find a 1+1D field theory, govern by a set of KPZ-like stochastic equations, predicts precisely the order book dynamics observed in empirical data. Despite the microscopic difference of markets, we argue the proposed effective field theory captures the correct universality class of market dynamics. We also show that the model agrees with the existing stochastic volatility models at the long-wavelength limit.

q-fin.TR↗

arXiv2015

Exploring the effects of photon correlations from thermal sources on bacterial photosynthesis

Thermal light sources can produce photons with strong spatial correlations. We study the role that these correlations might potentially play in bacterial photosynthesis. Our findings show a relationship between the transversal distance between consecutive absorption and the efficiency of the photosynthetic process. Furthermore, membranes where the clustering of core complexes (so-called RC-LH1) is high, display a range where the organism profits maximally from the spatial correlation of the incoming light. By contrast, no maximum is found for membranes with low core-core clustering. We employ a detailed membrane model with state-of-the-art empirical inputs. Our results suggest that the organization of the membrane's antenna complexes may be well-suited to the spatial correlations present in an natural light source. Future experiments will be needed to test this prediction.

physics.bio-ph↗

arXiv2012

Financial black swans driven by ultrafast machine ecology

Society's drive toward ever faster socio-technical systems, means that there is an urgent need to understand the threat from 'black swan' extreme events that might emerge. On 6 May 2010, it took just five minutes for a spontaneous mix of human and machine interactions in the global trading cyberspace to generate an unprecedented system-wide Flash Crash. However, little is known about what lies ahead in the crucial sub-second regime where humans become unable to respond or intervene sufficiently quickly. Here we analyze a set of 18,520 ultrafast black swan events that we have uncovered in stock-price movements between 2006 and 2011. We provide empirical evidence for, and an accompanying theory of, an abrupt system-wide transition from a mixed human-machine phase to a new all-machine phase characterized by frequent black swan events with ultrafast durations (<650ms for crashes, <950ms for spikes). Our theory quantifies the systemic fluctuations in these two distinct phases in terms of the diversity of the system's internal ecology and the amount of global information being processed. Our finding that the ten most susceptible entities are major international banks, hints at a hidden relationship between these ultrafast 'fractures' and the slow 'breaking' of the global financial system post-2006. More generally, our work provides tools to help predict and mitigate the systemic risk developing in any complex socio-technical system that attempts to operate at, or beyond, the limits of human response times.

physics.soc-ph↗

arXiv2011

Dynamic Red Queen explains patterns in fatal insurgent attacks

The Red Queen's notion "It takes all the running you can do, to keep in the same place" has been applied within evolutionary biology, politics and economics. We find that a generalized version in which an adaptive Red Queen (e.g. insurgency) sporadically edges ahead of a Blue King (e.g. military), explains the progress curves for fatal insurgent attacks against the coalition military within individual provinces in Afghanistan and Iraq. Remarkably regular mathematical relations emerge which suggest a prediction formula for the timing of the n'th future fatal day, and provide a common framework for understanding how insurgents fight in different regions. Our findings are consistent with a Darwinian selection hypothesis which favors a weak species which can adapt rapidly, and establish an unexpected conceptual connection to the physics of correlated walks.

physics.soc-ph↗

arXiv2008

Efficiency and formalism of quantum games

We pursue a general theory of quantum games. We show that quantum games are more efficient than classical games, and provide a saturated upper bound for this efficiency. We demonstrate that the set of finite classical games is a strict subset of the set of finite quantum games. We also deduce the quantum version of the Minimax Theorem and the Nash Equilibrium Theorem.

quant-ph↗

arXiv2002

Parrondo Games and Quantum Algorithms

We pursue the possible connections between classical games and quantum computation. The Parrondo game is one in which a random combination of two losing games produces a winning game. We introduce novel realizations of this Parrondo effect in which the player can `win' via random reflections and rotations of the state-vector, and connect these to known quantum algorithms.

quant-ph↗