The Archive

CausaLab environment benchmarks LLM agents on interactive causal discovery with validation of both solutions and underlying causal mechanisms.

The nine-year-old startup is replacing hundreds of employees with thousands of AI agents.

68-cell empirical study: LLM agents show +19.69pp higher sensitivity to semantic noise vs. surface noise across reasoning tasks.

ProAct: proactive agent architecture using idle-time compute to predict and prepare for future user requests via dialogue history analysis.

Practitioner observes that phrasing dependency relationships improves agent instruction compliance from 75% to 90%+ across email/Slack/Sheets automation.

Hugging Face revives PapersWithCode.co with new SOTA tracking features for agents, vision, and time-series; week 1 update.

- NEW: Tool Description: Workflow — Describes the Workflow tool for opt-in deterministic multi-subagent orchestration, including script metadata, agent hooks with plain-text or structured returns, pipeline vs. parallel control flow, token budgeting, quality patterns, concurrency limits, and resume behavior. - NEW: Agent Prompt: Workflow subagent plain text output — Instructs workflow-spawned subagents to return raw final text as the calling script's parsed value, avoiding human-facing confirmations, markdown wrappers, or SendUserMessage delivery. - NEW: Agent Prompt: Workflow subagent structu...

Multi-agent LLM framework with Creator and validation agents generates physics-constrained constitutive models ensuring compliance with continuum mechanics laws.

OpenSkillEval automated framework audits open-source skill-augmented LLM agent systems for quality, compatibility, and cost-performance trade-offs.

PCSP shared RL policy for persona-consistent NPC control in life sims achieves 22x faster inference than LLM baselines on 300-persona benchmark.

Co-ReAct integrates step-level rubrics to guide ReAct agent reasoning in multi-step search tasks.

PushBench evaluates quantitative goal persistence in long-horizon LLM agents via work-unit completion.

Goal-conditioned RL agent leverages parallel all-goals learning by jointly predicting values and actions for every goal simultaneously.

OpenAI named Leader in Gartner's 2026 Magic Quadrant for Enterprise AI Coding Agents; Codex cited for innovation and scale.

Daytona CEO discusses agent infrastructure platform achieving 74% MoM growth, 850K daily runs, and bare metal sandboxes for RL evaluation.

Anthropic's June 15 Agent SDK pricing changes signal shift toward managed agents, pressuring third-party integrations and local deployment strategies.

Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addresses this via intrinsic rewards derived from the mismatch between the agent's predictive model of the world and reality. However, translating this intrinsic motivation to complex, photorealistic environments remains difficult, as agents can become trapped in local loops and receive fresh rewards for revisiting forgotten states. In this work, we demonstrate that this failure stems from a lack of spatial persistence a...

Autonomous agentic systems are largely static after deployment: they do not learn from user interactions, and recurring failures persist until the next human-driven update ships a fix. Self-evolving agents have emerged in response, but all confine evolution to text-mutable artifacts -- skill files, prompt configurations, memory schemas, workflow graphs -- and leave the agent harness untouched. Since routing, hook ordering, state invariants, and dispatch live in code rather than in any text artifact, an entire class of structural failure is physically unreachable from the text layer. We argue ...

Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing systems communicate through natural language, recent work shows that latent communication, particularly through transformer key-value (KV) caches, can improve efficiency and preserve richer task-relevant information. However, KV caches also encode contextual inputs, intermediate reasoning states, and agent-specific information, creating an opaque channel through which sensitive content may propagate across agents without explicit textual disclos...

LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, including files and process state (e.g., memory, contexts, etc.). Existing mechanisms duplicate the entire state, causing hundreds of milliseconds to seconds of latency per C/R, which severely bottlenecks deep search and large-scale fan-outs. This paper observes that subsequent checkpoints in AI agents are highly similar. Therefore, instead of full duplication, a sandbox should only duplicate the ...

Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the dominant single-agent paradigm for physical applications, where other actors are ignored or treated as environmental noise, preventing effective coordination. Here we show that multi-agent reinforcement learning provides the essential safety scaffolding required for real-world interaction. Using high-speed quadrotor racing as a high-stakes testbed, we train agents to navigate complex aerodynamic interactions and strategic...

WorkstreamBench evaluates LLM agents on end-to-end spreadsheet construction in finance workflows, filling gap in agent evaluation.

Gemini 3.5 Flash achieves top score on APEX-Agents-AA benchmark, exceeding larger model performance on agent tasks.

The AI agents are coming. A lot of them.

Comparative evaluation of coding agents (GitHub Copilot, Pi, Claude Code, OpenCode) using Qwen 3.6 27B isolates model vs. harness performance.

The next big thing for Nvidia will be CPUs for AI agents, $200 billion worth, CEO Jensen Huang predicts.

Railway launches agent-native cloud platform with 3M users, 100K weekly signups, own data centers, and $200K+ monthly coding agent spend, positioning agents as core infrastructure.

Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating... Autonomous AI agents are taking on all types of work for businesses: routing logistics fleets, triaging support tickets, generating code, and orchestrating multistep workflows. How do you take a general-purpose model and make it excel at your specific task? Customization provides an agent with the right capabilities. This post explains nine techniques for customizing AI agents… Source

Agent JIT compilation compiles task descriptions into executable code for web agents, reducing latency vs. sequential fetch-execute loops.

roto 2.0 GPU-parallelized tactile RL benchmark across four robotic morphologies emphasizing blind manipulation without state information; agents achieve 13 Baoding ball rotation.