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The restless multi-armed bandit (RMAB) framework is a popular model with applications across a wide variety of fields. However, its solution is hindered by the exponentially growing state space (with respect to the number of arms) and the combinatorial action space, making traditional reinforcement learning methods infeasible for large-scale instances. In this paper, we propose GINO-Q, a three-timescale stochastic approximation algorithm designed to learn an asymptotically optimal index policy for RMABs. GINO-Q mitigates the curse of dimensionality by decomposing the RMAB into a series of subproblems, each with the same dimension as a single arm, ensuring that complexity increases linearly with the number of arms. Unlike recently developed Whittle-index-based algorithms, GINO-Q does not require RMABs to be indexable, enhancing its flexibility and applicability. Our experimental results demonstrate that GINO-Q consistently learns near-optimal policies, even for non-indexable RMABs where Whittle-index-based algorithms perform poorly, and it converges significantly faster than existing baselines.

AAAI 2026

GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits

technical paper

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.<br><br>

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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.

This work presented a web-based system which introduces an active-listening strategy in a spoken dialogue for self-disclosure to support mental health of a campus user. To enhance the system usability and safety, this demo is developed to conduct the bilingual (Mandarin/English) spoken dialogue where a high-risk dialogue detection during speech interaction is reliably augmented. In particular, a prompt-driven GPT classifier identifies the utterances indicating self-harm or suicide intent and triggers safety alerts with help center and counselor notification. We also integrate a TTS module for Taiwanese Mandarin and standard English, and redesign the user interface to automatically pop up alert messages when high-risk dialogue is detected. In addition, we collect speech data under diverse mental dialogue scenarios with bilingual speech to enable system analysis, evaluation and refinement. Overall, these extensions build a framework that promotes empathetic interactions, enables timely alert in critical cases, and improves the accessibility for diverse users.

Risk-Aware Bilingual Spoken Dialogue for Campus Mental Health Support

Scientific research articles, typically distributed in PDF format, contain valuable knowledge but remain challenging to convert into structured datasets due to fragmented workflows that separate parsing, annotation, and visualization. Existing annotation platforms operate on plain text, which requires an additional PDF-to-text conversion step before annotation, while PDF parsing tools lack automated annotation suggestions. To bridge this gap, we introduce Docora, a system that unifies PDF parsing, automated annotation assistance, and multi-view visualization into a single interactive platform. Docora enables researchers to configure entity and relation schemas for any domain, automatically generates initial annotations using rule-based, model-based, or LLM-based extractors, and provides synchronized visualizations across PDF, text, and graph views. Users can refine annotations directly on the PDF canvas, ensuring consistency between document layout and structured representations. The system’s source code is publicly available to facilitate further research and development.

Docora: A System for Interactive Knowledge Extraction and Visualization from Scientific PDFs

Event detection is essential for surveillance, particularly in retail loss prevention where accurate, timely monitoring is critical. Large vision–language models (VLMs) provide strong generalization but are inefficient on video streams and prone to hallucinations from redundant frames. We present \textbf{SmartEyes}, a plug-and-play system for real-time retail surveillance. SmartEyes introduces \textbf{Perception–Cognition Focusing (PCF)}, which combines lightweight perception with semantic triggering to isolate two keyframes—customer contact and departure—and constrain the VLM to a focused differencing task. This design reduces hallucination while enabling efficient reasoning. Our demo features a SAM-powered ROI interface and live CCTV monitoring, achieving accurate alerts within 1–2 seconds on a single RTX 4080 GPU.

SmartEyes: Plug-and-Play Event Detection for Retail Loss Prevention

We present DS SERVE, a framework that transforms large-scale text datasets—comprising half a trillion tokens—into a high-performance neural retrieval system. DS SERVE offers both a web interface and API endpoints, achieving low latency with modest memory overhead on a single node. The framework also supports inference-time tradeoffs between latency, accuracy, and result diversity. We anticipate that DS SERVE will be broadly useful for a range of applications such as large-scale retrieval-augmented generation (RAG), training data attribution, training a search agent, and beyond.

DS SERVE: A Framework for Efficient and Scalable Neural Retrieval

Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants.

A Visualized Framework for Event Cooperation with Generative Agents

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.

AutoTuneX: Interactive Automated Fine-Tuning for Large Language Models

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

Large Language Models (LLMs) are vulnerable to adversarial attacks that bypass safety guidelines and generate harmful content. Mitigating these vulnerabilities requires defense mechanisms that are both robust and computationally efficient. However, existing approaches either incur high computational costs or rely on lightweight defenses that can be easily circumvented, rendering them impractical for real-world LLM-based systems. In this work, we introduce the AlignTree defense, which enhances model alignment while maintaining minimal computational overhead. AlignTree monitors LLM activations during generation and detects misaligned behavior using an efficient random forest classifier. This classifier operates on two signals: (i) the refusal direction - a linear representation that activates on misaligned prompts, and (ii) an SVM-based signal that captures non-linear features associated with harmful content. Unlike previous methods, AlignTree does not require additional prompts or auxiliary guard models. Through extensive experiments, we demonstrate the efficiency and robustness of AlignTree across multiple LLMs and benchmarks.

AlignTree: Efficient Defense Against LLM Jailbreak Attacks

Humanoid robots are expected to operate reliably over long horizons while executing versatile whole-body skills.
Yet Reinforcement Learning (RL) motion policies typically lose stability under prolonged operation, sensor/actuator noise, and real world disturbances.
In this work, we propose a Selective Adversarial Attack for Robust Training (SA2RT) to enhance the robustness of motion skills.
The adversary is learned to identify and sparsely perturb the most vulnerable states and actions under an attack-budget constraint, thereby exposing true weakness without inducing conservative overfitting.
The resulting non-zero sum, alternating optimization continually strengthens the motion policy against the strongest discovered attacks.
We validate our approach on the Unitree G1 humanoid robot across perceptive locomotion and whole-body control tasks.
Experimental results show that adversarially trained policies improve the terrain traversal success rate by 40\%, reduce the trajectory tracking error by 32\%, and maintain long horizon mobility and tracking performance.
Together, these results demonstrate that selective adversarial attacks are an effective driver for learning robust, long horizon humanoid motion skills.

Keep On Going: Learning Robust Humanoid Motion Skills via Selective Adversarial Training

In this paper, we describe and benchmark a competitor-discovery component, an essential part of an agentic AI system for fast drug asset due diligence. A competitor-discovery AI agent, given an indication, retrieves all drugs comprising the competitive landscape of that indication and extracts canonical attributes for these drugs. The competitor definition is investor-specific, and data is paywalled/licensed, fragmented across registries, ontology-mismatched by indication, alias-heavy for drug names, multimodal, and rapidly changing. Although considered the best tool for this problem, the current LLM-based AI systems aren’t capable of reliably retrieving all competing drug names, and there is no accepted public benchmark for this task. To address the lack of evaluation, we use LLM-based agents to transform five years of multi-modal, unstructured due diligence memos from a private biotech VC fund into a structured evaluation corpus mapping indications to competitor drugs with normalized attributes. We also introduce a competitor validating LLM-as-a-judge agent that filters out false positives from the list of predicted competitors to maximize precision and suppress hallucinations. On our benchmark, our competitor-discovery agent achieves 83\% recall, exceeding OpenAI Deep Research (65\%) and Perplexity Labs (60\%). The system is deployed in production with enterprise users; in a case study with a biotech VC investment-fund, analyst turnaround time dropped from 2.5 days to $\sim$3 hours ($\sim$20x) for the competitive analysis.

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