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ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
The paper introduces ClinFusion, a multimodal large language model designed for holistic medical understanding. It can handle both 2D and 3D medical images and outperforms other open-source and proprietary models on…
Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects
A study explores the inconsistencies in linking sparse autoencoders (SAEs) with model behavior, focusing on the geometry of changes in model logits caused by feature interventions. The authors propose an unsupervised…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method for identifying parts of a text generated by Language Model (LLM) at the token level, improving on existing document-level methods. The method is based on smoothing adjacent token scores…
Expert Behavior Prior Reinforcement Learning
The paper proposes a new method called Expert Behavior Prior (EBP) for behavior prior reinforcement learning (BPRL). EBP aims to improve the effectiveness of policy priors by generating expert policy priors directly…
MIRROR: Learning from the Other View for Multi-Modal Reasoning
The paper presents MIRROR, a method to improve multi-modal reasoning in AI models by learning from different views (text, image, combined text and image) of the same problems. The study reveals that vision-language…
Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference
The researchers propose a decentralized collaborative learning paradigm for an ensemble of Tsetlin Machines (TMs) using consensus-based inference. Each agent has its own private TM model, and they combine individual…
Decentralized Online Riemannian Optimization for Strongly Geodesically Convex Functions
The authors study decentralized online optimization for specific types of functions (strongly geodesically convex) on Riemannian manifolds with certain curvature properties. They propose a new network-error analysis to…
Instance Hardness-Based Relevance for Imbalanced Regression
A new approach called Instance Hardness-based relevance function (InHaR) is proposed to identify rare instances in regression problems, particularly useful for complex scenarios like bimodal distributions where…
Instance Hardness-Based Relevance for Imbalanced Regression
The paper proposes a new relevance function called Instance Hardness-based relevance (InHaR) for identifying rare instances in imbalanced regression problems. This function takes into account the learning difficulty of…
Local Stability and Gaussian Smoothing of Quantized Neural Networks
The authors investigate using Gaussian averaging as a smooth alternative for quantized neural networks. They provide bounds on the difference between the original function (f) and the smoothed version (g), linking it…
Local Stability and Gaussian Smoothing of Quantized Neural Networks
The authors study the use of Gaussian averaging as a smoother alternative for quantized neural networks. They derive a bound on the difference between the original function and the smoothed version under certain…
On Optimization Complexity of Second-Order Certified Unlearning
The authors investigate the complexity of removing trained data from machine learning models, focusing on certified unlearning and optimization accuracy. They propose new bounds on the distance between initial and…
Statistical Inference for Rank Allocation in Low-Rank Adaptation
In this paper, the authors propose StatLoRA, a statistical inference-based rank allocation method for low-rank adaptation (LoRA) in large language models. The proposed method uses estimated p-values to determine which…
Statistical Inference for Rank Allocation in Low-Rank Adaptation
Researchers propose StatLoRA, a statistical inference-based method for rank allocation in low-rank adaptation (LoRA). This method aims to balance efficiency, expressiveness, and generalization by using estimated…
Variance-reduced Domain Adaptation using Paired Sampling
The paper proposes Paired Sampling for Domain Adaptation (PSDA), a new stochastic variance reduction technique designed for distribution-matching frameworks in unsupervised domain adaptation (UDA) that suffer from high…
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…
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…
AdaHome: An Adaptive Smart Home Assistant using Local Small Language Models
A paper titled 'AdaHome: An Adaptive Smart Home Assistant using Local Small Language Models' presents a new smart home assistant system designed to work with locally deployed small language models in…
Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
The paper introduces an LLM-based system to detect online firestorms (rapid escalations of negative user-generated content) on Reddit. The system operates in two modes: retrospective thread classification and early…
Latent Trajectory Discrimination for AI-Generated Text Detection
In this paper, the authors propose a new approach for detecting AI-generated text called Geometric Trajectory and Contrastive Learning (GTCL). Unlike existing methods that focus on aggregate statistics or global…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The authors propose a new method called RAPTOR for improving the efficiency of target-oriented path exploration in temporal knowledge graphs (TKG). The method injects a reachability-aware inductive bias to the agent…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
The paper proposes a new method for detecting text generated by large language models (LLMs) and attributing authorship to them. The method goes beyond just analyzing surface-level linguistic features and instead…
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
The paper proposes a new framework called Memory as a Controlled Process (MemCon) for large language model (LLM) agents that use external memory systems. MemCon learns an online policy to adaptively decide when, what…
Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild
The paper presents a new framework called AgentHOI that uses multimodal large language models (MLLMs) to detect human-object interactions (HOIs) in complex scenes without the need for any HOI data during training. The…
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