Singapore

We present AutoTuneX, a system architecture design and implementation for users to interactively fine-tune large language models (LLMs) based on automated hyperparameter optimization particularly built around Bandit Limited Discrepancy Search (Kishimoto et al. 2022). Next to a classical Graphical User Interface (GUI) our system features an agentic runtime to facilitate automated fine-tuning via chat.

AAAI 2026

AutoTuneX: Interactive Automated Fine-Tuning for Large Language Models

automatic grading large model fine-tuning smart education

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/a3db7a768409f05192210d98601edb25.png)](https://emnlp2022.rocket.chat/)

To access this site you need to register. Please register [here](https://2022.emnlp.org/registration/).

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EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

demo

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The dynamic nature of cloud spending and pricing structures
pose challenges for practitioners in IT Financial Operations
(FinOps). Recent advances in agentic systems enables them
to instead rely on agents for complex FinOps tasks such as
drawing insights from their data through natural language
queries. In this work, we present an IT FinOps Data Insights
Agent, that implements “chat with your data” approach to
support practitioners in their daily tasks. Our agent achieves
up to 90% accuracy across ITBench FinOps scenarios.

Agentic Solutions for IT Financial Operations

Accurate citation is critical, yet error rates remain high across scientific literature. We present RefLens, an end-to-end system that automates citation verification from PDF parsing to interactive report generation. Unlike summary- or embedding-based approaches, RefLens performs evidence-grounded verification by extracting verbatim spans from original sources and displaying citation-level cards and a paper-level dashboard. In a 35-participant study, users rated value (M=4.34), trust (M=4.15), and usability (M=4.19) highly, with strong adoption intention (M=4.28).

RefLens: End-to-End Evidence-Grounded Citation Verification with LLM Agents

In this paper, we present the development of an automated visual inspection system for detecting defects on the upper airframe surface. The system employs a multi-camera PTZ system to synchronously capture and process images at designated regions. Developed software handles path planning and camera localization, while a hybrid-AI framework is incorporated to detect various defect-types, such as hairline cracks, loose screws and bird strike damage. The demonstration showcases the detection capabilities and prototype functionalities on a large aircraft model, furnished with a user interface to run system features and visualize results. To support this work, performance testing is provided against relevant models.

Automated Multi-Camera Inspection System for Aircraft

The demo presents a tool that visualizes acting of planning agents in dynamic environments that might be modified by ``acts of nature'', The purpose of this tool is to better understand the behavior of the agent, debug agent's behavior, and for making the underlying planning concepts accessible to wider audience.

PANSim: Visualization Tool for Planning and Acting against Nature

Nature is inherently structured! The entities in the real world are naturally organized in rich relationships. For example, dolphins and sharks, despite their striking visual resemblance in body shape and fins, are actually from entirely different branches of the animal hierarchy, i.e., mammals and fishes, respectively. This remarkable similarity is a prime example of ‘convergent evolution’, where unrelated species develop similar features because they face similar environmental challenges. This illustrates how nature’s underlying organization often transcends superficial visual resemblances. Although humans intuitively grasp and utilize these profound natural constraints, they are typically underutilized in most AI systems. As a result, trained AI models tend to align with
statistical patterns in the data, such as sampling biases or class imbalance, rather than adhering to the underlying relational consistency. This thesis argues that AI systems must evolve beyond learning “flat” feature representations, which are domain-agnostic and derived purely from data correlations, to “explicitly model the domain-specific structural relationships”. A key benefit of encoding relational priors in the learning process is that it can inject domain knowledge as an inductive bias, leading to more robust and reliable models. My research investigates incorporating domain knowledge by leveraging “graph-based structural priors” that explicitly model relational constraints in various visual recognition tasks. This work spans three distinct dimensions of visual recognition, progressing from coarse-level (image-level) to fine-grained (scene-level) understanding. My research highlights a crucial limitation in existing AI models: they often fail to incorporate real-world constraints, leading to significant errors. I show that even powerful, pre-trained neural networks can make severe mistakes due to a lack of domain knowledge. I argue that
standard metrics like top-1 accuracy, precision, and recall are insufficient for evaluating model robustness, and propose a new metric based on rank order of the predictions as a better indicator of reliability. The benchmark on various large-scale datasets confirms that existing solutions do not sufficiently capture the domain knowledge, which is often available as a taxonomy tree, motivating our design of better learning frameworks. I also examine complex visual re-identification (Re-ID) tasks, such as monitoring animals in the wild. I find that existing foundational models struggle with new species and environments. This challenge is compounded by the high cost of manual annotation for adapting these systems to new settings. While existing unsupervised learning methods can help reduce the need for extensive labeling, they often suffer from under- and over-segmentation errors, which led me to develop more effective active learning strategies. Finally, I address the limitations of the classic Kalman filter, a widely used tool for dynamic systems. I point out that this filter makes a flawed assumption that the movement of each individual object is independent of its
dynamic surroundings. In the real world, this is rarely the case. I demonstrate the need for a new filtering mechanism that not only considers an object’s past movements but also its spatial relationship with other dynamic entities in its environment. In my analysis, I observed the vision foundation models for all recognition tasks, i.e., classification, detection, and segmentation, lack the domain knowledge. I believe that our learning framework, which was designed specifically for classification, can be adapted for other recognition tasks. I speculate that a unified learning framework can be designed that can be leveraged for making vision foundation models aware of the available taxonomy.

Exploiting Graph-Based Structural Priors for Visual Recognition

My research aims to pioneer efficient and reliable wearable
intelligence algorithms that transform healthcare robotics
into adaptive, patient-centered systems.
I take a four-step approach: (1) design multimodal wearable
sensing platforms to capture human and biometric signals;
(2) train a foundation model that learns from these rich
datasets to reason about human behaviors and health states;
(3) validate the model through large-scale simulation and
principled uncertainty quantification; and (4) deploy it in
rehabilitation and assistive robots for intelligent,
personalized care.
This research not only advances fundamental understanding
of multimodal human behavior, but also opens new pathways
for early disease diagnosis, adaptive treatment, and
accessible digital health.
By bridging AI, wearables, and robotics, my work aspires to
lay the groundwork for the next generation of healthcare
technologies that are proactive, trustworthy, and deeply
aligned with human well-being.

Wearable Intelligence for Healthcare Robotics: From Brain Activity to Body Movements

Deep neural networks (DNNs) have revolutionized machine
learning, driving breakthroughs from image classification
to autonomous vehicles. However, a critical flaw undermines
their reliability. Most DNNs operate under the unrealistic
closed-set assumption that all potential classes have been
encountered during training. This ignores the inevitability
of outliers in real-world scenarios. In safety-critical
domains like autonomous driving, this oversight can have
dire, irreversible consequences. DNNs may confidently
misclassify unknown outlier inputs as familiar classes.
Addressing this vulnerability is essential for public trust
and the adoption of Artificial Intelligence (AI) in
high-stakes environments. Out-of-distribution (OOD)
detection has therefore emerged as a linchpin for the safe
and dependable deployment of intelligent systems. This
thesis tackles the urgent need for robust OOD detection. It
presents three innovative contributions that elevate the
field and set new standards for reliability and safety
across real-world contexts.

First, we confront the common yet unrealistic
dataset-dependent OOD detection splitting definition that
one labeled dataset is in-distribution (ID) and all the
unlabeled datasets are OOD, under the impractical
assumption that training data is clean, balanced.We
introduce two novel frameworks to handle these
complexities. Most real-world applications follow the
semantically coherent OOD detection splitting definition,
where some ID samples appear in these unlabeled datasets.
The Adaptive Hierarchical Graph Cut (AHGC) network resolves
multi-granularity label discrepancies between labeled and
unlabeled datasets. It effectively identifies semantically
coherent OOD samples that other methods misclassify.
Complementing this, the Uncertainty-aware Adaptive Semantic
Alignment (UASA) network tackles cross-domain and
class-imbalanced data. It pioneers a prototype-based
alignment strategy that closes the domain gap and is robust
to imbalanced classes, addressing OOD detection and ID
classification in the unlabeled target domain.

Second, we address the significant practical limitation of
data scarcity by venturing into the domain of few-shot OOD
detection. Recognizing that most existing methods require
extensive labeled in-distribution data, we developed the
Adaptive Multi-prompt Contrastive Network (AMCN). This
model uniquely leverages the power of large-scale
vision-language models (CLIP) to generate adaptive textual
prompts for both in-distribution and out-of-distribution
classes. By learning a discriminative class boundary from
only a handful of samples, AMCN effectively compensates for
the scarcity of training data and corresponding labels,
marking a significant step towards data-efficient OOD
detection.

Third, we extend the scope of OOD detection from static
images to dynamic, complex video scenarios. We introduce
the novel task of OOD Action Detection (ODAD) in untrimmed
videos. To solve this, we propose the Uncertainty-Guided
Appearance-Motion Association Network (UAAN). This approach
reasons over spatial-temporal inter-object interactions by
synergistically modeling appearance and motion features. It
allows for the simultaneous localization and identification
of both known (in-distribution) and unknown
(out-of-distribution) actions. This is a critical
capability for safety-critical applications like autonomous
driving.

Collectively, these contributions redefine the landscape of
OOD detection across four pivotal dimensions: semantic
granularity understanding, cross-domain robustness, data
efficiency, and temporal dynamics modeling. The
methodologies introduced not only surpass existing
benchmarks but also prove their value in diverse,
real-world settings where robustness is non-negotiable.

Advancing Out-of-Distribution Detection Across Diverse Scenarios

Recently, deep Reinforcement Learning (RL) methods have
been widely used in labor management within transportation
gig markets, such as ride-hailing, food delivery, and
express delivery. Compared to traditional rule-based and
optimization-based methods, RL can capture more information
about long-term uncertainty and environmental dynamics,
leading to better and non-myopic strategies. However, deep
learning methods have long been criticized for their low
interpretability, raising concerns about algorithmic
discrimination in gig markets. Currently, most works focus
on this issue from the perspective of statistical analysis
and surveys. However, the underlying reasons related to the
algorithms remain unclear, as most companies do not
disclose their algorithms. This lack of transparency can
hinder governments from designing efficient management
policies to address these problems. To fill this research
gap, this thesis proposal aims to develop appropriate RL
methods to mimic the labor management behavior of
transportation gig platforms and to propose effective
policies that protect the rights of gig workers.

Towards Fairness in Transportation Gig Markets: Identifying, Imitating, and Mitigating Algorithm Discrimination via Deep Reinforcement Learning

AI systems often fail on challenging or out-of-distribution inputs—a critical limitation in domains such as healthcare, finance, and autonomous driving. Learning to Defer (L2D) addresses this by training models not only to predict but also to decide when to defer to external experts. This thesis develops a unified and robust framework for L2D that advances its theoretical foundations, reliability, and applicability. It characterizes Bayes-optimal routing policies, establishes surrogate-consistency guarantees, and introduces a unified adversarial framework for attacking and defending L2D with Bayes-optimal robustness. It further proposes the first top-k deferral methods in both two-stage and one-stage settings. Empirical studies validate these ideas in multi-task learning and extractive question answering with large language models. Ongoing work explores token-level routing in LLMs, online adaptation with dynamic experts, and partial deferral.

Towards Robust Human–AI Decision-Making via Learning-to-Defer

While Reinforcement Learning (RL) has demonstrated remarkable success in solving complex sequential decision-making problems, its application in real-world, safety-critical systems is hindered by its reliance on carefully engineered reward functions. Designing effective rewards is notoriously challenging and can lead to unintended or unsafe behaviors — a phenomenon known as reward hacking. Specification-guided RL has emerged as a principled alternative, leveraging formal methods to directly encode high-level objectives, safety requirements, and behavioral constraints. However, the practical utility of this approach is often limited by coarse or under-specified logical formulas and the computational challenge of enforcing safety at scale. This thesis addresses these limitations by developing a unified framework for the automated refinement, scalable enforcement, and flexible adaptation of formal specifications in RL.

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A Critical Look at Fine-tuning Generalization: Are Patterns All We Need?

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