Google AI Introduces Gemini Embedding 2: A Multimodal Embedding Model that Lets Your Bring Text, Images, Video, Audio, and Docs into the Embedding Space
Google expanded its Gemini model family with the release of Gemini Embedding 2. This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address the high-dimensional storage and cross-modal retrieval challenges faced by AI developers building productio...
Fish Audio Releases Fish Audio S2: A New Generation of Expressive Text-to-Speech (TTS) with Absurdly Controllable Emotion
The landscape of Text-to-Speech (TTS) is moving away from modular pipelines toward integrated Large Audio Models (LAMs). Fish Audio’s release of S2-Pro, the flagship model within the Fish Speech ecosystem, represents a shift toward open architectures capable of high-fidelity, multi-speaker synthesis...
MASEval: Extending Multi-Agent Evaluation from Models to Systems
arXiv:2603.08835v1 Announce Type: new
Abstract: The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet existing benchmarks are model-centric: they fix the agentic setup and do not compare other syste...
LDP: An Identity-Aware Protocol for Multi-Agent LLM Systems
arXiv:2603.08852v1 Announce Type: new
Abstract: As multi-agent AI systems grow in complexity, the protocols connecting them constrain their capabilities. Current protocols such as A2A and MCP do not expose model-level properties as first-class primitives, ignoring properties fundamental to effectiv...
Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search
arXiv:2603.08877v1 Announce Type: new
Abstract: Agentic Retrieval-Augmented Generation (RAG) systems combine iterative search, planning prompts, and retrieval backends, but deployed settings impose explicit budgets on tool calls and completion tokens. We present a controlled measurement study of ho...
Interpretable Markov-Based Spatiotemporal Risk Surfaces for Missing-Child Search Planning with Reinforcement Learning and LLM-Based Quality Assurance
arXiv:2603.08933v1 Announce Type: new
Abstract: The first 72 hours of a missing-child investigation are critical for successful recovery. However, law enforcement agencies often face fragmented, unstructured data and a lack of dynamic, geospatial predictive tools. Our system, Guardian, provides an ...
AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem
arXiv:2603.08938v1 Announce Type: new
Abstract: The rapid emergence of open-source, locally hosted intelligent agents marks a critical inflection point in human-computer interaction. Systems such as OpenClaw demonstrate that Large Language Model (LLM)-based agents can autonomously operate local com...
arXiv:2603.08717v1 Announce Type: new
Abstract: AI-enabled Radio Access Networks (AI-RANs) are expected to serve heterogeneous users with time-varying learning tasks over shared edge resources. Ensuring equitable inference performance across these users requires adaptive and fair learning mechanism...
Hindsight Credit Assignment for Long-Horizon LLM Agents
arXiv:2603.08754v1 Announce Type: new
Abstract: Large Language Model (LLM) agents often face significant credit assignment challenges in long-horizon, multi-step tasks due to sparse rewards. Existing value-free methods, such as Group Relative Policy Optimization (GRPO), encounter two fundamental bo...
Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
arXiv:2603.08758v1 Announce Type: new
Abstract: Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques do not directly apply. We show that, when a group $G$ acts transitively ...
SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning
arXiv:2603.08763v1 Announce Type: new
Abstract: A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie tas...
By Vivek TrivedyTLDR: Agent = Model + Harness. Harness engineering is how we build systems around models to turn them into work engines. The model contains the intelligence and the harness makes that intelligence useful. We define what a harness is and derive the core components today's and tomor...
How Coding Agents Are Reshaping Engineering, Product and Design
EPD (Engineering, Product, and Design) at software company is about creating good software. Separate roles exist, but the end goal is functional software that solves a business problem that users can use. At the end of the day, this is just code. It is important to recognize that the output
3 Questions: Building predictive models to characterize tumor progression
Assistant Professor Matthew Jones is working to decode molecular processes on the genetic, epigenetic, and microenvironment levels to anticipate how and when tumors evolve to resist treatment.
How Joseph Paradiso’s sensing innovations bridge the arts, medicine, and ecology
From early motion-sensing platforms to environmental monitoring, the professor and head of the Program in Media Arts and Sciences has turned decades of cross-disciplinary research into real-world impact.
NVIDIA AI Releases Nemotron-Terminal: A Systematic Data Engineering Pipeline for Scaling LLM Terminal Agents
The race to build autonomous AI agents has hit a massive bottleneck: data. While frontier models like Claude Code and Codex CLI have demonstrated impressive proficiency in terminal environments, the training strategies and data mixtures behind them have remained closely guarded secrets. This lack of...
Setting Up a Google Colab AI-Assisted Coding Environment That Actually Works
This article focuses on Google Colab , an increasingly popular, free, and accessible, cloud-based Python environment that is well-suited for prototyping data analysis workflows and experimental code before moving to production systems.
As Open Models Spark AI Boom, NVIDIA Jetson Brings It to Life at the Edge
The Cat 306 CR mini-excavator weighs just under eight tons and fits inside a standard shipping container. It’s the machine a contractor rents when the job site is tight: a utility trench near a foundation, a basement dig in a dense neighborhood. The cab is roughly the size of a phone booth. The oper...
Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
I really thought I was onto something big: add a couple of simple domain rules to the loss function, and watch fraud detection just skyrocket on super-imbalanced data. The first run looked amazing… until I fixed a sneaky threshold bug and ran the whole thing across five different random seeds. Sudde...
From raw interaction to reusable knowledge: Rethinking memory for AI agents
It seems counterintuitive: giving AI agents more memory can make them less effective. As interaction logs accumulate, they grow large, fill with irrelevant content, and become increasingly difficult to use. More memory means that agents must search through larger volumes of past interactions to find...
AgentMail raises $6M to build an email service for AI agents
AgentMail provides an API platform that lets you give AI agents their own email inboxes, with support for two-way conversations, parsing, threading, labeling, searching, and replying.