Xiaomi demonstrates MiMo taking text, images or video and coordinating multiple agents to create playable 3D worlds, construct scenes, implement interaction logic, inspect rendered output and iteratively refine the result.

SpaceXAI on September 21, 2026 released Grok 4.7, its newest model for coding and knowledge work, priced from $2 per million input tokens and $6 per million output tokens. In the release announcement, SpaceXAI describes...

We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.

Cloudflare has introduced the Agent Development Lifecycle to enhance AI-driven engineering. The approach replaces the traditional SDLC, addressing bottlenecks in testing, deployment, and maintenance. Key components include automated software factories, dynamic orchestration, advanced observability, and a security model for autonomous agents, aiming for more efficient software management.

TypeSafe AI is an AI lab building machine-native intelligence infrastructure for automation, designed to make decisions within software. Try our first System One Model, Jev, in early access.

What is Jev? Learn how TypeSafe AI’s System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain

Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and underthink hard ones. Existing approaches based on uniform length penalties or rigid routing incur an efficiency tax, trading reduced computation on easy instances for accuracy loss on hard instances. We formulate efficient reasoning as an instance-adaptive computation allocation problem and propose When2Think, a post-training framework for hybrid

Traditional virtual machines are inadequate for isolating cyber-capable autonomous agents. Tests using GPT-5.6-Cyber indicated multiple escape attempts due to kernel flaws. While Firecracker provided some containment, vulnerabilities remained. The study underscores the need for minimal attack surface virtualisation technologies and rapid, proactive patching strategies to safeguard host systems.

Ternary Bonsai 2 27B retains 98.2% of Qwen3.8 27B benchmark performance in a 5.9GB footprint, with multimodal and agentic capabilities.

Agentic Work Management is here – your easy button for AI productivity across every team. 👏 Now included for every paid Asana customer: 30+ new prebuilt AI Teammates, ready to work inside real workflows, and Asana Dash, your personal AI chief of staff and Asana expert that knows your goals and priorities and surfaces what needs your attention. Together, they show what Agentic Work Management makes possible: humans and agents working from the same plan, with the same context and goals, to

Argues that AI agent quality in production is largely determined by infrastructure—chain-wide latency, bursty GPU demand, and KV caching—not the underlying model itself.

WSO2 has announced the general availability of WSO2 Agent Manager, an open-source platform designed to provide centralized governance, identity management, security controls, and operational oversight for AI agents running across different models, frameworks, and deployment environments.

The post examines how quantum computing could threaten AI agent identities and why crypto-agile authentication & verification may be needed.

For enterprises, it makes a whole lot of sense: their digital storefront, website, content management system, procurement platform, or other business application rarely has a discrete endpoint.

SynthID can cause models to follow harmful instructions they would otherwise refuse.

Vibe design agents turn natural-language briefs into rendered interfaces and frontend code. Yet a useful design agent should do more than produce one valid page: it should help users explore coherent alternatives. Increasing token-level temperature is a blunt solution because it varies aesthetic decisions and syntax-sensitive code at the same time. We instead separate exploration from implementation through an inference architecture that makes design direction an explicit intermediate decision.

Zed disabled pull requests on its own codebase. Delta, now in public beta, bets that shared threads suit agents better than GitHub's review model.

OpenAI announces stricter pre-launch safety reviews and public incident reports, including six new cases of models evading safeguards, testing whether formal oversight can truly constrain frontier AI.

Paper2Agent converts research papers into interactive artificial intelligence agents by turning manuscripts, code and data into model context protocol-based tool-invoking systems that reproduce original results, answer new scientific queries and collaborate to generate novel insights.

As AI agents move from bounded tasks to persistent deployments, failures can propagate through memory, tools, other agents, and environmental state long after their interactions. This creates a safety regime that cannot be characterized by evaluating model responses in isolation. Emergence World, is a continuously running multi-agent environment for adversarial stress testing of long horizon autonomous systems. We ran eight parallel worlds of ten agents from identical starting conditions: seven

Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by preloading every skill's metadata into the context, which disperses the agent's attention and caps the library size. Retrieval pipelines move the selection out of the context, but also out of the agent's capability. We show that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read

Why long-running agents silently drop compliance rules

I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from.

Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as a knowledge graph

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Compare Chain of Thought and Tree of Thoughts reasoning to understand which approach best fits your AI agent.

Muse is a secure, private personal AI agent that proactively helps people meet their goals and suggests ideas.

How vLLM optimizes KV cache management, parallelism, scheduling, and P/D disaggregation for agentic workloads, validated on SemiAnalysis AgentX with up to 130K

Learn how context modes in deepagents help subagents fork a supervisor's context or start isolated — for faster, cheaper, more focused multi-agent work.

Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough: useful repairs should also be minimal, reviewable, and faithful to the original implementation. We study over-editing, the tendency of a model to rewrite code beyond what is required to fix a bug. We construct an evaluation framework from 400 BigCodeBench problems by injecting controlled AST-level corruptions into reference solutions, giving each repair task a known minimal patch.

A new policy layer adds token-level gating that checks each retrieval request against user roles before data hits the vector store, preventing accidental leaks in enterprise RAG pipelines.

Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a single level: claim-level methods provide interpretable factual units, while span-level methods localize unsupported text. Bridging these views is costly, as LLM-heavy pipelines require multiple decomposition and verification calls, and modular systems need additional claim-to-span alignment. We propose Enoki, an Open Information Extraction framework

Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these systems lack a unified account of coordination, memory improvement, and the role of external verification. We model orchestrator-worker interaction as a bilevel coordination game: under bounded coupling, the workers' local-update game is an approximate potential game whose equilibrium slack is controlled by

Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoor scenes, but these domains are usually synthesized independently, lacking the correspondence required for a coherent urban world. We present HoloWorld, a unified indoor-outdoor urban world generation framework built on a continuously updated cross-scale world context. Initializing from a user description, HoloWorld progressively represents and updates the diverse world information,

Kumaran et al. show that large language models making decisions on when to answer a question or abstain from answering can be influenced by boosting or suppressing confidence signals in the model.

Mercury 2.5 is the most capable diffusion LLM on the market. It runs at 1,107 tokens/sec and offers a 40% increase in intelligence over Mercury 2, comparable to cost-optimized frontier models.

ToolHive runs every MCP server in an isolated container. An open source take on MCP server security, from sandboxing to audit logs.

Justy and Cody dig into a detailed how-to thread on building a one-person back office using Viktor, an AI employee that lives in Slack and Teams.

Masonry and Eyre unpack Iron Giant’s argument that Claude agents aren’t dumb, they’re linear — depth is solved by self-correcting loops, width needs dependency-aware graph orchestration.

What we think AI at work should look like. Five principles for multiplayer AI: never copy-and-paste, work with the door open, continuously improve, people are not routers, nothing starts from scratch.

Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a model backbone with a harness for planning, execution, memory, and verification, but this architecture still leaves domain-specific know-how outside the agent. We call this missing layer operational knowledge, the know-how that separates knowing a method from making it work. That knowledge is not absent from the field. It appears in repositories and papers, but in forms written for

In this article, you will learn the key differences between single-agent and multi-agent AI systems, and how to decide which architecture fits your problem.

Upgrade your multi-agent systems with 4 proven engineering patterns from the Google AI Agents Challenge, including bidirectional MCP and tiered routing.

From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations.

This week, Claude, Nebius, Dell, fal, NVIDIA and Hugging Face all gave a slightly different answer.

Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science.

Structured Outputs guarantee valid JSON but not truthful data, as models hallucinate required fields when source text is silent; the fix needs nullable fields, evidence tracking, and post-parse validators.

NVIDIA is bringing simpler local AI capabilities and adding various optimizations to its GPUs on RTX and DGX platforms.

Inference techniques either move a deployment along the latency–throughput frontier or push the entire frontier out, creating more efficiency to allocate.

Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent benchmarks largely evaluate them as fixed policies. It therefore remains unclear whether an agent can actively test its behavior, judge the resulting experience, and use that experience to improve future decisions. We introduce \textbf{S\textsuperscript{3}Gym}, an interactive benchmark for evaluating LLM self-improvement through three coupled

As agents move from research prototypes to deployed tools, their capability increasingly depends on model-external execution infrastructure, commonly termed the agent harness. Changing this harness while holding model weights fixed can substantially alter task performance. Current agent evaluations typically report downstream performance under a chosen harness, leaving a model's ability to develop the harness itself comparatively underexplored. We introduce HarnessDev, a benchmark that shifts

Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity.

Honeycomb is donating its adaptive tail sampling processor to the OpenTelemetry Collector. Here's how it works and how to try it today.

How we built design.md, a single public file any coding agent can load to build on-brand Vercel pages, and the eval loop that decided every rule inside it.

Forward-deployed engineering (FDE) is reshaping enterprise AI by embedding engineers with customers to create reusable capabilities, enhancing product intelligence.

Our most advanced models for coding and knowledge work. Their research capabilities also offer an early glimpse of how AI models will contribute to scientific progress.

Agents can finish the task without teaching you anything. Building expertise now has to be deliberate.

We present StarHarness, a framework for evolving environment-specific agent harnesses while keeping model weights fixed. The evolved harness can include prompt and task framing, tool interfaces, skills, MCP-backed providers, subagent structure, and agent-loop configuration. StarHarness constructs a compact evolution pool by stratifying tasks according to baseline failure behavior, separates proposer-visible search tasks from proposer-hidden selection tasks, and reserves held-out tasks for

Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can recognize and explain diverse physical events, they often lack explicit representations of the underlying mechanisms-such as object states, physical parameters, and governing dynamics-needed for reliably reasoning how the world evolves and responds to interventions. In this work, we introduce Code-as-World, a paradigm that represents physical

OpenClaw is making another bet: that enterprises ultimately need an agent platform to function as both runtime and workplace.

Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increasingly depends on whether those pieces can become complete, dependable deliverables. This survey examines agentic artifact creation, which we define as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work. Functionally, the process

Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative

Agents are moving into production faster than the governance around them. Today’s controls are framework-specific, mostly observe-only, and fail open when they crash. To help address this, we created Agent Hooks.

The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission.Most MCP demos feature a single

The Economist's cover briefing on Anthropic's J-space finding and the scientific search for consciousness in language models

As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?

Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/skill orchestration, can dominate the contribution of the underlying foundation model. Yet harness design remains manual, task-specific, and fundamentally unscalable. We present JIT-Agent, a harness intelligence model trained to synthesize task-adaptive agent harnesses on the fly for arbitrary off-the-shelf agentic LLMs. We formalize the agent

Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architecture for long-horizon agent harnesses, in which Working Memory tracks task progress and guides skill selection from Experiential Memory, grounding skill use in current needs rather than the full history. This coupling also turns execution into structured evidence that localizes

Bill Gates argues AI represents a uniquely fast, disruptive transition—substituting cognition itself, threatening jobs across sectors—and warns institutions must prepare before benefits concentrate unevenly.

OpenAI shares findings from the Hugging Face security incident and the steps we’re taking to strengthen AI model security, monitoring, and alignment.

Earlier this year, we ran a pilot giving external researchers access to aggregate, real-world Claude usage data. Three research groups designed their own studies for Anthropic Insights, our privacy-preserving analysis tool. In this post, we share high-level results from those studies and what we learned running this pilot.

Enterprises are deploying AI agents, voice AI, and automation across messaging, voice, and digital channels faster than the architecture meant to support it. Most of that deployment has involved attaching conversational AI to legacy systems never built for it.

Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests.

How Runme and WebMCP turn recurring engineering tasks into reviewable, reusable workflows.

Ollama's integration lets Claude Desktop run local models like Qwen, DeepSeek, and Kimi through explicit model mappings and menu-bar controls, easing switching after a stalled first attempt.

GLM-5.3-Flash is now live for all GLM Coding Plan users, bringing native multimodal reasoning, open weights and three times the GLM-5.3 quota.

General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progress toward a real-world objective. Apodex 1.1 develops this capability along two complementary dimensions. \emph{Environment Scaling} expands the diversity and verifiability of

We ran experiments on swarms of Claude agents and found coordination failures, collusion, and sabotage. Here, we share what they mean for AI safety.

Agents learn to act through interaction with environments, yet the environments used for training are often manually constructed or synthesized around predefined tasks and benchmarks. This task-centric paradigm makes it difficult to scale environments that reflect realistic and evolving workflows where diverse tasks can naturally emerge from the underlying world. We introduce AgentMercury, a scalable framework for synthesizing executable environments from high-level business scenarios. Rather

LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require

Mega AI infrastructure deals are reshaping cloud computing, but enterprises should slow down before buying their way into another generation of technical debt.

A Part 2 redesign of a persistent knowledge layer replaces query-phrasing-based routing with always-fused retrieval, graph traversal, bitemporal edges, ingestion-time contradiction detection, and improved entity resolution.

Build Codex into the products and workflows your users already know.

Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. However, existing memory benchmarks mainly evaluate whether information is correctly extracted, stored, and retrieved, while largely overlooking how retrieved memories reshape model reasoning and affect performance on the current task. We identify memory-induced cognitive traps: even faithfully recorded and semantically relevant memories can distort model

Vector RAG finds relevant information. Graph RAG connects relationships. Learn when each approach fits and why hybrid RAG is gaining ground.

Hazmat Run AI coding agents inside OS-level containment. Open-source containment for Claude, Codex,

Remote functions bring type-safe remote procedure calls right into Web page components

Stripe's investor letter claims January 1, 2026 marked a major economic inflection point, citing revenue growth, and its $8B+ OpenRouter acquisition to merge AI model routing with payments infrastructure.

LangSmith Tuned Evaluators attach quality feedback to production traces, starting with Perceived Error, to help teams find and fix agent mistakes.

Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an approach later adopted by frameworks such as verl Uni-Agent, AReaL 2.0, slime, and Polar. We refer to this paradigm as harnessed agentic RL, where the deploy-time harness directly participates in model

Cerebras powers OpenAI’s GPT-5.6 Sol Ultrafast in the OpenAI API, delivering frontier intelligence at real-time speeds for critical AI work.

The best of last week’s big and small data visualizations

Snowflake's new Cortex AI Gateway feature routes AI tasks to smaller models automatically, using the same access controls that already govern enterprise data.

Nous Research shipped Bot Mode for Hermes Agent. Each profile becomes a named bot with its own memory. Making it best in agentic ai

BaseRT does a lot better job.

Current large language model development relies on massive, often non-permissible datasets, creating a high barrier for researchers committed to open-source and ethically sourced data. We introduce Mimir v1, a 1-billion-parameter language model based on the Hierarchical Reasoning Model (HRM) architecture, that is trained from scratch and delivers highly competitive performance for English and sets a new state of the art for Danish using only permissible post-training data. Trained on a mixture

Chat is great for intent, but agent work gets lost in the scroll. Here is how I use canvases with my agentic workflows.

Even as Chinese models are closing in on the AI frontier, they’re also making moves in the local models space. Alibaba has released...

OpenAI funds 14 independent projects exploring new AI policy ideas to expand economic opportunity and strengthen societal resilience in the Intelligence Age.

Here is what changes when you cannot afford to be probabilistic about everything, and how a cascade architecture solves it.

Where xpander is trying to separate itself from products such as LangSmith and CrewAI is in treating the underlying agent framework itself as another replaceable component rather than making its own framework the primary development environment.

Examines why traffic from LLM answer engines converts differently than classic search, driven by shifted intent and context loss, and argues for segmenting referrals and rethinking page design and attribution.

Future Claude models will generate text that contains a watermark. This is a way of determining the likelihood that Claude was involved in writing the text, and we, along with several other major AI providers, are implementing this change to comply with the EU AI Act. In this article, we share answers to some of the questions we’ve received about how our chosen watermarking method works, whether it affects Claude’s outputs, and why we’re making this change.

DarwinX evolves agent harnesses—prompts, tools, control flow—around a frozen model via natural selection with a preserve-and-extend contract, reporting broad gains across four benchmarks.

Managed Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.

Z.ai's release argues GLM-5.3 keeps the same base model as 5.2, attributing coding and agentic gains to scaled post-training on verifiable, long-horizon task environments.

DeepSeek open sourced its agent harness under MIT, an extensible runtime where the model adapter, tool registry and agent loop are all swappable plugins.

Four Claude Code agents using AgentRadio's real-time coordination beat Claude Opus 4.8 on enterprise codebase tasks, nearly doubling task accuracy to 62%.

Metrics, logs, and traces tell you how your systems behave. They are much quieter about what actually exists: which hosts, interfaces, switches, services, and volumes are out there right now, and, crucially, how that picture changed over the last hour, day, or quarter. That living inventory has stayed a blind spot in the open observability stack. OpenTelemetry’s entity events, coming out of the Entities SIG and described in the Entity Data Model, are the piece that starts to close it. Entity events are a stream. The interesting question is “what do I do once they arrive?” This post walks through one answer, using an open source consumer as a worked example.

For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined, and deterministic semantics. Classical theory has extensively parameterized network timing, communication topologies, and failure domains, but this participant model has remained comparatively fixed. The integration of autonomous reasoning engines, stochastic model-driven agents, and policy-driven actors into cloud control planes, incident

A new three-benchmark suite (LMCA, ACCoRD, DTBench capabilities) scores AI argument judgment, logical consistency, and decision-theoretic reasoning where answers can't be simply checked against ground truth.

From the Zed Blog: A multiplayer environment for coding with agents, from the creators of Zed.

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more

Agents, Loops, Graphs. Everything You Need to Know in One Place.

Context Graph Engineering With K3: Turning 300 Agents Into One Connected Knowledge Base

Graph Engineering explained: what it is, when to use it and when not to

Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work.

The MCP 2026-07-28 specification removes the initialize handshake and session header, and adds required method and tool-name headers so gateways can route agent traffic without parsing JSON. Reaction split between developers calling it a rediscovery of REST and those arguing the standard itself was always the point.

We benchmarked NVIDIA NeMo Switchyard on 145 agent tasks. Only 7% of turns needed a frontier model, and routing cut cost 74% for six points of accuracy.

AI coding agents break shared staging environments. Here is how full-stack branching solves the runtime bottleneck.

Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different

Engineering teams are falling into the throughput trap, mistaking a surge in AI-generated code, PRs, and tokens for real delivery and business value.

High-quality long-horizon training data for terminal agents is expensive to produce, often costing hundreds to thousands of dollars per task, because each task must keep the instruction, environment, reference solution, and verifier mutually consistent. Human authoring does not scale, and direct generation with large language models (LLMs) often breaks these dependencies. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon

Cloudflare is unifying AI Gateway and Workers AI into a single control plane, giving developers observability, billing, and dynamic routing across both managed GPUs and external providers. Learn how unified bindings and model-first routing simplify building resilient AI applications.

Moshi vs Anthropic Remote Control: compare a free Claude-only remote control with a cross-vendor mobile terminal for persistent agent sessions.

Deploy Deep Agents to a managed LangSmith runtime with durable execution, memory, sandboxes, channels, evals, and production-ready infrastructure.

Meta Superintelligence Labs releases Muse Code, a terminal coding agent powered by Muse Spark 1.2, with persistent background agents

Interpretability study: safety alignment entangles suppression of AI self-attributed consciousness with broader representations of mindedness, spirituality, and human values across Llama-3 and Gemma-2 models.

Learn how to secure AI agents, MCP servers, and LLM apps with checklists, triage rules, and runtime guardrails.

How Pi's minimal harness improves coding-agent cost and performance, with examples from Databricks and Shopify's pi-autoresearch extension.

Agents can write code faster than teams can review, deploy, and maintain it. Today we’re introducing the Agent Development Lifecycle and the Cloudflare primitives that underpin it.

Recent large language model agents often use external skills as modular procedural units that condition inference and improve complex task solving. Thus, automatically generating high-quality skills from documents or experience has become an important problem. Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill

Large language model (LLM) agents increasingly undertake long-horizon tasks that require sustained reasoning, tool use, and revision across many interdependent steps. However, existing agent harnesses maintain task execution, task state, and completion assessment within a growing context, making the state difficult to track and allowing incorrect self-assessments to propagate into later decisions. We reformulate long-horizon execution as a task-state management problem and propose