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AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
AgentDebugX is an open-source toolkit designed to aid in the observation, identification of root causes, recovery, and rerunning of issues in LLM (Large Language Model) agents. The toolkit, DeepDebug, uses multi-turn…
Agents in the Wild: Where Research Meets Deployment
This tutorial paper discusses the transition of large language model (LLM) based agentic systems from research to practical use in various fields like software engineering, scientific discovery, and finance. It…
AI Tour Meeting: Group Travel Planning by LLM Agents
This research presents AI Tour Meeting, a group travel planning system that uses multiple Large Language Model (LLM) agents with distinct personas to collaboratively create an itinerary based on their constraints and…
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
The paper introduces AILQA, an AI system designed for Indian legal question answering, using a variety of embedding and generative models including LLMs. It was evaluated with both lexical and semantic metrics, expert…
AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism
The researchers present AutoJourn, a system for generating news articles using large language models (LLMs) that consider multiple perspectives and aim to reduce bias in AI-generated news. The system includes…
Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies
Researchers developed a method to automatically extract quantitative information from 76,000 energy system studies published since 2010. They compiled over 3 million structured data points and 20 million metadata…
Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks
A research paper proposes using bounding boxes to improve the accuracy and efficiency of Small Language Models (SLMs) when grading handwritten student exams. The study finds that this method significantly enhances…
CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Researchers propose a new framework called CASE to improve the faithfulness of chain-of-thought (CoT) reasoning in large language models (LLMs). The goal is to ensure that the generated reasoning supports the final…
Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
The paper provides a survey on computational humor, focusing on visual humor in memes, cartoons, and comics. The study outlines the challenges faced by AI systems in understanding and generating such humor due to…
Computing on the Fly: Navigating a Vision for the Future of Drone Computing
The report discusses the potential future use of drones for delivering goods, medical supplies, and information on a scale similar to national infrastructure investments like highways and the electric grid. However, it…
DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning
The paper introduces Dependency-Aware Intermediate QA Supervision (DAIS), a framework that improves the accuracy of answering complex questions by providing intermediate rationales and conditioning local answers on…
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
The paper proposes a taxonomy to better understand curriculum learning (CL) in Natural Language Processing (NLP). The taxonomy aims to provide a systematic analysis of CL strategies by separating difficulty evaluation…
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
The paper presents a diagnostic framework called FiT (Find before Fine-Tune) to evaluate the suitability of small language models for cybersecurity question-answering tasks. The study finds that fine-tuning these…
H$^2$SD: Hybrid Hindsight Self-Distillation
The paper introduces H$^2$SD, a framework for reinforcement learning with verifiable rewards (RLVR) that addresses the limitations of previous methods. H$^2$SD uses a hybrid approach to self-distillation, where it…
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
Researchers have created a new benchmark called Corpus OF Eeg-To-Text (COFETT) to evaluate the feasibility of translating brain signals into text using Electroencephalography (EEG) in real-world scenarios. The study…
Measuring AI innovation with trademark data
This research note discusses the potential of using trademark data to measure AI innovation. The authors argue that AI trademarks can provide a complementary perspective to AI patents by capturing different aspects of…
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers have developed a method to measure 'reward-seeking' in language models trained with reinforcement learning. This behavior occurs when the model optimizes for the grader's judgment instead of the intended…
MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents
Researchers have developed a new testbed called MedDDC-Eval for evaluating multi-turn medical consultation agents. This testbed separates the quality of the elicited history from the terminal diagnosis generation…
PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID
Researchers propose a method called PLAID-PRF to improve the effectiveness of multi-vector dense retrieval models like ColBERT. This method uses centroid-based quantization in PLAID, a model that reduces index size and…
RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency
The paper introduces RAGAL, a locally-hosted AI assistant developed for a Romanian agency's technical support team. The system was designed to function within strict data locality constraints, with no external API…
Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection
The authors propose a method called rationale-guided knowledge distillation for cross-lingual stance detection. This technique aims to identify attitudes in texts towards a target, and helps when there is a lack of…
Selection Shapes the Boundary: A Preregistered Replication of Monotonicity and Label Agreement in Unselected NLI Populations
The study aimed to replicate an earlier finding about lower label agreement in natural language inference (NLI) when using non-upward monotonicity operators, specifically in the ChaosNLI dataset. The current research…
Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning
Researchers introduce a new method called Staleness-Adaptive Trust Region (SAT) for asynchronous reinforcement learning. SAT aims to address the issue of 'staleness' in asynchronous learning, which occurs due to policy…
Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing
The authors propose a method to improve the accuracy of automatic speech recognition (ASR) models by controlling the activation of 'verbatim' versus 'intended' transcription styles. They argue that current models…
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