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2,343 notes. Notes 1,301 to 1,350, page 27 of 47.
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers have developed Bulkhead, a framework that uses large language models and formal methods to automatically detect and fix container escape vulnerabilities caused by path traversal. This is done through…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers present Bulkhead, an automated framework that uses large language models and formal methods to detect and fix container escape vulnerabilities called path traversal (PaTra) vulnerabilities. These…
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
Researchers investigated if emotions induced in large language models (LLMs) could influence their sequential decision-making using the Iowa Gambling Task (IGT). They found that, unlike humans, induced emotion did not…
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
Researchers explored whether inducing emotions in large language models (LLMs) can influence their sequential decision-making using the Iowa Gambling Task (IGT). They found that while LLMs could sense emotions and…
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers propose E3, a method to help large language model agents estimate the complexity of tasks and reduce unnecessary work. The proposed method reduces cost, tokens, and inspected files by significant margins on…
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers have proposed a method called E3 (Estimate, Execute, Expand) to improve the efficiency of large language model (LLM) agents. E3 aims to estimate the difficulty of a task, execute a minimum viable path, and…
Dynamic Resource Allocation for Ensemble Determinization MCTS
The paper proposes enhancements for Ensemble Determinization Monte Carlo Tree Search (MCTS) algorithms, specifically focusing on dynamic resource allocation methods. These methods include Dynamic Number of…
Evidence-Grounded AI for Musculoskeletal Care
The paper introduces OrthoPilot, a clinical AI system that uses a large language model to integrate hospital data with external medical knowledge for continuous musculoskeletal management. The system outperformed 81…
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
The paper introduces EG-VAR, a tool-calling architecture that aims to make the reasoning of a large language model (LLM) more trustworthy by verifying all outputs through tool-attestation axioms and kernel checks. The…
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
Junyu Ren presents EG-VAR (Evidence-Grounded Verified Agentic Reasoning), a tool-calling architecture that aims to make empirical reasoning in large language models (LLMs) more trustworthy and auditable. The system…
Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models
In this research, Mehmet Iscan introduces a method called PoPE (Popperian Placebo-controlled Evaluation) to test the effectiveness of Frozen small code LLMs in self-repair when faced with errors. The study compares…
Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models
The study introduces a methodology called PoPE (Popperian Placebo-controlled Evaluation) to measure whether a frozen small code language model (LLM) can use evidence that falsifies its own generated code. The study…
FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation
The paper presents FormalAnalyticGeo, a framework for generating multimodal analytic geometry problems using neural-symbolic based methods. The framework leverages a formal intermediate representation called CDL to…
From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation
Researchers introduce a new framework called CARE-PPO to improve language-based quantitative prediction by both accurately estimating numerical values and providing reliable confidence signals. The method uses…
Human-AI Agent Interaction as a Neuroplastic Training Environment
The text discusses how interactions with AI agents can serve as a neuroplastic training environment due to the repetitive loop of request, response, and appraisal. It proposes a framework for using these interactions…
Human-AI Agent Interaction as a Neuroplastic Training Environment
The text discusses how interacting with AI agents can be seen as a form of neuroplastic training due to the frequent iterative loop in human-AI interaction. It suggests that these interactions may reinforce negative…
Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration
The paper presents the Internet of Agentic Things (IoAT), an architectural framework that integrates various technologies like AI, IoT, cyber-physical systems, edge computing, and digital twins to create a unified…
Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration
The paper presents the Internet of Agentic Things (IoAT), a framework that combines AI agents, IoT, cyber-physical systems, edge computing, digital twins, and more to create a unified system for closed-loop IoT…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
The paper introduces a new reinforcement learning algorithm called Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). This algorithm uses domain knowledge to improve the sample efficiency of…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
In this research paper, the authors present a novel reinforcement learning algorithm called Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). This algorithm is designed to work in Parametrized Action Markov…
Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes
The paper presents a new Reinforcement Learning algorithm named Neuro-Symbolic Knowledge- and Gradient-Guided Reinforcement Learning (KGRL). KGRL uses knowledge from a Datalog knowledge base to prune non-applicable…
Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
Researchers propose a new method called EcoSpec for optimizing the performance of Large Language Models (LLMs) using Sparse Mixture-of-Experts (MoE). This method aims to improve decoding speed by considering the cost…
Line-Anchored Feedback Cuts Token Costs and Improves Correctness in AI Code Editing
A study shows that using structured, line-anchored feedback for generative AI (GAI) code editing can reduce the number of generated tokens, improve correctness, and is especially beneficial for weaker models.
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
The study introduces Elenchos, a framework to evaluate the abductive reasoning capabilities of large language models (LLMs). The research finds that while LLMs can detect when changes have been made in a formal system…
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Researchers introduce Elenchos, a framework to evaluate abductive reasoning (inferring latent hypotheses) of Large Language Models (LLMs). The study finds that LLMs can often detect changes in formal systems but…
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
The paper introduces MemOps, a new benchmark for evaluating the memory capabilities of large language models in extended, multi-session interactions. Unlike existing benchmarks that focus on downstream question…
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
The paper introduces MemOps, a new benchmark for evaluating the lifecycle of memory operations in long-horizon conversations for LLM-based agents. Unlike existing benchmarks that focus on final answers, MemOps focuses…
Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
A new automated program repair framework called CT-Repair is presented, which addresses two limitations of existing large language models in this area. The first issue is the large and repetitive nature of raw…
Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
The paper introduces CT-Repair, an agentic Automated Program Repair (APR) framework that addresses the limitations of large language models in APR. The framework uses Code Property Graphs (CPG) and Temporal Execution…
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
The paper introduces OOD-RL-Bench, a new framework for testing out-of-distribution (OOD) detection in reinforcement learning (RL). The framework is designed to evaluate the performance of detectors against various…
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
The paper introduces PalmClaw, a native agent framework designed to run on mobile phones. It aims to overcome the limitations of existing mobile agents by directly accessing device capabilities, making each action…
PalmClaw: A Native On-Device Agent Framework for Mobile Phones
Researchers have developed a new agent framework called PalmClaw, which allows large language model agents to run natively on mobile phones. This framework aims to overcome the limitations of existing mobile agents…
Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systems
The article discusses the challenges of using AI-enabled systems with opaque functions in various domains, such as military applications. It suggests that leveraging human abilities like practical judgment, virtue, and…
Reproducible Reservoir Computing with Thermally Driven Superparamagnets: Controlling Temperature Sensitivity
The authors propose a new type of computing system using superparamagnetic nanodots, which are sensitive to temperature fluctuations due to their thermal activation dynamics. In this paper, they simulate how…
Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLMs
The authors present a method for creating counterfactual report mediators, which help align language models by making their reports invariant to certain influences (like pressure or prestige) and responsive to others…
Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels
The research proposes JADR, a protocol for measuring the internal safety mechanisms of language models before their first response is generated. It uses Jacobian space to record top-k J-space tokens from prompts and…
Solution of the Hempel's statistical ambiguity problem and Causal AI
This research provides a solution to Hempel's problem of statistical ambiguity in inductive-statistical inference, where contradictory predictions can be derived from statistical laws. The proposed solution involves…
Solution of the Hempel's statistical ambiguity problem and Causal AI
The paper presents a solution to Hempel's statistical ambiguity problem in inductive-statistical inference by introducing the concept of Maximally Specific Causal Relationships (MSCRs). The MSCRs are derived from a…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulation developed by researchers to train autonomous driving agents efficiently at scale without the need for real-world data demonstrations. It offers improved speed, realistic…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulator and training stack for reinforcement learning at scale, designed to train autonomous driving agents without human demonstrations or fallback planners. The simulator provides…
Tracing Agentic Failure from the Flow of Success
The text discusses a new model called OAT for failure attribution in LLM-based agentic systems. The model aims to identify the steps causing failures in these systems, which is crucial for debugging and improvement…
Tracing Agentic Failure from the Flow of Success
The paper proposes a new method called OAT (One-Class Attribution for Tracing) for identifying errors in language models without relying on expensive or difficult-to-collect data. This method is faster and more…
Unveiling Complex Collective Behaviors from Simple Rewards
Researchers have developed a new method called the Agent Response Map (ARM) to help understand how simple rewards can lead to complex behaviors in multi-agent reinforcement learning (MARL). The ARM reveals that robots…
Unveiling Complex Collective Behaviors from Simple Rewards
The paper proposes a method called the Agent Response Map (ARM) to analyze Multi-agent Reinforcement Learning (MARL) and understand the mechanisms behind complex behaviors that emerge from simple rewards. The ARM…
Unveiling Complex Collective Behaviors from Simple Rewards
The paper proposes a new framework and analytical tool called EEC and Agent Response Map (ARM) to understand the complex behaviors that can emerge from simple rewards in multi-agent reinforcement learning (MARL). The…
UR-VC: Unsupervised Robotic Value Correction for Time-Derived Progress Proxies
Researchers introduce an offline method called Unsupervised Robotic Value Correction (UR-VC) to correct time-derived progress labels used by modern robot learning systems. The method is based on a regularity in…
Vertical Standardisation for High-Risk AI Systems under the EU AI Act: A Domain-Specific Framework for Algorithmic Hiring
The authors propose a domain-specific framework for standardizing high-risk AI systems, particularly in recruitment, in compliance with the EU AI Act. The framework focuses on areas like risk management, data quality…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
The authors present a method for creating and refining evaluation metrics for self-improving language models (LLMs). The proposed method, called Double Ratchet, evolves metrics by combining small drawback detectors in…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
The paper presents a method for creating and improving self-evolving agent systems, which are capable of developing, modifying, and discarding their own skills. The authors claim that in many real-world applications, a…
Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation
The study examines how plan evaluators may inadvertently reward less explicit plans generated by Language Model (LLM). The research focuses on a staged expected-value scorer for LLM-produced venture routes and…
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