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2,343 notes. Notes 1,651 to 1,700, page 34 of 47.
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
The research presents a new coding system called Graph-as-Policy (GaP) for robots to work reliably in commercial and industrial applications. GaP combines interpretable robot programming with the adaptability of…
Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
Researchers have proposed a new planning algorithm called Graph Sparse Sampling (GSS). This algorithm is designed to help autonomous systems plan under uncertainty in continuous domains, which can be computationally…
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Researchers developed a machine learning model that can classify transient candidates in time-domain surveys, known as Real-Bogus classification, without needing human-labeled data. The model is trained using simulated…
LLM-as-a-Verifier: A General-Purpose Verification Framework
The paper introduces LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback to improve the accuracy of agentic tasks. The framework scales across multiple dimensions for better…
LLM-as-a-Verifier: A General-Purpose Verification Framework
Researchers introduce a new framework called LLM-as-a-Verifier to improve the ability of Large Language Models (LLMs) to verify the correctness of solutions for various tasks. The method provides fine-grained feedback…
LLM-as-a-Verifier: A General-Purpose Verification Framework
In this research, a general-purpose verification framework called LLM-as-a-Verifier is introduced. This framework provides detailed feedback for agentic tasks without requiring additional training. The framework…
LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review
A study on Large Language Models (LLMs) used to determine correct behavior in tests, identifies a gap in understanding the source of these models' authority. The study conducts a systematic literature review and finds…
Localized LoRA-MoE: Block-wise Low-Rank Experts With Adaptive Routing
In this research, the authors propose Localized LoRA-MoE, a framework that combines localized spatial blocking with dynamic, context-conditioned routing for Large Language Models (LLMs) and high-dimensional perception…
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
MetaSkill-Evolve is a two-timescale framework that allows self-improving language models to improve not only their task skills but also the improvement procedure itself. The authors claim that this recursive approach…
MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution
MetaSkill-Evolve is a framework that allows for recursive self-improvement of Large Language Model (LLM) agents. This is achieved by introducing a two-timescale system where each agent has both a task skill and a…
Multiplayer Interactive World Models with Representation Autoencoders
Researchers present a new model for multiplayer interactive worlds, specifically designed for complex environments like Rocket League with fast-paced, coupled dynamics. The model is trained on 10,000 hours of gameplay…
Multiplayer Interactive World Models with Representation Autoencoders
Researchers have developed a new multiplayer world model for complex games like Rocket League, which can generate four-player matches in real time using a latent diffusion model trained on 10,000 hours of gameplay. The…
Open Problems in AI Incident Governance
The paper discusses issues with current AI incident governance systems, specifically highlighting inconsistencies in definitions, classification, monitoring, and reporting across various frameworks.
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The researchers have developed a multi-agent framework called OptiAgent that can take a natural language description of an Operations Research problem and produce a mathematically formulated solution along with…
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
The authors propose OptiAgent, a multi-agent framework that takes a natural language description of an Operations Research problem and produces a solver-ready mathematical formulation and executable code. The…
PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
The paper introduces PDEFlow, a framework that converts user-provided ODE and PDE descriptions into neural operator pipelines. It includes a stateful input graph to validate problem specifications, a data-generation…
PDEFlow: Autonomous Agentic PDE Pipelines for Neural Operator Learning and Solver-Free Inference
The paper presents PDEFlow, a framework that converts user-level descriptions of ODEs and PDEs into neural operator pipelines for autonomous agentic simulations. It includes stages for problem specification, data…
REDDIT: Correcting Model-Generated Timestamp Drift in ASR without Forgetting via Replay-Based Distribution Editing
Researchers propose a solution, called REDDIT, to correct timestamp drift issues in automatic speech recognition (ASR) systems that generate timestamps as part of the transcription. The proposed method uses…
Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
Researchers propose a new approach for dynamic pricing in liberalized railway systems using Relational Multi-Agent Reinforcement Learning (RL). The method models the environment as a graph, process features of entities…
Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
Researchers propose an entity graph modeling approach for dynamic pricing in liberalized railway systems, using a modified multi-agent twin delayed deep deterministic policy gradient algorithm with graph-based…
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
The paper discusses a research project aimed at improving the capabilities of visual generators by addressing their limitation in fabricating what they don't know about the ever-evolving and vast visual world. The…
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
The researchers created two new datasets, SearchGen-20K and SearchGen-Bench, to evaluate the performance of current visual generators in handling a wide range of requests that require knowledge beyond what they were…
Search Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual Generation
Researchers have created two datasets called SearchGen-20K and SearchGen-Bench, containing 20,839 prompts across twelve failure categories and twenty-two domains to evaluate the performance of visual generators. They…
Selective Disclosure Watermarking for Large Language Models
Researchers have proposed a new watermarking method for large language models (LLMs) that allows selective disclosure of embedded metadata. This means that the method can control which parts of the embedded message are…
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
The text introduces a new benchmark called SovereignPA-Bench for evaluating user-owned personal agents. The benchmark focuses on aspects like preserving user sovereignty, privacy, consent, and resisting manipulative…
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
The paper introduces SovereignPA-Bench, a benchmark for evaluating user-owned personal agents in terms of preserving user sovereignty under various conditions such as evolving intent, platform mediation, privacy…
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
The text discusses a new benchmark called SovereignPA-Bench for evaluating the performance of user-owned personal agents. These agents are designed to remember preferences, filter information, use tools, and negotiate…
SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
A new benchmark called SPEARBench is introduced to evaluate the naturalness of speech-to-speech language models during question-answer interactions. The benchmark covers aspects like response latency, interruptions…
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
The paper proposes TacReasoner, a new framework that enhances the perception and representation of dynamic tactile signals for intelligent systems. It addresses two challenges: insufficient modeling of such signals and…
The Changing Role of Symbolic Methods in Artificial Intelligence
The article discusses the changing role of symbolic reasoning in artificial intelligence (AI), arguing that it is not a fundamental property of intelligence but a computational consequence of operating on simplified…
Three-Phase Evaluation of AI-Assisted Software Development Life Cycle
The paper evaluates the impact of increasing levels of AI autonomy on software development productivity, adherence to requirements, and developer cognitive workload using a web application case study. The study…
Toward Trustworthy Large Language Model Agents in Healthcare
The paper presents CareConnect, a safety-focused AI chatbot designed to automate healthcare appointment scheduling. It uses large language models, retrieval-augmented generation, and safety guardrails. The system…
Toward Trustworthy Large Language Model Agents in Healthcare
A study presents CareConnect, a safety-first conversational agent for automating healthcare appointment scheduling using large language models (LLMs), retrieval-augmented generation (RAG), and safety guardrails. The…
TREK: Distill to Explore, Reinforce to Refine
The research presents TREK (Teacher-Routed Exploration via Forward KL), a method that uses distillation for exploration support expansion, rather than imitation. This method can be used with various types of teachers…
TREK: Distill to Explore, Reinforce to Refine
TREK is a procedure that uses distillation for exploration support expansion to improve policy optimization on tasks where the current policy stalls on hard prompts. It identifies low-performing prompts, generates…
TREK: Distill to Explore, Reinforce to Refine
The paper proposes a method called TREK (Teacher-Routed Exploration via Forward KL) to improve Group Relative Policy Optimization (GRPO). TREK uses distillation for exploration support expansion and can work with…
Unified Audio Intelligence Without Regressing on Text Intelligence
The researchers introduce a unified audio-text language model called Audex, which is built on a strong text-only model. Audex allows for strong fusion of audio and text, seamless multimodal generation, and…
Unified Audio Intelligence Without Regressing on Text Intelligence
Researchers have developed a unified language model called Audex, which combines audio and text intelligence. The model is built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM (Language Model). Audex uses a…
Weak-to-Strong Generalization via Direct On-Policy Distillation
Researchers propose a method called Direct On-Policy Distillation (Direct-OPD) for improving stronger language models by leveraging the knowledge gained from reinforcement learning on smaller, weaker models. This…
When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents
Researchers have developed a new type of attack called 'stealth memory injection' that targets persistent personal agents, which combine long-term memory and access to users' external environments. The attack involves…
When Claws Remember but Do Not Tell: Stealthy Memory Injection in Persistent Personal Agents
Researchers have developed a method called MemGhost, which allows an adversary to silently inject poisoned memory into a persistent personal agent (PPA) through a single email. This can affect the future behavior of…
Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
Researchers have identified a new type of attack, called Forged Amplifying Rationale Memory Attack (FARMA), that can manipulate a large language model (LLM) agent's reasoning history by inserting forged traces using…
Your Agent's Memories Are Not Its Own: Forged Reasoning Attacks on LLM Agent Memory and Defenses
Researchers introduce a new type of attack, called Forged Amplifying Rationale Memory Attack (FARMA), that targets the reasoning history of large language model (LLM) agents. FARMA inserts forged reasoning traces using…
A$^{2}$utoLPBench: An Auto-Generated, Agent-Friendly LP Benchmark via Inverse-KKT Construction
The paper introduces A$^{2}$utoLPBench, a new linear programming (LP) benchmark for testing LLM-driven agents. The benchmark generates LP problems in plain text, and uses a known feasible point and dual to write down a…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
Researchers introduce a new contract for long-horizon language models (LLMs) called bounded contract. This contract allows each decision to be made from a fresh user message, with no raw cross-decision transcript…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
The authors introduce a new bounded contract for long-horizon Large Language Model (LLM) agents in the game Slay the Spire 2. The contract allows decisions to be made from a fresh user message, with no raw…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
The researchers introduce a new contract system for long-term learning machine (LLM) agents that limits the memory of each decision to a fresh user message, assembled by typed retrieval. They test this contract in the…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
The paper introduces a new contract for long-horizon LLM agents that restricts the amount of past information available during decision-making in the game Slay the Spire 2. The new contract allows for easier isolation…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
The paper evaluates if four large language models can accurately grade Linux/bash command responses in a way that approximates expert judgment. The authors use a four-level cognitive taxonomy to test the models on real…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
A study evaluates four large language models (GPT, Claude Opus, Gemini, and GLM) to grade Linux/bash command responses for computing education. The study uses a four-level cognitive taxonomy that assesses the…
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