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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for code commits

StakeBench evaluates language understanding by linking comments to market commitments, improving model alignment with real-world outcomes.

problem Existing financial NLP benchmarks measure perceived language rather than market commitments.
method StakeBench uses observable market behavior to supervise models, testing their ability to detect commitments, identify sides, and project odds.
result Models partially recover position-side signals but struggle with later tasks, highlighting structural failures.

The way developers collaborate inside and particularly across teams often escapes management's attention, despite a formal organization with designated teams being defined. Observability of the actual, organically formed engineering structure provides decision makers invaluable additional tools to manage their talent p…

2019-05-07abs ↗pdf ↗

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a system, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary probl…

2018-12-24abs ↗pdf ↗

Before a person can be prosecuted and convicted for insider trading, he must first execute the overt act of trading. If no sale of security is consummated, no crime is also consummated. However, through a complex and insidious combination of various financial instruments, one can capture the same amount of gains from i…

2018-05-16abs ↗pdf ↗

The study analyzes when Bayesian averaging over decision trees is reliable.

problem When do Bayesian model averaging weights over decision trees provide reliable information?
method Closed-form solution for Bayesian decision trees with Catalan-exponential priors.
result Established a complete non-asymptotic theory of rational commitment thresholds.

In this paper, we study multi-armed bandit problems in explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the hig…

2019-04-30abs ↗pdf ↗

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.

Study of 2imes22 imes 2 zero-sum games with noisy observations and commitments.

problem Analyzing 2imes22 imes 2 zero-sum games with noisy observations and commitments.
method Modeling a 2imes22 imes 2 zero-sum game with a leader committing to a strategy and a follower observing a noisy version of the leader's action.
result Observing the leader's action is either beneficial or immaterial for the follower, and the equilibrium payoff is bounded.

New algorithm tackles high-dimensional contextual bandits without sparsity.

problem High-dimensional linear contextual bandit problem with large feature space.
method Proposes explore-then-commit (EtC) and adaptive explore-then-commit (AEtC) algorithms.
result Derives optimal rate for ETC algorithm and shows adaptive AEtC achieves it.

This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key is…

2019-08-09abs ↗pdf ↗

Investigates multi-period portfolio optimization for DC plans using buffered Probability of Exceedance.

problem Optimizing long-term Defined Contribution plans with realistic constraints and dynamic dynamics.
method Formulates and solves bilevel optimization problems for pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization.
result Time-consistent Mean-bPoE strategies maintain investor preferences for minimum terminal wealth, unlike Mean-CVaR.

Continuous-time Kyle model shows privacy subsidy from noise-perturbed order flow.

problem Quantifying break-even fees for committed-AMM exchanges under privacy-aggregated information.
method Extended Nakamura's (2026) single-period result to continuous-time, observing order flow perturbed by Brownian noise.
result Cumulative privacy subsidy is identified as equivalent to Loss-Versus-Rebalancing in price observation gap.

This paper solves the best arm identification problem with both quick commitment and reward maximization.

problem Simultaneously identifying the best arm and minimizing regret in a stochastic Multi-Armed Bandit problem.
method Introduces Regret Optimal Best Arm Identification (ROBAI) and presents algorithms EOCP and its variants.
result Achieves asymptotic optimal regret and quick commitment to the optimal arm in both pre-determined and adaptive stopping times.

New protocol identifies impossible edge orientations in causal graphs.

problem Causal-discovery algorithms cannot distinguish edge directions without assumptions.
method Discrete impossibility certificates and oracle queries.
result Upper bound of 1+K1+K expert interactions for DAG recovery.

The discrete-time mean-variance portfolio selection formulation, a representative of general dynamic mean-risk portfolio selection problems, does not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated pre-committed efficient policy may become inefficient when considering the corresponding trunc…

2014-03-04abs ↗pdf ↗

In this paper, we investigate the Merton portfolio management problem in the context of non-exponential discounting. This gives rise to time-inconsistency of the decision-maker. If the decision-maker at time t=0 can commit his/her successors, he/she can choose the policy that is optimal from his/her point of view, and …

2007-08-03abs ↗pdf ↗

This paper uses a diffusion model to forecast electrical loads with uncertainty.

problem Uncertainties in electrical load forecasting due to renewable energy and external events.
method Diffusion-based Seq2Seq structure for epistemic uncertainty and robust additive Cauchy distribution for aleatoric uncertainty.
result Ability to separate and quantify both types of uncertainties in load forecasting.

Framework uses expert intervention to solve long-horizon reinforcement learning tasks.

problem Long horizon robot learning tasks with sparse rewards.
method Option templates and expert intervention to enable high-level task understanding.
result Framework outperforms state-of-the-art approaches by two orders of magnitude.

New algorithm for decentralized matching markets without prior preference rankings.

problem Decentralized two-sided matching markets without known preference rankings.
method Epoch-based CA-ETC algorithm for decentralized matching markets.
result Achieves player optimal expected regret of O(T_0 (K log T / T_0 Δ^2)^(1/γ) + T_0 (T / T_0)^γ).

Algorithm reduces regret in partially observable systems by learning dynamics and using optimistic control.

problem Minimizing regret in partially observable linear quadratic control systems with unknown dynamics.
method ExpCommit algorithm that learns model parameters and uses optimism in uncertainty.
result End-to-end sublinear regret upper bound of O~(T2/3)\tilde{\mathcal{O}}(T^{2/3}) for ExpCommit.

Study uses LLMs to categorize financial tweets, revealing useful sentiment signals.

problem Discovering meaningful sentiment signals from unstructured financial social media data.
method Leveraged LLMs to automatically label financial tweets with event categories and aligned with returns.
result Certain event labels consistently yield negative alpha, with statistically significant Sharpe ratios and information coefficients.

New framework tackles submodular welfare with multi-agent combinatorial bandits.

problem Maximizing total welfare among agents with shared constraints and submodular utilities under bandit feedback.
method Proposes an explore-then-commit strategy with randomized assignments for multi-agent combinatorial bandits.
result Achieves ildeO(T2/3) ilde{\mathcal{O}}(T^{2/3}) regret, first for partition-based submodular welfare problem under bandit feedback.

PRISM infers model structures and parameters from simulations, controlling complexity at test time.

problem Choosing among large model families for scientific discovery.
method Simulation-based encoder-decoder that infers model structures and parameters, with test-time complexity control.
result PRISM scales to large model families and performs model selection in biophysical diffusion MRI.

New bounds for high-dimensional sparse linear bandits, balancing information and regret.

problem Stochastic linear bandits with high-dimensional sparse features.
method Derivation of minimax regret lower and upper bounds for explore-then-commit algorithm.
result Optimal rate of Θ(n2/3)Θ(n^{2/3}) for data-poor regime, complemented by O(n)O(\sqrt{n}) under signal magnitude assumption.

Study integrates reliability constraints into generation planning models.

problem Challenges in integrating reliability constraints with generation planning models.
method Leverages a weighted oblique decision tree (WODT) technique to embed reliability verification constraints.
result Demonstrates effectiveness in achieving reliable and optimal planning solutions.

SafeML monitors ML systems for safety and security risks.

problem Ensuring safety and explainability of ML systems in safety-critical domains.
method Statistical difference measures of ECDF to detect distributional shifts.
result Approach can detect invalid application contexts of ML components.

This paper introduces modal epistemic tools for risk management.

problem Identifying and certifying risk claims when institutions lack the necessary epistemic stance.
method Develops crisp and fuzzy modal semantics for assurance and working commitment, distinguishing between object-level risk claims and meta-level epistemic diagnostics.
result Risk governance should model evidential incompleteness and failures of escalation, not just hazards and losses.

PFPN uses particle filtering to improve character control in physics-based simulations.

problem Premature commitment to suboptimal actions in high-dimensional continuous control problems for articulated characters.
method Proposes a particle-based action policy using particle filtering to dynamically explore and discretize the action space.
result Demonstrates better imitation performance and robustness to external perturbations compared to Gaussian policies.

Paper studies zero-sum games with noisy observations and identifies equilibrium conditions.

problem Zero-sum games with noisy observations of the leader's actions.
method Analyzes the equilibrium of games with noisy action observability, identifies necessary conditions for uniqueness, and investigates the cardinality of best responses.
result The noisy observations significantly impact the cardinality of the follower's set of best responses, and under certain conditions, this set becomes a singleton almost surely.

The paper minimizes Borda regret in dueling bandits models.

problem Minimizing Borda regret in dueling bandits models.
method Proposes explore-then-commit and EXP3-type algorithms for stochastic and adversarial settings respectively.
result Achieves nearly matching regret upper bounds of O(d2/3T2/3)O(d^{2/3} T^{2/3}) for both settings.

Investors benefit from long horizons in a market with mean-reverting equity returns.

problem Optimal portfolio choice in a market with mean-reverting risk-free rate and equity risk-premium.
method Mean-variance optimization, Euler-Lagrange equation, Calculus of Variations, spectral problem.
result Optimal policies are characterized by eigenvalues of the lambda-matrix, leading to better risk-return trade-offs for long-term investors.

In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…

2010-02-11abs ↗pdf ↗

Generative model predicts financial market order flow with high accuracy.

problem Creating realistic order flow models for financial markets.
method Token-level autoregressive generative model using deep state space layers.
result Model generates high-quality order flow data with low perplexity.