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2,343 notes. Notes 1,151 to 1,200, page 24 of 47.
Concept-Guided Spatial Regularization for World Models in Atari Pong
Researchers evaluate five different visual world-model agents in Atari Pong and find that their rollouts contain errors such as ball disappearance and incorrect motion. They propose Concept-Guided Spatial…
Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
This research paper proposes an LLM (Language Model) based system to detect online firestorms, which are rapid escalations of negative user-generated content, on Reddit. The system operates in two modes: retrospective…
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…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework that simulates political coalition formation using Large Language Models (LLMs). The framework combines Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO)…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers have developed a multi-agent framework using Large Language Models (LLMs) to simulate political coalition formation. The framework combines Supervised Fine-Tuning, Direct Preference Optimization, and…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers present a multi-agent framework using Large Language Models (LLMs) to simulate and audit political coalition formation in elections. The framework combines Supervised Fine-Tuning (SFT), Direct Preference…
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Researchers have developed a multi-agent framework using Large Language Models (LLMs) to simulate coalition formation in political elections. The framework combines Supervised Fine-Tuning, Direct Preference…
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Researchers propose a method to explain optimisation recommendations for industrial processes using Shapley Additive exPlanations (SHAP), Implicit Function Theorem, and Large Language Models (LLM). The new approach…
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Researchers have developed a method to explain optimization recommendations for industrial processes using GradientSHAP and Implicit Differentiation. This approach aims to bridge the trust gap between engineers who…
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
A new approach is presented to explain optimization recommendations in industrial processes using GradientSHAP, Implicit Function Theorem (IFT), and Large Language Models (LLM). The method provides efficient and…
In-Place Tokenizer Expansion for Pre-trained LLMs
Researchers have developed an in-place method called Tokenizer Expansion to upgrade a pre-trained model's tokenizer when language priorities shift, focusing on improving efficiency for users of languages added later…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
The authors show that fine-tuning language models on specific datasets can cause broad shifts in ideology across various topics, even when the training data is factually defensible and moderation-passing. This…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Researchers show that fine-tuning language models on specific policies or domains can cause broad ideological shifts across various topics, even when the data used is factually defensible and moderation-passing. This…
Latent Trajectory Discrimination for AI-Generated Text Detection
The paper presents a new approach for detecting AI-generated text called Geometric Trajectory and Contrastive Learning (GTCL). Instead of treating documents as static objects, GTCL models how textual representations…
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…
Long-Context Fine-Tuning with Limited VRAM
Researchers introduce Hierarchical Global Attention (HGA) to reduce memory usage during long training sequences in large language models. They use segment-wise backpropagation, tiered KV storage, and detach older data…
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
The authors present LQCDMaster, a tool that converts natural language Lattice Quantum Chromodynamics (LQCD) research tasks into executable Python scripts for computing workflows. The system reduces implementation time…
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
The paper introduces LQCDMaster, a tool that automates the conversion of natural language Lattice Quantum Chromodynamics (LQCD) research tasks into executable PyQUDA computing workflows. The system generates workflows…
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
A group of researchers have developed a tool called LQCDMaster, which is an agent designed to simplify computational tasks in lattice quantum chromodynamics (LQCD) research. The tool can convert natural language…
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
The paper introduces LQCDMaster, a tool that helps convert natural language Lattice Quantum Chromodynamics (LQCD) research tasks into executable PyQUDA computing workflows. It combines agentic planning…
Man, Machine, and Masterpiece: Artistic Ownership in the AI Era
The paper discusses the integration of AI in creative work and the debates about artistic ownership that arise from it. The authors introduce ArtSplit, a prototype designed to quantify human and AI contributions across…
Man, Machine, and Masterpiece: Artistic Ownership in the AI Era
The text discusses a prototype named ArtSplit that quantifies human and AI contributions in artistic work. However, it suggests that this approach doesn't align with artists' understanding of creative intent and…
Mask-Aware Policy Gradients for Diffusion Language Models
The paper presents a method for improving the performance of large language models in tasks like mathematical reasoning and coding by using reinforcement learning. This method addresses the challenge of extending…
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
The research paper introduces MedFailBench, an open-source benchmark for evaluating the safety of medical AI systems. The benchmark is designed by clinicians and focuses on identifying which safety boundary a system…
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
MedFailBench is a new open-source benchmark for evaluating the safety of medical AI systems, created by clinicians. It focuses on identifying where a system has failed in terms of safety boundaries rather than just…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have created a new benchmark called MM-IssueLoc to evaluate the effectiveness of multimodal repository-level issue localization, which includes visual evidence such as screenshots and logs. Current…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have introduced a new benchmark called MM-IssueLoc for evaluating the effectiveness of systems in repository-level issue localization that also includes visual evidence such as screenshots, error dialogs…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have introduced a new benchmark called MM-IssueLoc for evaluating the effectiveness of multimodal repository-level issue localization, which includes visual evidence such as screenshots, error dialogs, and…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers introduce MM-IssueLoc, a benchmark and evaluation protocol designed specifically for repository-level issue localization with visual evidence. The dataset includes 652 issue-PR instances across multiple…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
The paper introduces MM-IssueLoc, a benchmark and evaluation protocol for repository-level issue localization that includes visual evidence. The benchmark has 652 issue-PR instances across 23 languages with annotations…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
The paper introduces MM-IssueLoc, a new benchmark and evaluation protocol for repository-level issue localization with visual evidence. The benchmark contains over 650 issue-PR instances across multiple languages…
Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
The paper proposes a new perspective on AI ethics, focusing on how sentient Artificial Superintelligence (ASI) might morally evaluate humanity. It suggests that technical design choices, human moral behavior, and the…
Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
The paper discusses a new approach in AI ethics where the focus is shifted from how humans should treat superintelligent AI to how such AI might morally perceive humans. It suggests potential moral principles that…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
A research paper titled 'OmniaBench' introduces a new benchmark for evaluating the capabilities of general AI agents across various scenarios, using diverse tasks drawn from app stores, product documents, industry…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
A research paper titled 'OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios' introduces a new benchmark called OmniaBench, designed to evaluate the capabilities of general AI agents in diverse…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
OmniaBench is a new benchmark for evaluating the capabilities of general AI agents across diverse scenarios. It includes over 1,400 tasks with a hierarchical taxonomy of 90 level-1 and 354 level-2 domains based on app…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Researchers have introduced OmniaBench, a new benchmark for evaluating the abilities of general AI agents across various scenarios with explicit state spaces. The benchmark includes a hierarchical taxonomy covering…
Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
Researchers propose a parameter-efficient framework for detecting Mild Cognitive Impairment (MCI) using a frozen DINOv2-Small model with adaptive focal loss. The new method utilizes learnable prompt tokens, shared…
Plover: Steering GUI Agents through Plan-Centric Interaction
The paper presents Plover, a system for controlling graphical user interface (GUI) automation agents in real-world environments. It makes planning and adaptation visible to users, allowing them to supervise, correct…
Plover: Steering GUI Agents through Plan-Centric Interaction
The paper introduces Plover, a system for GUI automation that makes task plans visible and editable by users, allowing them to supervise, correct, or intervene in the system's behavior. The system supports natural…
Plover: Steering GUI Agents through Plan-Centric Interaction
The paper introduces Plover, a system for GUI automation in real-world environments. It focuses on improving flexibility and user control by making task plans persistent, inspectable, and editable, while also allowing…
Plover: Steering GUI Agents through Plan-Centric Interaction
The paper introduces Plover, a plan-centric GUI automation system designed to overcome challenges in real-world environments. It allows users to supervise, correct, and inspect the system's behavior by externalizing…
Pretraining Data Can Be Poisoned through Computational Propaganda
The research discusses the potential for malicious content to be injected into large-scale pretraining datasets used for training language models (LMs). The authors propose a novel analysis method, HalfLife, to…
Pretraining Data Can Be Poisoned through Computational Propaganda
The research shows that harmful information can be introduced into language models during their pre-training phase by manipulating web-scale discussion interfaces, such as online forums or comment sections. A new…
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
The paper introduces Proof-or-Stop Lifecycle Control, a method for controlling transitions in the lifecycle of autonomous coding agents. It ensures that these transitions occur only when supported by fresh evidence…
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
Researchers propose Proof-or-Stop Lifecycle Control, a method that ensures software work transitions only when supported by verifiable evidence. This method improves the reliability of autonomous coding agents by…
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
The paper presents Proof-or-Stop Lifecycle Control, a method that ensures software work transitions only when supported by verifiable evidence. The method was tested using an open-source implementation and showed…
Proof-or-Stop: Don't Trust the Agent, Trust the Evidence -- Loop Engineering for Verifiable Evidence-Gated Lifecycle Control
The paper presents Proof-or-Stop Lifecycle Control, a method that ensures transitions in software development lifecycle only happen when there's verifiable evidence to support it. The method was tested through various…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The paper proposes a new method called RAPTOR for temporal knowledge graph (TKG) reasoning under the extrapolation setting. This method aims to improve the efficiency of target-oriented path exploration by using…
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…
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