GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
arXiv:2606.28514v1 Announce Type: new
Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. Existing benchmarks show that these models possess many of the required component capabilities, but the conditions that coincide in coll...
IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
arXiv:2606.28556v1 Announce Type: new
Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging. However, existing medical AI benchmarks are fragme...
Search for Truth from Reasoning: A Dynamic Representation Editing Framework for Steering LLM Trajectories
arXiv:2606.28589v1 Announce Type: new
Abstract: Current approaches to enhance Large Language Model (LLM) reasoning, such as Chain-of-Thought and "Wait" prompts, primarily encourage models to think more, yet often fail to guide them toward Truth. While Representation Editing (RepE) offers a intrinsi...
Millions of exploding stars could soon reveal dark energy's secrets
A new AI-powered framework could transform how astronomers measure the expansion of the Universe. By analyzing images of Type Ia supernovae and modeling their environments in unprecedented detail, researchers can estimate cosmic distances with near-spectroscopic accuracy. The technique is designed f...
Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity
AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory system separating what’s stored from how it's retrieved.
The post Memora: A Harmonic Memory Represe...
arXiv:2606.27385v1 Announce Type: new
Abstract: The most widely used RANSAC variants score candidate models by counting inliers or summing per-point scores that saturate beyond a residual threshold. Every such score requires a user-supplied parameter that is a function of the inlier scale, which mu...
Unified Zero-Shot Time Series Forecasting: A Darts Foundation
arXiv:2606.27438v1 Announce Type: new
Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models have recently claimed accuracy improvements in zero-shot forecasting, promising a paradigm shift from ...
PairSAE: Mechanistic Interpretability from Pair Representations in Protein Co-Folding
arXiv:2606.27440v1 Announce Type: new
Abstract: Foundation models for structural biology have achieved remarkable performance in predicting biomolecular structure and show promise for the design of proteins and small molecules. Yet understanding which internal features drive their outputs remains c...
AI-Model Network: Concept, Current State and Future
arXiv:2606.27382v1 Announce Type: new
Abstract: While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Computers create the Internet, and the Internet empowers the value of computers. The rapid development o...
When Does Personality Composition Matter for Multi-Agent LLM Teams?
arXiv:2606.27443v1 Announce Type: new
Abstract: Personality prompting shapes how large language models communicate, yet whether these behavioral shifts affect objective task outcomes remains under-explored. Prior work shows that agents prompted with low agreeableness produce adversarial language, w...
Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
arXiv:2606.27483v1 Announce Type: new
Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks. Unlike humans who employ "what-if" reasoning to evaluate potential plans before commitm...
Odyssey: Constructing Verifiable Local Truth-Preserving Foundation Models
arXiv:2606.27593v1 Announce Type: new
Abstract: We introduce a categorical framework called ODYSSEY for constructing verifiable, local truth-preserving foundation models as compositions of foundries: building-block architectural components that specify a cover of local contexts, local representatio...
DysLexLens: A Low-Resource LLM Framework for Analysing Dyslexic Learners Insights from Online Forums
arXiv:2606.27619v1 Announce Type: new
Abstract: Dyslexic learners increasingly use artificial intelligence (AI) tools to support reading, writing, organisation, and study-related tasks. However, their lived experiences with these tools remain largely underexamined. This paper proposes DysLexLens, a...
30 startups rebuilding enterprise software with AI agents
In Q1 2026, AI companies pulled in $242 billion in venture capital. That is 80% of all global VC funding for the quarter. From coding to compliance, customer service to clinical documentation, these 30 companies are not updating enterprise software. They are rebuilding it from scratch.
Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration
arXiv:2606.26168v1 Announce Type: new
Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run--tumble process driven by stimulus--response rules. However, such descriptions overlook how organis...
KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction
arXiv:2606.26179v1 Announce Type: new
Abstract: While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pathways. We present KG-TRACE, a novel neuro-symbolic framework that integrates the WHO mutation knowled...
Detecting and Controlling Sycophancy with Cascading Linear Features
arXiv:2606.26155v1 Announce Type: new
Abstract: Interpreting and controlling model behaviors through activation steering methods requires many pairs of contrastive samples that clearly exhibit desired or undesired behavior. These data pairs determine the degree to which interpretability frameworks ...
Refusal Lives Downstream of Persona in Chat Models
arXiv:2606.26161v1 Announce Type: new
Abstract: Linear directions in activation space have been identified for both refusal and persona traits in instruction-tuned chat models, but the two have been studied as separate mechanisms. We show they interact: a compliant persona gates refusal. In Qwen2.5...
AlgoEvolve: LLM-driven Meta-evolution of Algorithmic Trading Programs
arXiv:2606.26173v1 Announce Type: new
Abstract: Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current applications focus on static coding benchmarks. We extend this paradigm to algorithmic tradi...
Agentic Analysis for Agentic Infrastructure: An LLM-Powered Pipeline for Comparative Governance of DAO and Corporate AI Protocols
arXiv:2606.26203v1 Announce Type: new
Abstract: As AI agent protocols proliferate, the governance structures shaping their interoperability standards remain empirically underexamined. We introduce an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automat...
Understanding the brain with AI-driven explanations and experiments
Researchers introduce generative causal testing, which translates black box models into clear hypotheses and verifies them in the scanner, revealing what specific brain regions respond to in language.
The post Understanding the brain with AI-driven explanations and experiments appeared first on Micr...
Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Models
arXiv:2606.24898v1 Announce Type: new
Abstract: Looped language models turn hidden states into runtime state: each state is decoded for prediction and fed back into future computation. This creates a basic supervision question: which state variables does cross-entropy actually control? We show that...
From Meta Idea to Advanced Mathematical Discovery -- Human-AI Co-Discovery of Sign-Embedding Quantum Algorithms
arXiv:2606.24899v1 Announce Type: new
Abstract: AI-assisted mathematics is often evaluated on solving predefined problems. In practice, however, many important advances begin earlier, when a vague research intuition is transformed into a concrete problem, a promising route, and a theorem family wor...
arXiv:2606.24900v1 Announce Type: new
Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired thr...