All notes, newest first
2,343 notes. Notes 2,101 to 2,150, page 43 of 47.
In-Context Model Predictive Generation: Open-Vocabulary Motion Synthesis from Language Models to Physics
Researchers propose a new method called In-Context Model Predictive Generation (ICMPG) for generating realistic human motions from textual descriptions. The method combines language-model planning with physical…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study reveals that Language Models (LLMs) fine-tuned for security classification can overlook vulnerabilities introduced during fine-tuning itself. These models may learn specific token-level indicators which…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study shows that Language Models (LLMs) fine-tuned for security classification can have vulnerabilities missed during standard evaluation because they learn token-level indicator semantics that work well on their…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
The study shows that Language Models (LLMs) fine-tuned for security classification may overlook vulnerabilities introduced during fine-tuning itself. These models can learn token-level indicator semantics that preserve…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
A study shows that Language Models (LLMs) fine-tuned for security classification can miss vulnerabilities due to learning token-level indicator semantics that preserve accuracy on their training data but fail under…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
The paper introduces a new method called Joint Learning of Experiential Rules and Policies (JERP) for large language model agents operating in multi-step interactive environments. The main challenge addressed is…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
Researchers present a method called Joint Learning of Experiential Rules and Policies for Large Language Model Agents (JERP). This method aims to help LLM agents, which operate in multi-step interactive environments…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
A research paper proposes a method called Joint Learning of Experiential Rules and Policies (JERP) to improve the effectiveness of Large Language Model Agents in multi-step interactive environments. The method updates…
Language-Based Digital Twins for Elderly Cognitive Assistance
The paper proposes a language-based digital twin framework that uses large language models to mimic the conversational behavior of elderly individuals, helping in early detection of Mild Cognitive Impairment (MCI). A…
Language-Based Digital Twins for Elderly Cognitive Assistance
Researchers propose using large language models (LLMs) to create digital twins that mimic the conversational behavior of elderly individuals, potentially aiding in early detection of Mild Cognitive Impairment (MCI)…
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
The text describes a prize-winning system for bimanual garment folding from the LeHome Challenge 2026. The system used a reinforcement learning loop to improve a vision-language-action (VLA) policy, placing first in…
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
A research paper describes a system that won first place online and second place offline in the LeHome Challenge 2026, a competition on bimanual garment folding. The system improves a vision-language-action (VLA)…
Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
The researchers propose a method called Look-Before-Move for camera planning in dynamic 3D story worlds. The aim is to enable cameras to decide what to observe and how to move, based on narrative intent and physical…
Look-Before-Move: Narrative-Grounded World Visual Attention in Dynamic 3D Story Worlds
Researchers have developed a new camera planning framework called Look-Before-Move for embodied AI operating in dynamic 3D environments. The goal is to enable the camera to actively decide what to observe, not just…
NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models
Researchers have created a new benchmark called NuclearQAv2 to evaluate the performance of large language models (LLMs) in the domain of nuclear engineering. This benchmark consists of about 1,240 question-answer pairs…
NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models
Researchers have created a new benchmark called NuclearQAv2 to evaluate the performance of large language models (LLMs) in the domain of nuclear engineering. The benchmark consists of approximately 1,240…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) of Large Language Models (LLMs). The new dataset, OpenRCA 2.0, includes 500 instances and is the first cross-system RCA benchmark with…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Researchers introduce a new method, called PAVE, to support the rigorous evaluation of large language models (LLMs) in root cause analysis (RCA). The current datasets only label the root cause and not the path…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) that includes step-wise causal annotations for large language models (LLMs). The benchmark aims to test the capabilities of LLMs in…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) of large language models (LLMs). The benchmark includes step-wise causal annotations for LLMs and addresses the issue that existing…
Parametric Open Source Games
The authors introduce a new concept called parametric open-source games, which are continuous versions of game equilibria where players choose parameter vectors to determine their actions in an underlying finite game…
Parametric Open Source Games
The paper introduces 'parametric open-source games', a continuous version of program equilibria in which players choose parameter vectors for their behavior. The authors establish equilibrium existence results, derive…
Parametric Open Source Games
The authors introduce a new concept called parametric open-source games, which are a continuous version of program equilibria in game theory. In these games, players choose parameter vectors that determine their…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
The study investigates prompt injection, a strategy used by job applicants to manipulate automated resume screening systems using large language models (LLMs). The research shows that prompt injection is effective when…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
The research investigates the impact of 'prompt injection' on automated resume screening using large language models (LLMs). Prompt injection refers to strategic manipulation in text to influence LLM evaluations. The…
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
The study explores the impact of prompt injection, a self-promotional text strategy used by job applicants to manipulate automated hiring systems, on Large Language Models (LLMs) in résumé screening. The research shows…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
The paper introduces a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the extraction of unseen relations between entities in text for real-world applications. Current…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
The paper proposes a new method called Reasoning-guided progressive Open Relation Extraction (ReaORE) to improve the performance of Open Relation Extraction (OpenRE). This method addresses the limitations of current…
Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks
Researchers propose a method for improving the safety of autoregressive image generation models, which generate images by sequentially predicting discretized visual tokens derived from a codebook. The proposed method…
Safe Autoregressive Image Generation with Iterative Self-Improving Codebooks
The authors propose a method for safe image generation using autoregressive unified multimodal models. They introduce Iterative Self-Improving Codebooks to eliminate harmful mappings in the codebook that could generate…
Semantic Early-Stopping for Iterative LLM Agent Loops
The research proposes a method for stopping large language model loops (like drafting and revising) early, based on when consecutive drafts no longer change in meaning and the answer's quality stops improving. This…
Semantic Early-Stopping for Iterative LLM Agent Loops
The research presents a method called semantic early-stopping for multi-agent large language model (LLM) loops, such as a Writer and a Critic. The goal is to halt the loop when the consecutive draft embeddings no…
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
The paper introduces ShareLock, a stealthy multi-tool threshold poisoning framework designed to exploit the interaction between Large Language Models (LLMs) and external tools through the Model Context Protocol (MCP)…
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
Researchers introduce a new method called ShareLock, a multi-tool threshold poisoning framework that uses Shamir's threshold scheme to inject malicious instructions into LLM-servers through MCP. The method disperses…
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
The paper introduces a new multi-tool threshold poisoning framework called ShareLock. This framework uses Shamir's threshold scheme to distribute malicious instructions across multiple tool descriptions, making it hard…
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
The paper introduces a new method called ShareLock, which is a multi-tool threshold poisoning framework that can exploit the interactions between Language Model servers and external tools. This method is designed to…
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
Researchers compare Simulation-Based Inference (SBI) with Markov chain Monte Carlo (MCMC) methods for Bayesian calibration of a mechanistic epidemiological model using COVID-19 ICU occupancy data from Germany in 2020…
Simulation-based inference for rapid Bayesian parameter estimation in epidemiological models: a comparison with MCMC
A study compares two methods for estimating parameters in epidemiological models: Markov chain Monte Carlo (MCMC) and Simulation-based Inference (SBI). The authors use SBI with neural posterior estimation to analyze…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers studied how the representation of a state given to a reinforcement learning (RL) agent impacts energy trading decisions. They used a fixed Double DQN agent in a pumped-storage arbitrage environment and…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers study the impact of state representation on a reinforcement learning agent in energy trading decisions, focusing on a pumped-storage arbitrage environment. They find that combining absolute price…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers studied the impact of state representation in deep reinforcement learning (DRL) for energy trading decisions, using a fixed Double DQN agent in the HydroDam environment. They found that combining absolute…
State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading
Researchers compared different methods for representing market conditions in reinforcement learning (RL) models for energy trading. They found that combining multiple types of features led to stronger performance…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The Spec Growth Engine is a proposed framework to address two challenges in AI coding agents: context explosion and silent spec-code drift. It does this by creating a machine-readable spec graph, scoping agent context…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper presents the Spec Growth Engine, a framework to address the structural failure modes introduced by AI coding agents in software development. These failure modes include context explosion and silent spec-code…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper proposes a new framework called the Spec Growth Engine to improve AI coding agents during software development. The framework addresses two main issues: context explosion and silent spec-code drift. It uses a…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The Spec Growth Engine is a new framework that aims to address two issues with AI coding agents: context explosion and silent spec-code drift. It uses a machine-readable spec graph, a Spine context assembler…
Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text
The paper presents GAversary, a new method for generating adversarial attacks on natural language models using a Genetic Algorithm (GA). This method is able to reduce the accuracy of several target models significantly…
Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text
Researchers have developed a new method called GAversary, which uses a Genetic Algorithm (GA) and GloVe embeddings to generate adversarial attacks on natural language models. These attacks can significantly reduce the…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The study examines the use of multiple language models (LLMs) to improve accuracy over single models and finds that their performance is limited by a factor called 'beta'. Beta represents the rate at which all models…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The study discusses multi-model language models and how their accuracy is limited by a factor called 'beta', which is the rate at which all models are wrong on the same query. The authors find that across 67 models, a…
Page 43 of 47
