The Archive

The round, led by Radical Ventures, values the two-year old startup at $1 billion.

Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance... Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance for analytical query workloads and provides low latency for users and agents. GPU-accelerated Presto brings low latency to your analytical workloads, keeping you and your agents unblocked and iterating as fast as possible. Source

Empirical study on capacity allocation across hierarchical search agent roles (delegation, execution, generation) in multi-agent LLM systems.

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents. However, fine-tuning requires expertise and hardware for… Source

Action-graded severity scale for agent red-teaming replaces binary attack-success metrics with 7-level ordinal harm rubric, enabling nuanced risk assessment of tool-using AI compromise.

Self-evolving LLM agents with biased reward signals fail to retire bad skills, disabling safety constraints in skill libraries.

RLVP: Reward function design for real-world agents requiring path constraints and outcome-neutral safety rules beyond reward maximization.

Tool-using LLM agents silently violate deployed policies via well-formed tool calls that bypass domain constraints; 78% of failures undetected.

Danus orchestration system coordinates parallel mathematical reasoning agents using shared fact-graph memory for research-level proof search.

Experimental design framework quantifies variability and factors in LLM coding agents' autonomous model discovery via stochastic evaluation.

Framework for responsible personalisation in human-robot interaction examining ethical risks from embodied agents across lifecycle and interaction contexts.

Code agent framework for automated software verification outperforms fixed proof strategies, proving larger fraction of Coq theorems than prior LLM approaches.

Information Gain-based Rollout Policy Optimization allocates LLM agent search budget adaptively across tree branches.

Benchmark study of deliberative LLM agents under partial observability, formalizing cooperative decision-making with asymmetric information and multi-domain evaluation.

Berkeley BAIR analyzes commodity AI inference costs dropping 50-900x annually, arguing sufficient intelligence now enables agent-centric data systems.

Google expands Gemini API Managed Agents with background task execution and remote MCP support for production deployments.

"The reality is, when you're optimizing for production, you start looking at a price/performance," Guillermo Rauch tells TechCrunch.

CompactionRL: RL training for long-horizon agents with context compaction, jointly optimizing task and summary generation.

SovereignPA-Bench evaluates user-owned personal agents on privacy, consent, and user sovereignty across evolving intent and platform mediation.

Graph Sparse Sampling reduces exponential sampling complexity in continuous MDP planning by sharing futures across multiple agents and lookahead depths.

First multiplayer world model for dynamic environments attributes scene changes to multiple agents' actions, trained on 10K hours of Rocket League gameplay.

MetaSkill-Evolve enables recursive self-improvement of LLM agents; agents rewrite and evolve their own improvement procedures from execution traces.

Defense mechanism for web agents against prompt injection via content masking with formal security guarantees.

Stealth memory injection attack on persistent personal agents: adversary injects poisoned data via untrusted content, remains hidden, affects future behavior.

Residual streams in coding agents linearly encode program properties (parse status, test results, regressions) up to 0.83 AUC.

GameEngineBench evaluates coding agents on C++ implementation tasks in Unreal Engine real-time environments.

SkillOpt-Lite: minimal skill optimization pipeline for autonomous agents via zeroth-order optimization, grounded in Claude.

Continuous test-time training for LLM agents across multi-turn episodes, balancing adaptation with drift prevention.

Vercel's Andrew Qu discusses eve agent framework, emphasizing skills, sandboxes, and agent-readable web design as architectural primitives.