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Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
Researchers propose a simple method to merge large language models with significantly different parameters without training or alignment by using dimensional adaptation and ratio-controlled interpolation. The method…
Sparse Evidence Can Suffice: Agentic Evidence Seeking for Multimodal Video Misinformation Detection
Researchers propose SIEVE, a framework for detecting video misinformation by first identifying key pieces of evidence that contribute to the decision, and then verifying the veracity based on these identified pieces…
Stress Testing Concept Erasure with Large Language Model Agents
The paper introduces a new framework called Stress Testing Agents for Concept Erasure (STACE). STACE uses multiple Large Language Model (LLM) agents to stress-test models that have had certain concepts erased from…
The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems
The Autonomous Agency Scale (AAS) is a new framework proposed to measure the self-directed behavior of AI systems. The scale evaluates seven dimensions of agency across two temporal bands: active and ambient. It…
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search
The study introduces a benchmark to analyze the Shared Discovery Paradox, a phenomenon where pooling information for one ranking leads to improved beliefs but reduced coverage in a discovery problem. The paradox arises…
WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting
The paper introduces WorldCupArena, a dynamic benchmark for evaluating language models and deep-research agents in predicting football matches. The benchmark was used for the 2026 FIFA World Cup, but can be applied to…
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
The paper 'Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI' proposes an assurance architecture called HALO. This system aims to ensure the accuracy of AI output…
AutoSynthesis: An agentic system for automated meta-analysis
AutoSynthesis is a new system designed to automate the process of meta-analysis in various fields like science, medicine, education, and policy. It can take a research question in natural language, perform tasks such…
AutoSynthesis: An agentic system for automated meta-analysis
A new system called AutoSynthesis has been developed to automate the process of meta-analysis in various fields such as science, medicine, education, and policy. The system takes a research question in natural…
AutoSynthesis: An agentic system for automated meta-analysis
AutoSynthesis is a multi-agent system designed to automate meta-analysis, making it easier and scalable for turning primary research into reliable knowledge in various fields like science, medicine, education, and…
Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Researchers compared six large language models on a standardized test for understanding scientific visualizations (SciVis). They found that the model Gemini performed best overall, but all models struggled with certain…
Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Researchers evaluate the ability of six multimodal large language models to understand scientific visualizations (SciVis) using a standardized test with 49 items based on various visualization techniques and tasks. The…
BrainPilot: Automating Brain Discovery with Agentic Research
BrainPilot is a multi-agent system designed to accelerate brain science research using AI agents grounded in curated domain knowledge, traceable logs, and auditable records for transparency. The system is fully…
Concept-Guided Spatial Regularization for World Models in Atari Pong
Researchers evaluated five world model agents in Atari Pong and found failures in visual and dynamical aspects of the generated trajectories by these agents. They propose a method called Concept-Guided Spatial…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
The authors show that fine-tuning language models on specific datasets can cause broad shifts in ideology across various topics, even when the training data is factually defensible and moderation-passing. This…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
Researchers show that fine-tuning language models on specific policies or domains can cause broad ideological shifts across various topics, even when the data used is factually defensible and moderation-passing. This…
Latent Trajectory Discrimination for AI-Generated Text Detection
The paper presents a new approach for detecting AI-generated text called Geometric Trajectory and Contrastive Learning (GTCL). Instead of treating documents as static objects, GTCL models how textual representations…
Latent Trajectory Discrimination for AI-Generated Text Detection
In this paper, the authors propose a new approach for detecting AI-generated text called Geometric Trajectory and Contrastive Learning (GTCL). Unlike existing methods that focus on aggregate statistics or global…
Long-Context Fine-Tuning with Limited VRAM
Researchers introduce Hierarchical Global Attention (HGA) to reduce memory usage during long training sequences in large language models. They use segment-wise backpropagation, tiered KV storage, and detach older data…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have created a new benchmark called MM-IssueLoc to evaluate the effectiveness of multimodal repository-level issue localization, which includes visual evidence such as screenshots and logs. Current…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
The paper introduces MM-IssueLoc, a benchmark and evaluation protocol for repository-level issue localization that includes visual evidence. The benchmark has 652 issue-PR instances across 23 languages with annotations…
Pretraining Data Can Be Poisoned through Computational Propaganda
The research shows that harmful information can be introduced into language models during their pre-training phase by manipulating web-scale discussion interfaces, such as online forums or comment sections. A new…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The authors propose a new method called RAPTOR for improving the efficiency of target-oriented path exploration in temporal knowledge graphs (TKG). The method injects a reachability-aware inductive bias to the agent…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces a new system called SearchOS, a multi-agent framework designed to improve the efficiency and quality of open-domain information seeking for AI agents. The framework addresses the issue of tracking…
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