The Archive

TraceLab releases 4,300 real coding-agent sessions with 350K LLM steps for analyzing serving workloads across agents and models.

Identifies entity binding failures in tool-augmented LLM agents as distinct safety problem: selecting correct tool but acting on wrong entity.

AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on... AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on behalf of a user. This unlocks productivity, but can also give agents access to sensitive enterprise data and the ability to complete tasks and take action across business systems, making a secure, governed environment essential. Source

Study compares nine open-weight LLMs against human participants in networked Prisoner's Dilemma, finding selected models reproduce cooperation macro-dynamics but lack individual fidelity.

Enterprise investment in AI is booming. Gartner is calling 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents…

Survey of persistent-state LLM agents covering memory, credentials, audit records, and lifecycle governance across six diagnostic axes.

ManimAgent: self-evolving multimodal agent with episodic memory bank carries reflection experience across code-generation tasks for scientific visualization.

LoRA variants for continual motion-language agents: addresses stability-plasticity tradeoff in bidirectional motion understanding/generation under sequential task learning.

Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infrastructure that coordinates projects, agents, and digital and physical resources. We identify this as a shift from code-centered execution loops to research-oriented collaboration processes, where questions, evidence, participants, and resources must be coordinated under uncertainty. In this framing, an agent may be an AI system, a human researcher, a team, a labor...

Current multimodal fusion approaches, particularly those based on static Mixture-of-Experts (MoE) architectures, often struggle to provide the adaptive and efficient collaborative reasoning required by complex real-world applications. We introduce the Dynamic Agent-based Interaction Network (DAIN), which reconceptualizes multimodal fusion as a dynamic, multi-agent collaborative process. DAIN employs a context-aware Meta-Controller that dynamically schedules sparse activation of specialized interaction agents and orchestrates compressed inter-agent communication for consensus-building. The fra...

Dynamo enables frozen VLMs to evolve reusable skills and visual tools without retraining, improving reasoning on visual tasks.

MirrorCode benchmark evaluates AI coding agents on full program reimplementation tasks, extending beyond short coding exercises.

Introduces AgentCanvas, a framework automating embodied agent architecture search via typed-graph runtime, extending prior text-domain AAS to perceptual agents.

Jon Udell argues for 'agent-assisted' framing in software development: agents join human workflows rather than replacing human authority in autonomous loops.

AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep... AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep some context across a session. Today, we have long-horizon agents. Systems that plan many steps, split work between sub-agents, keep context across a long task, and run tools in a safe sandbox. The NVIDIA AI-Q Blueprint is an open source… Source

Fictional incident report imagining competing AI code review agents entering costly disagreement loops, illustrating multi-agent coordination and cost/security risks.

Agent-Native Immune System (ANIS): runtime defense framework for autonomous agents against memory poisoning, tool-chain manipulation, multi-agent attacks.

Study shows autonomous AI coding agents pass individual benchmarks but accumulate integration friction in shared codebases, revealing ecosystem-level risks.

CPAgents uses agentic iteration to auto-generate cardiac phenotypes for disease association studies via composite feature discovery.

Tandem RL training pairs weak and strong agents to maintain verifiable reasoning quality while improving compatibility and readability.

ToolPrivacyBench evaluates purpose-bound privacy in multi-tool LLM agent trajectories, filling gap in agent evaluation.

Cohere publishes practical guide on building AI agents for enterprise automation, covering reliability, security, and deployment patterns.

Agent testing startup Patronus AI, founded by former Meta AI researchers, is experienced nearly insatiable demand, its investor says.

Notion is "going all in on using agents to run your inbox."

PEEU method trains open-source MLLMs for GUI task planning via autonomous environment exploration and hindsight experience replay.

Study identifies co-failure ceiling limiting accuracy gains in multi-model LLM routing/voting systems across 67 frontier models; proposes beta metric beyond pairwise correlation.

Framework for persistent embodied agents combining cyber-physical action spaces with autonomous failure recovery in unstructured environments.

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

Joint learning framework combines natural-language experiential rules and parameter updates to improve LLM agent policies in sparse-reward multi-step environments.

A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.