All notes, newest first
2,343 notes. Notes 1,901 to 1,950, page 39 of 47.
MVP-Nav: Multi-layer Value Map Planner Navigator
MVP-Nav is a new framework proposed for RGB-only navigation, which addresses the challenges posed by zero-shot object goal navigation. It uses 3D foundation models to create a global spatial semantic representation and…
MVP-Nav: Multi-layer Value Map Planner Navigator
The paper presents a new navigation framework called MVP-Nav which addresses the challenge of zero-shot object goal navigation using only RGB perception. The framework aligns perception, planning, and control with the…
PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
PolicyGuard is a framework that uses neuro-symbolic technology to help organizations ensure documents comply with their specific policies, guidelines, or playbooks. It separates policy formalization, local document…
PolicyGuard: From Organizational Policies to Neuro-SymbolicCompliance Review Engines
The researchers have developed a framework called PolicyGuard to ensure documents comply with an organization's specific policies or guidelines. Instead of relying on large language models for end-to-end prompting…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
Researchers introduce QVal, a tool that evaluates dense supervision signals for long-horizon Learning with Long Memory (LLM) agents without the need for training. This allows for a fair comparison between different…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
A research paper introduces QVal, a tool for evaluating dense supervision signals in long-horizon tasks for LLM agents without the need for training. The tool measures how well a method's score aligns with Q-values of…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
The paper introduces QVal, a training-free testbed for evaluating dense supervision signals in long-horizon reinforcement learning tasks. QVal measures the alignment of a method's score with the Q-values of a strong…
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
The paper proposes a framework called Robust Adversary Instance Search (RAISE), which uses Large Language Models (LLMs) to design heuristics that perform well under real-world distributional shifts. The framework…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Researchers have developed two new mechanisms - Reinforcement Learning with Metacognitive Feedback (RLMF) and Metacognitive Data Selection - to improve the performance of Language Models (LLMs). The goal is to make…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
The paper discusses a new approach called Reinforcement Learning with Metacognitive Feedback (RLMF) to improve Language Model's (LLM) metacognitive abilities. The RLMF aims to help LLMs better monitor and regulate…
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
This research paper discusses the need for better understanding and transparency in Artificial Intelligence (AI) used in healthcare, focusing on the explanatory process of Machine Learning models. It highlights that…
Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA
The authors investigate the generation of question-answer (QA) pairs used for training language models and find that this process is not neutral as it can introduce biases due to the selection of evidence and the…
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
The paper proposes ShopX, a foundation model aimed at bridging the gap between language understanding and item-space fulfillment in AI shopping applications. It unifies intent understanding, execution planning, and…
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
The paper proposes a foundation model named ShopX to enhance AI shopping applications by bridging the gap between language understanding and item-space fulfillment. The model allows LLMs (large language models) to…
STEB: Style Text Embedding Benchmark
The authors introduce a new comprehensive benchmark called Style Text Embedding Benchmark (STEB), designed to evaluate style embeddings, which have been previously assessed in a fragmented manner. The STEB includes 96…
Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents
Researchers present LuckyStar 111B, a hybrid reasoning model developed by Cohere and LG CNS for enterprise agents that can switch between concise non-reasoning behavior and longer tool-oriented reasoning in Korean and…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers propose a multi-agent system (MAS) called TreeAgent to automate bias labeling in forestry via expert decision trees and Vision-Language Models (VLMs). The MAS aims to reduce the need for human annotation…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers have proposed a multi-agent system that uses expert decision trees and Vision-Language Models (VLMs) to automate the bias classification in tree height in forestry remote sensing. The system mitigates VLM…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers propose a system called TreeAgent that uses artificial intelligence (AI) to classify tree heights in forestry remote sensing. The system combines decision trees created by experts with Vision-Language…
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
The paper proposes a new framework called TRIAGE for credit assignment in agentic reinforcement learning. This framework adds a semantic role axis to outcome credit, aiming to correct the issues of standard GRPO where…
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
The paper proposes a new framework called TRIAGE for credit assignment in agentic reinforcement learning. This framework adds a semantic role axis to outcome credit, allowing for more accurate reward distribution for…
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
The research evaluates the issue of data referencing errors (DREs) made by large language models (LLMs) while performing table tasks. The study presents a systematic evaluation of these errors across different models…
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Researchers propose a new multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The system can classify malware families, detect packed versus unpacked malware, and identify malware…
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
The paper proposes AgentsNet, a new benchmark for multi-agent reasoning. This benchmark is designed to measure the ability of multi-agent systems to collaborate effectively and leverage their network topology. The…
Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
The paper 'Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents' presents a survey of always-on agents, which are systems that store information from past interactions to influence future…
Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents
The paper 'Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents' discusses the concept of always-on agents, which are systems that have a durable state accumulated from past interactions…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
BayesEvolve is a new framework for autonomous scientific discovery. It uses large language models (LLMs) to maintain explicit, uncertainty-aware beliefs about the quality of hypotheses, and it uses these beliefs to…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
The paper introduces a new framework called BayesEvolve for autonomous scientific discovery. This framework aims to improve the efficiency of experimentation by maintaining explicit, uncertainty-aware beliefs about…
C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
The paper introduces C$^2$R, a method to improve Sparse Autoencoders (SAEs) used for interpreting large language models. The main challenge with SAEs is scaling to large dictionaries, which leads to issues like feature…
Can LLMs Rank? A Tale of Triads and Triage
The authors discuss the use of Large Language Models (LLMs) in ranking people for scarce resources, such as housing or emergency services. They present two methods for assessing the consistency of LLM's judgments: one…
Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration
The paper presents a new infrastructure called Clarus designed to coordinate autonomous research agents for web-scale scientific collaboration. The goal is to shift from individual or closed workflow-based research…
COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies
The COHORT framework is an automated solution that aims to mitigate cyber attacks in enterprise networks more efficiently. It uses a multi-agent system and a high-fidelity GNS3 emulator to generate, implement, and…
COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies
COHORT is an automated end-to-end framework designed to mitigate attacks in enterprise networks, significantly reducing the time and expertise required for this process. It uses a multi-agent workflow, high-fidelity…
Collective cooperation without individual fidelity in LLM agents
Researchers compared the behavior of Large Language Models (LLMs) to humans in a networked Prisoner's Dilemma experiment. While LLMs could replicate some collective cooperation patterns, they failed to match human…
Collective cooperation without individual fidelity in LLM agents
Researchers tested large language models (LLMs) in a Prisoner's Dilemma experiment with human participants to evaluate if their behavior can be seen as a faithful representation of human decision-making. The LLM was…
DOPD: Dual On-policy Distillation
The paper proposes a new method called Dual On-policy Distillation (DOPD) for transferring knowledge from a teacher model to a student model in on-policy distillation. DOPD aims to address the issue of 'privilege…
Entity Binding Failures in Tool-Augmented Agents
This research paper focuses on a problem in tool-augmented language model agents called 'entity binding failures'. These failures occur when an agent performs actions incorrectly even though it has chosen the right…
Entity Binding Failures in Tool-Augmented Agents
The paper discusses a problem called 'entity binding failures' in AI agents that are augmented with tools. These failures occur when an agent incorrectly identifies or interacts with the wrong entity despite selecting…
EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures
This paper presents EvalSafetyGap, a hybrid approach combining survey and conceptual framework for evaluating the safety of Large Language Models (LLMs). The authors aim to address the shared challenge in LLM…
Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
Researchers propose a method to improve the quality of data retrieval from structured metadata catalogs by introducing permutation-invariant fine-tuning (PI-FT). This technique helps in maintaining the same retrieval…
Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
Researchers propose a method called permutation-invariant fine-tuning (PI-FT) to improve structured metadata retrieval. They show that the choice of field order in a record can significantly impact retrieval quality…
GROW$^2$: Grounding Which and Where for Robot Tool Use
Researchers introduce GROW$^2$, a system that helps robots use tools creatively by grounding which object to use as a tool and where to apply it, even when the object is not intended for that purpose. This system uses…
Latent Actions from Factorized Transition Effects under Agent Ambiguity
The study introduces Observed Transition Factorization (OTF) to help Latent Action Models (LAMs) understand actions in multi-object or distractor-rich scenes, where the source of action can be unclear due to mixed…
Latent Actions from Factorized Transition Effects under Agent Ambiguity
The research introduces Observed Transition Factorization (OTF), a method to decompose transitions in multi-object or distracting scenes into reusable transition primitives. These primitives are used to form…
Latent Actions from Factorized Transition Effects under Agent Ambiguity
The research introduces a method called Observed Transition Factorization (OTF) to improve Latent Action Models (LAMs). LAMs struggle to understand actions in multi-object or cluttered scenes due to mixed motion…
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
The paper presents LeVo 2, a hybrid framework for generating full-length songs that balance coherence, musicality, and detailed acoustics. It uses a language model for semantic planning and a diffusion-based Music…
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
A new routing architecture called ANTAP (Automatic Non-Textual Agent Picker) was introduced in this research. It aims to address security vulnerabilities in multi-agent systems by discarding indirect proxies and…
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
Researchers present ManimAgent, a self-evolving multimodal agent that uses a dual-channel Episodic Memory Bank to carry reflection experience across tasks in generating Python code using the Manim library for…
ManimAgent: Self-Evolving Multimodal Agents for Visual Education
The research presents ManimAgent, a self-evolving multimodal agent that learns from its failures and successes in generating Python code using the Manim library to render mathematical animations. The agent stores these…
MCP Server Architecture Patterns for LLM-Integrated Applications
This industry experience paper presents five architectural patterns observed in the use of Model Context Protocol (MCP) servers, which connect large language models to external tools, data sources, and services. The…
Page 39 of 47
