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Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
Researchers propose a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for training large language models without human annotations in specialized domains where they are costly to obtain. This method…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
Researchers have developed a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for training large language models without the need for real-world interaction feedback or human-labeled supervision…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
In this paper, the authors propose a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for annotation-free self-distillation of large language models. This method uses internal neuron activations to…
Online Safety Monitoring for LLMs
Researchers propose a simple online safety monitoring system for Large Language Models (LLMs). The system uses a verifier signal from an external model to decide when safety can no longer be assumed, by setting a…
Online Safety Monitoring for LLMs
The researchers propose a straightforward method for monitoring Large Language Models (LLMs) in real-time to ensure safety during deployment. This method involves setting a threshold for a verifier signal from an…
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
The paper provides an overview of the methods used for assessing and managing risks associated with intelligent systems, in response to the need for safe and reliable AI systems as demanded by emerging regulatory…
Overview of Risk Assessment and Management for Intelligent Systems under the AI Act and Beyond
This paper provides an overview of methods for assessing and managing risks associated with intelligent systems, as required by emerging regulatory frameworks. It reviews various types of AI-related risks, existing…
Program-as-Weights: A Programming Paradigm for Fuzzy Functions
The authors propose a new programming paradigm called fuzzy-function programming, where functions can be compiled from natural language specifications into compact, locally executable neural networks. They introduce…
Program-as-Weights: A Programming Paradigm for Fuzzy Functions
The authors propose a new programming paradigm called fuzzy-function programming, which compiles natural language specifications into compact, locally executable neural artifacts using a 4B compiler trained on a large…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes QFedAgent, a new approach for multi-agent activity recognition in federated learning. It uses a hybrid quantum-classical framework and a variational quantum circuit fusion module that models…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes QFedAgent, a hybrid quantum-classical personalized Federated Learning framework for multi-agent activity recognition. It introduces a variational quantum circuit fusion module that models…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes a new method called QFedAgent for multi-agent activity recognition in privacy-sensitive robotic sensing applications. The method uses a hybrid quantum-classical personalized Federated Learning…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study examined the impact of additional capabilities on agentic coding assistants in terms of software quality and reliability. The results showed that while high-tier models performed well, a low-cost local model…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study comparing different agentic coding assistants found that the level of reasoning effort, rather than the available tools, significantly impacts the reliability and first-try success rate of software generation…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study found that the level of reasoning effort in agentic code generation, rather than the availability of additional capabilities like testing tools and design prompts, significantly affects the reliability of…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study tested the assumption that giving agentic coding assistants more capabilities leads to better software. The study found that investing in reasoning effort yielded better first-try reliability and fewer…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study compared different agentic coding assistants and their abilities in building a real-time retrospective board. The results showed that the level of reasoning effort, rather than access to additional tools or…
Reasoning effort, not tool access, buys first-try reliability in agentic code generation: an observational study
A study found that more reasoning effort in agentic code generation (AI programs assisting human coders) leads to higher reliability and fewer errors, rather than the use of additional tools or capabilities.
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Researchers introduce DramaSR-532K, a large-scale benchmark for speaker recognition in long-form TV dramas. They also propose DramaSR-LRM, a robust approach that uses a large reasoning model to improve speaker…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Researchers present two contributions aimed at improving speaker recognition in long-form TV dramas. They create a large-scale benchmark called DramaSR-532K with over 900 characters and propose using a large reasoning…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
The paper introduces a new benchmark, DramaSR-532K, for speaker recognition in long-form TV dramas. They also propose a new approach called DramaSR-LRM which uses a large reasoning model (LRM) to aggregate contextual…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
The researchers introduce a large-scale benchmark called DramaSR-532K for speaker recognition in long-form TV dramas and propose a new method, DramaSR-LRM, built on a large reasoning model (LRM). This method aggregates…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers have proposed a new method called Recursive Evidence Replay (RECONTEXT) to improve the ability of large language models to understand and reason over long contexts. This method doesn't require training…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose a new method called Recursive Evidence Replay (RECONTEXT) to improve the ability of large language models (LLMs) to understand and reason over long contexts. This method doesn't require training and…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose a new method called Recursive Evidence Replay (RECONTEXT) to improve long-context reasoning in large language models without requiring additional training, memory, or context pruning. RECONTEXT…
SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces
The paper proposes SkillFuzz, a method to detect potential unintended objectives (implicit intents) in Large Language Model (LLM)-based agents that use reusable skills from open skill marketplaces. SkillFuzz identifies…
SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces
The paper presents SkillFuzz, a method for discovering unintended objectives (implicit intents) in Large Language Model (LLM)-based agents that use reusable skills from open skill marketplaces. SkillFuzz works by…
SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces
The paper presents SkillFuzz, an execution-free testing approach that helps detect unintended objectives (implicit intents) in large language model (LLM)-based agents operating on open skill marketplaces. SkillFuzz…
Steerability via constraints: a substrate for scalable oversight of coding agents
The paper suggests a method to manage coding agents, which can handle complex tasks but require human oversight due to security risks and scalability issues. The author proposes using the same methods used for managing…
Steerability via constraints: a substrate for scalable oversight of coding agents
The paper proposes a system for managing coding agents that replicates methods used for decades in managing large human engineering teams, such as access control and strict coding conventions enforced by tooling. The…
Steerability via constraints: a substrate for scalable oversight of coding agents
The author proposes a method for improving the scalability of oversight on coding agents by implementing constraints such as access control, network policies, and strict coding conventions enforced by tooling. This…
Steerability via constraints: a substrate for scalable oversight of coding agents
The author proposes a method for managing coding agents that mimics traditional methods used in large engineering teams, such as access control and strict coding conventions enforced by tools. They present a controlled…
TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
Researchers have developed TestEvo-Bench, a benchmark for testing software that evolves alongside code changes. The benchmark includes tasks mined from 152 open-source Java projects, with two tracks: test generation…
TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
The paper introduces TestEvo-Bench, a new benchmark for testing software that evolves with its code. This benchmark is designed to evaluate how well a test automation agent understands the impact of a code change on…
TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
Researchers have developed TestEvo-Bench, a live benchmark for testing software tests and code co-evolution. The benchmark includes two tracks: test generation and test update, where an agent should either write new…
TestEvo-Bench: An Executable and Live Benchmark for Test and Code Co-Evolution
The paper introduces TestEvo-Bench, a new benchmark for evaluating test automation agents that can generate or update tests in response to code changes. The benchmark includes 746 test generation and 509 test update…
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Researchers propose a new framework, SPG-Layout, to generate physically plausible indoor scenes in complex non-Manhattan environments. The framework uses statistical priors of object distributions and a hierarchical…
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Researchers have developed a new method called SPG-Layout for creating realistic indoor scenes in complex, non-rectangular (non-Manhattan) environments using large language models. This method improves upon existing…
The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry
A study analyzes the geometry of Large Language Model (LLM) personas using psychometric questionnaires and SPD manifolds. The research finds that LLM personas have two components: aggregated features (Big Five scores)…
The Dual Nature of LLM Persona: Aggregated Tendencies and Frame-Dependent Geometry
In this study, the authors analyze the structure of Large Language Model (LLM) personas by examining how they respond to psychometric questionnaires. They find that the geometry of these responses is not inherent but…
The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits
The paper introduces Eticas AI Risk Taxonomy v2.0.0, an open-source framework for operationalizing AI audits. It bridges the gap between identifying AI risks and executing audits by providing a method to measure and…
The Eticas AI Risk Taxonomy: Open Infrastructure for Operationalizing AI Audits
The Eticas AI Risk Taxonomy v2.0.0 is an open-source framework for operationalizing AI audits. It provides a standardized method for evaluating AI systems across various high-stakes domains, focusing on turning risks…
UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development
The researchers proposed UA-ChatDev, an uncertainty-aware multi-agent software development framework to improve the reliability of autonomous multi-agent software development. The framework uses a lightweight…
UA-ChatDev: Uncertainty-Aware Multi-Agent Collaboration for Reliable Software Development
The research paper proposes UA-ChatDev, a multi-agent software development framework that is designed to mitigate issues caused by the propagation of incorrect decisions during software development. This is achieved by…
Understanding Agent-Based Patching of Compiler Missed Optimizations
The paper presents a study on the effectiveness of AI agents in addressing missed optimizations in compilers. The authors found that while coding agents can often optimize given examples, the generated patches…
Understanding Agent-Based Patching of Compiler Missed Optimizations
The paper 'Understanding Agent-Based Patching of Compiler Missed Optimizations' presents a study on how well coding agents can fix missed optimizations in compilers, cases where the compiler failed to optimize certain…
Understanding Agent-Based Patching of Compiler Missed Optimizations
The paper 'Understanding Agent-Based Patching of Compiler Missed Optimizations' studies how well artificial intelligence (AI) agents can patch missed optimizations in compilers, where the compiler failed to optimize…
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Researchers explore how the social structure in AI models affects what an agent says publicly versus privately. In a debate setting, they found that alignment-inducing settings lead to significant public-private…
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
Researchers studied how language models (LLMs) behave in socially structured settings, where role, audience, and relationships can influence what the LLM says publicly versus privately. They found that when there's an…
What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates
The study investigates how the social structure in multi-agent debates influences what LLM agents say publicly versus privately, without any explicit objective in the prompt. The researchers found that…
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