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Researcher profile

Jerry Huang

Jerry Huang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and LanguageCryptography and SecurityDiscrete MathematicsMachine Learningmath.CO

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Published work

4 published item(s)

preprint2026arXiv

Herculean: An Agentic Benchmark for Financial Intelligence

As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carry out financial professional work. Existing financial benchmarks offer only a partial view of this ability, as they primarily evaluate static competencies such as question answering, retrieval, summarization, and classification. We introduce Herculean, the first skilled benchmark for agentic financial intelligence spanning four representative workflows, including Trading, Hedging, Market Insights, and Auditing. Each workflow is instantiated as a standardized MCP-based skill environment with its own tools, interaction dynamics, constraints, and success criteria, enabling consistent end-to-end assessment of heterogeneous agent systems. Across frontier agents, we find agents perform relatively well on Trading and Market Insights, but struggle substantially on Hedging and Auditing, where long-horizon coordination, state consistency, and structured verification are critical. Overall, our results point to a key gap in current agents in turning financial reasoning into dependable workflow execution in high-stakes financial workflows.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning

Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite increasing advances in foundation model research, the relationship between such large language models (LLMs) and their calibration remains an open area of research. In this work, we look at a critical gap in the calibration of LLMs within multilingual settings, in an attempt to better understand how the data scarcity can potentially lead to different calibration effects and how commonly used techniques can apply in these settings. Our analysis on two multilingual benchmarks, over 29 and 42 languages respectively, reveals that even in low-resource languages, model confidence can increase significantly after instruction-tuning on high-resource language SFT datasets. However, improvements in accuracy are marginal or non-existent, resulting in mis-calibration, highlighting a critical shortcoming of standard SFT for multilingual languages. Furthermore, we observe that the use of label smoothing to be a reasonable method alleviate this concern, again without any need for low-resource SFT data, maintaining better calibration across all languages. Overall, this highlights the importance of multilingual considerations for both training and tuning LLMs in order to improve their reliability and fairness in downstream use.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Audited Skill-Graph Self-Improvement for Agentic LLMs via Verifiable Rewards, Experience Synthesis, and Continual Memory

Reinforcement learning is increasingly used to transform large language models into agentic systems that act over long horizons, invoke tools, and manage memory under partial observability. While recent work has demonstrated performance gains through tool learning, verifiable rewards, and continual training, deployed self-improving agents raise unresolved security and governance challenges: optimization pressure can incentivize reward hacking, behavioral drift is difficult to audit or reproduce, and improvements are often entangled in opaque parameter updates rather than reusable, verifiable artifacts. This paper proposes Audited Skill-Graph Self-Improvement (ASG-SI), a framework that treats self-improvement as iterative compilation of an agent into a growing, auditable skill graph. Each candidate improvement is extracted from successful trajectories, normalized into a skill with an explicit interface, and promoted only after passing verifier-backed replay and contract checks. Rewards are decomposed into reconstructible components derived from replayable evidence, enabling independent audit of promotion decisions and learning signals. ASG-SI further integrates experience synthesis for scalable stress testing and continual memory control to preserve long-horizon performance under bounded context. We present a complete system architecture, threat model, and security analysis, and provide a fully runnable reference implementation that demonstrates verifier-backed reward construction, skill compilation, audit logging, and measurable improvement under continual task streams. ASG-SI reframes agentic self-improvement as accumulation of verifiable, reusable capabilities, offering a practical path toward reproducible evaluation and operational governance of self-improving AI agents.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A method for eternally dominating strong grids

In the eternal domination game, an attacker attacks a vertex at each turn and a team of guards must move a guard to the attacked vertex to defend it. The guards may only move to adjacent vertices and no more than one guard may occupy a vertex. The goal is to determine the eternal domination number of a graph which is the minimum number of guards required to defend the graph against an infinite sequence of attacks. In this paper, we continue the study of the eternal domination game on strong grids. Cartesian grids have been vastly studied with tight bounds for small grids such as $2\times n$, $3\times n$, $4\times n$, and $5\times n$ grids, and recently it was proven in [Lamprou et al., CIAC 2017, 393-404] that the eternal domination number of these grids in general is within $O(m+n)$ of their domination number which lower bounds the eternal domination number. Recently, Finbow et al. proved that the eternal domination number of strong grids is upper bounded by $\frac{mn}{6}+O(m+n)$. We adapt the techniques of [Lamprou et al., CIAC 2017, 393-404] to prove that the eternal domination number of strong grids is upper bounded by $\frac{mn}{7}+O(m+n)$. While this does not improve upon a recently announced bound of $\lceil\frac{m}{3}\rceil \lceil\frac{n}{3}\rceil+O(m\sqrt{n})$ [Mc Inerney, Nisse, Pérennes, CIAC 2019] in the general case, we show that our bound is an improvement in the case where the smaller of the two dimensions is at most $6179$.

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