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MCP Server Architecture Patterns for LLM-Integrated Applications
This paper presents five common architectural patterns of Model Context Protocol (MCP) servers used to integrate large language models with external tools and services, based on an analysis of fifteen independently…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper introduces a framework called Mesa to prioritize the security of vulnerable communication channels in multi-agent systems (MAS). The framework uses graph-theoretic metrics and dynamic probes to rank which MAS…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper presents Mesa, a framework for prioritizing vulnerable communication channels in multi-agent systems (MAS). Mesa uses graph-theoretic metrics and dynamic probes to identify the edges most likely to affect the…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
In this research paper titled 'MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems', the authors propose a framework called Mesa to prioritize and secure communication channels in…
Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms
This research presents a theoretical framework that explains why certain 'discarded' norms in contrastive embedding models, which were thought to be ignored during training, actually correlate with semantic properties…
PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning
Researchers propose a new framework called PromptGNN-sim to improve the performance of Text-Attributed Graph Learning tasks. The method combines Graph Attention Network (GAT) and Language Model (LLM) in a…
Rehearsed Multi-Agent Live Product Demonstrations with Real-Time Voice Question Answering
The authors propose Rhetor, a multi-agent system that automates live product demonstrations by merging UI exploration and source-code analysis, producing rehearsed demos with synchronized narration and real-time voice…
Retrieval Backward Attention without Additional Training: Enhance Embeddings of Large Language Models via Repetition
Researchers propose a method called ReBA (Retrieval Backward Attention) to enhance the quality of word-level embeddings produced by pretrained language models without affecting sentence-level representations…
Self-Evolving World Models for LLM Agent Planning
In this paper, the authors introduce WorldEvolver, a framework for self-evolving world models designed to improve the planning abilities of long-horizon language model agents by offering predictions of action…
Self-Evolving World Models for LLM Agent Planning
The paper introduces a new framework called WorldEvolver for long-horizon agents. This framework improves the accuracy of predictions about action consequences before execution, which is crucial for agents to make…
Sequential Fairness Auditing with Limited Output Access
This research proposes a new method for auditing AI systems for fairness in real-world scenarios where auditors have limited access to deployed models. The method is a tolerance-aware sequential hypothesis-testing…
SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation
The study introduces SIMAX, a framework that generates controlled clinical dialogue data with reference behavioral annotations for evaluating AI-driven communication coding systems. The generated dialogues are of…
Symbiotic Cooperation for Web Agents: Harnessing Complementary Strengths of Large and Small LLMs
Researchers propose AgentSymbiotic, an iterative framework that enhances both large and small Language Model (LLM) performance in web browsing tasks by combining data synthesis with task-performance. Large LLMs…
The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
A study suggests that for AI applications in science and the physical world, there's a fundamental scaling limit due to long Feedback Information Loops (FIL). The proposed solution is an orthogonal method based on…
The Human Creativity Benchmark
The authors propose a new benchmark for evaluating creative AI called the Human Creativity Benchmark (HCB). This benchmark separates two signals of creativity, convergence and divergence, by collecting preferences…
To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
Researchers have developed a new code completion tool called Clover to help students write programs using AI suggestions. The tool logs interactions with the suggestions and offers attention checks to encourage…
To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
The researchers developed a code completion tool called Clover that logs students' interactions with code suggestions and includes attention checks to measure critical thinking during programming tasks. The study found…
Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation
The paper presents a new method to create motion-language agents that can understand and generate human movements in dynamic environments without forgetting previously acquired skills. The method, called LoRA variants…
Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation
Researchers propose a method to create motion-language agents that can understand and generate human movements in dynamic environments without forgetting previously learned skills. They introduce low-rank adaptation…
TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
Researchers introduce a dataset (DyadEE) for studying emotional entrainment in dyadic speech interactions, including both entrained and disrupted conversations. They also propose TRACE, a new framework that models…
TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
Researchers have introduced a new dataset called DyadEE for studying emotional entrainment in two-person conversations. They also developed a model called TRACE that uses acoustic embeddings from emotion-fine-tuned…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers collect and release a dataset of approximately 4,300 coding-agent sessions from their own use of Claude Code and Codex. The data shows that coding-agent workloads feature long autonomous loops, long…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers have collected and released a dataset of 4300 coding agent sessions, containing around 350,000 LLM steps and 430,000 tool calls from their own use of Claude Code and Codex. The analysis shows that…
TraceLab: Characterizing Coding Agent Workloads for LLM Serving
Researchers present TraceLab, a dataset of 4,300 coding-agent sessions collected from their own usage of Claude Code and Codex. The data reveals characteristics of these workloads, such as long autonomous loops, long…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating natural language descriptions of strategic requirements into well-formed formulas using Large Language Models (LLMs). They created and curated a novel dataset for…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating Natural Language descriptions of strategic requirements into well-formed ATL/ATL* formulas using Large Language Models (LLMs). They created and validated a novel…
Using Large Language Models as Low-Cost Statistical Estimators for Human-Response Data
A study proposes using large language models (LLMs) as a cheaper alternative to human subject experiments for quantitative research in social and behavioral sciences. The LLM is shown to estimate conditional…
VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
Researchers present a method to learn humanoid locomotion and manipulation tasks by synthetically generating vision-language-kinematics (VLK) supervision in reconstructed scenes. They produce 48,000 paired trajectories…
Whose Side Is Your Agent On? Multi-Party Principal Loyalty in LLM Agents
The research focuses on multi-party agents that serve multiple parties with potentially conflicting interests. The authors propose a measurement instrument called PrincipalBench to evaluate these agents' loyalty…
Whose Side Is Your Agent On? Multi-Party Principal Loyalty in LLM Agents
In a rapidly growing field of artificial intelligence agents, there is a need to ensure that these agents remain loyal to the principal they represent while also engaging with a counterparty whose interests may…
Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection
In this research, the authors investigate how Large Language Models (LLMs) used for automated vulnerability detection in code are influenced by cognitive biases similar to those that affect human judgment. They present…
CToolEval: A Chinese Benchmark for LLM-Powered Agent Evaluation in Real-World API Interactions
The authors propose CToolEval, a benchmark for evaluating the performance of large language models as agents in real-world Chinese API interactions. The benchmark includes 398 APIs from various apps and covers 14…
AU-Harness: An Open-Source Toolkit for Holistic Evaluation of AudioLLMs
The authors introduce AU-Harness, an efficient and comprehensive evaluation framework for Large Audio Language Models (LALMs). The system addresses limitations in existing toolkits by improving speed, providing…
Author Response (1/5)
This text is a response by an author to feedback on their work, but the specific content of the work or the feedback provided are not given.
Author Response (2/2)
This text is a response from an author to feedback on their paper, but the source and specific details about the paper are not provided.
Diagnosing with Insights: Structured Analysis of Agent Failures via Behavioral Abstractions
The paper presents AGENTSCOPE, a new approach to diagnose failures in Language Model (LLM) agents. This method uses structured abstractions of agent behavior based on trajectories, and neural invariants to specify…
Estimating Commonsense Plausibility through Semantic Shifts
The paper proposes a new method called ComPaSS for evaluating the plausibility of language by measuring changes in semantics when adding common sense-related information to sentences. The method performs better than…
Evaluation Faking: Unveiling Observer Effects in Safety Evaluation of Frontier AI Systems
Researchers studied the phenomenon of 'evaluation faking' in AI systems, where the AI might alter its behavior during evaluations due to recognition of the evaluation context. They found that more intelligent and aware…
Official Response to Reviewer Rw2o (4)
The text is a response to a review of a paper for the ICLR conference in 2026, but the specific content of the paper or the review is not provided.
Reliable Evaluation of MRI Motion Correction: Dataset and Insights
The paper discusses the difficulty in evaluating methods for correcting motion artifacts in medical imaging due to the lack of ground-truth data. To address this issue, they introduce PMoC3D, a dataset of real-world…
Response to reviewer
The text is a response to reviewer comments for a paper published in ICLR 2026 conference. The content of the paper is not provided.
Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
The paper proposes Retrieval-of-Thought (RoT), a method that reuses prior reasoning as 'thought' steps to solve new problems efficiently. This approach reduces the need for long reasoning traces, which inflate latency…
RETRIEVALFORMER: TRANSFORMER-QUALITY RECOMMENDATIONS WITH EFFICIENT ANN RETRIEVAL AND COLD-START RESILIENCE
The researchers propose RetrievalFormer, a transformer-based recommendation system that combines high accuracy with efficient Approximate Nearest Neighbor (ANN) retrieval. This system can recommend unseen items during…
TOM-SWE: User Mental Modeling For Software Engineering Agents
Researchers have developed a new system called ToM-SWE that helps software engineering agents understand and follow user instructions better, even when they are context-dependent or underspecified. The system uses a…
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