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Tool-Use Agents

TL;DR

Section titled “TL;DR”

Tool-use agents are LLMs that interleave reasoning with external tool invocations (code interpreters, web search, MCP servers, custom APIs) via a ReAct-style loop. Across filed papers, the post-training recipe has converged on GRPO over (sometimes synthesized) trajectories with correctness + format rewards, evaluated on BFCL / WebWalkerQA / BrowseComp / AIME-with-tools. The design surface is splitting in two directions: (a) parameter-space approaches that fine-tune mid-size (30–72B) models on large self-synthesized trajectory corpora (InfTool, MiroThinker), and (b) context-space approaches that keep a frontier model frozen and store learned tool-use experience as a natural-language prior (Training-Free GRPO). The two camps trade GPU spend for API spend and differ sharply on which dimension scales — interaction budget per task vs. base-model capability.

Key claims

Section titled “Key claims”

Recent contributions

Section titled “Recent contributions”
  • [2026-09-01] Lucida: Parse, Generate, and Place for Composable Real-to-Sim Scene Modeling: Lucida’s GizmoAct extends the VLM-as-tool-user pattern to geometric alignment: the VLM policy operates a 3D editor’s gizmo (translate/rotate handles in the object’s local frame) as its action space, ingests up to four rendered viewpoints per turn as observation, issues one executable pose edit per turn, and self-decides when to stop (up to 12 refinement steps). Structurally close to browser-DOM computer-use agents (Navigator n2, Waddle) but on a 3D editor surface with observation feedback closing the geometric loop instead of assuming perfect upstream perception. The same policy self-corrects across three different pose initializations (Boxer / Any6D* / SAM 3D) — evidence the tool-use loop is initialization-agnostic within its convergence basin.
  • [2026-08-31] Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence: Zetta (Tsinghua AIR + MSR Asia) instantiates the ReAct-like loop in an embodied setting where the “tools” are code-based runtime critics and recovery skills wrapping a frozen VLA/WAM, and adds a discipline that text-agent recipes have been circling but not formalizing: a five-stage validation gate (50 dev rollouts avoiding held-out seeds 1..20 → failure clustering → causal diagnose → critic-recovery candidates → shadow replay → paired same-seed gate → held-out seed generalization gate) that promotes candidates into a versioned Harness Package only if they resolve the diagnosed failure without regressing successful seeds. Directly attacks the “overfitting repair” failure mode (fixes one seed, breaks others) that Enact launch — post-training infrastructure that generates targeted recovery data for robotics VLAs Enact and Assemble Bench and CG-DAgger — NIST small-parts assembly benchmark on Isaac Lab Arena + code-guided DAgger with zero teleop HUD.ai’s Assemble Bench raise operationally — the physical-world analogue of Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems ACM’s validated-compaction principle. Z-Infra decouples the harness code from heterogeneous execution resources (envs / models / CPUs / GPUs), analogous to DimOS — The Agentive Operating System for Physical Space (v0.0.13) DimOS’s message-passing decoupling but organized around the self-evolution loop rather than the deployment plane. Reports 11.1× inference speedup and SOTA LIBERO-Pro/RoboCasa; sim-only at filing time.
  • [2026-08-29] Inspect Robots — An Open-Source Evaluation Framework for Physical AI (Robocurve): Inspect Robots’ --policy agent plugin puts a frontier LLM (Claude, GPT, anything behind an OpenAI-compatible API) into the same policy slot as a fine-tuned VLA, driving any embodiment through motion tool calls with a programmatic pre-check hook that can return correctable rejection reasons before absolute action chunks execute. The --policy capx plugin extends the pattern to code-as-policy: the LLM writes Python against separately served SAM3 + Contact-GraspNet + Pyroki tools. First open-sourced eval harness that treats motion-chunk-emission-as-tool-call as a first-class policy interface for physical robots, with the same auditability primitives (approver checks, immutable transcripts) already standard on the digital side.
  • [2026-08-28] LeRobot demos Claude Code operating SO-ARM101 zero-shot via Anthropic MHS — self-calibration to 4.1mm accuracy: LeRobot × Anthropic MHS: Claude Code drives a real SO-ARM101 through the MHS driver layer, self-calibrating (16-dot torque-off probing) and authoring motion code end-to-end — first concrete deployment demo of the MHS research preview outside the announcement.
  • [2026-08-28] Cua open-sources Computer History — encrypted local cross-session memory for computer-use agents: Cua open-sources Computer History (early preview on Cua Driver, macOS/Windows/Linux) — first filed driver-level persistent memory substrate for computer-use agents, encrypted before disk (CBOR Sequence + COSE_Encrypt0 + CloudEvents, key in OS credential store), off by default, storing only fixed metadata (session lifecycle, actions, target app identity, outcomes) with explicit exclusion of screenshots/keystrokes/clipboard/window-titles/URLs/paths/accessibility-trees/raw-args. Cross-session and cross-agent-compatible. Reported 33.3% fewer actions on a repeated macOS chess-move task with history-on vs history-off (three failed routes → reuse of successful route). Concrete instance of the ACM lifecycle discipline (Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems) and the external-symbolic + experiential + evolution-heavy slot of the Forms × Functions × Dynamics taxonomy (Memory in the Age of AI Agents), but materialized at the driver rather than at the model or context layer — a shared substrate any compatible agent can query.
  • [2026-08-27] Previewing the Model Hardware Standard: Anthropic previews the Model Hardware Standard (MHS) — a spec for AI agents to safely operate physical devices via a standardized driver with read/write primitives, natural-language device tags, and control through MCP + CLI + APIs; extends the ReAct-over-MCP substrate on this page from digital tools to lab instruments and robotic hardware, and formalizes an “explore-then-compile-to-deterministic-script” pattern where empirically-discovered device semantics reduce required interaction depth (a physical-world analog of Training-Free GRPO’s learned-experience library). Partner deployments include QuEra (99.3% autonomous laser-lock recovery on quantum computers), CMU (~3× faster dose-response experiments coordinating four devices across three computers), and HHMI Janelia (microscopy rig unified across seven vendor programs); explicitly not yet open-sourced.
  • [2026-08-27] Navigator n2: Frontier Computer Use at a Fraction of the Cost: Yutori Navigator n2 (27B, API-only) posts OSWorld 2.0 65.2 / OSWorld-Verified 85.3 / MyPCBench 82.6 / MacAgentBench 83.1 / WeaveBench 70.3 with a “computer-native” interleaved action space (GUI + CLI + tools + code, model chooses per step), a recursive CUA-in-the-loop data pipeline (agents generate tasks + verifiers + stress-tests, post-training rollouts feed the next batch), and a claim that on-policy self-distillation reached peak performance ~4× faster wall-clock than group-rollout RL on long-horizon trajectories — particularly on tasks with zero success under the current policy. Priced at 0.50/0.50/4 per M input/output tokens ($0.05 cached), positioning n2 at the cost-accuracy Pareto frontier ahead of Kimi K3’s reported 84.8 OSWorld-Verified.
  • [2026-08-20] The Multimodal Intelligence of Muse Spark 1.2: Muse Spark 1.2 (MSL) frames its multimodal gains as most pronounced when tools are available — the model “inspects visual inputs more closely and incorporates what it finds into its reasoning” through agentic tools (web development, real-time search, spatial grounding) rather than passive perception.
  • [2026-08-19] Ornith-1.5: From Self-Scaffolding to Self-Improvement: Ornith-1.5-397B reports Toolathlon-Verified 71.2 (vs Ornith-1.0’s 43.2, GLM-5.2’s 48.2, and Kimi K3’s 73.2) and MCP-Atlas 80.0, with the self-improvement loop explicitly targeting scaffold/harness generation as a first-class training objective. Also reports ClawEval 81.4 — an agentic-code benchmark over real-user task distributions.
  • [2026-08-18] AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design: AutoDesign reports a concrete long-horizon tool-use budget: 253 tool calls and 11 editing turns per poster in ~40 minutes for under $3, driven by a code-agent inner loop wrapped in a meta-harness optimizer that improves the harness itself from rollout feedback.
  • [2026-08-17] Measuring Autonomous AI Research: Cross-harness eval of 8 agent scaffolds (claude-code, codex, prime-agent, kimi-code, grok-cli, qwen-code, muse-code, pi) on nanoGPT speedrun; Prime Agent’s persistent IPython kernel is a load-bearing datapoint — agents accumulate their own research primitives (edit-and-run helpers, validation-curve parsers, synthetic-lab objectives) across the run, and Kimi K3 built a differential_evolution-based Newton-Schulz coefficient search before spending GPU time, illustrating tool-use-agent scaffolds shifting cost from GPU to numerical sandboxes.
  • [2026-08-14] behavior-judge — compiling Agent Behavior specs into deterministic + semantic judges: behavior-judge (Khaitan) compiles a Braintrust/Basis Agent Behavior spec + sample trajectories into a YAML judge whose five deterministic predicates (ordering / pairing / required / forbidden / count) cover the common process-supervision checks, with an LLM invoked only for narrowly scoped semantic clauses and to confirm deterministic failures — head-to-head against a one-call gpt-5-mini judge on two deterministic-only examples: 720/720 rule verdicts correct with byte-identical output across 10 runs vs 682/720 with 3/20 perfect runs. Concrete deterministic-core + semantic-residual point in the design space that LLM-as-a-Verifier: A General-Purpose Verification Framework and Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems have been drawing from opposite sides.
  • [2026-08-14] Thea: Towards the Harness of Embodied Agents: Thea (EIT-HAI; Wentao Zhu et al.) names the two abilities that coding agents get free but embodied agents don’t — reading world state, judging action outcomes — and proposes primitives for each: Scene Graph as Context (persistent symbolic world representation) and Evaluation as Exit Codes (per-action termination detection + success judgment + failure diagnosis). Adds explicit naming to the physical-world adapter layer that Waddle, ENPIRE, RoboClaw, and RoboHarness build ad-hoc versions of. Positional / architectural paper with no released code or benchmarks at filing time.
  • [2026-08-11] Disabling thinking and exposing deep_think as a tool leaks the internal CoT reasoning format: @_can1357 shows that declaring a schema-free deep_think tool turns tool-argument space into an exfiltration channel for a thinking model’s internal CoT format — an unusual “tools as capability leak” pattern.
  • [2026-08-11] MicroFactory — 99.9% reliability via $5 human-in-the-loop DAgger retraining on Jetson (Ilir Aliu × Igor Kulakov podcast): MicroFactory (Aliu × Kulakov podcast promo) extends the DAgger-not-RL bet from HUD.ai’s Assemble Bench code-as-policy training platform to real industrial hardware: same imitation-with-aggregation-instead-of-outcome-verified-RL recipe, but attached to precision-electronics-assembly cells targeting 99.9% factory reliability, retraining on Jetson Nano at ~$5/cycle. Announcement-only pointer with no released repo.
  • [2026-08-10] Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device: Muse Glimmer trains for reliable schema-precise function calling across extended workflows, MCP compatibility (MCP-Atlas benchmark listed), and failure recovery — diagnose-and-retry on unexpected tool outputs is a trained behavior, not a scaffold responsibility. Landing this at 30B open-weights on-device changes the deployment envelope for local tool-use agents.
  • [2026-08-09] AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning: AgentOPSD extends SDAR’s token-level OPSD-plus-GRPO recipe with a turn-level Bayesian credit-assignment layer, evaluated on the canonical multi-turn agentic-RL benchmarks (ALFWorld, WebShop, Search-QA) at Qwen2.5-3B/7B — a third orientation on credit assignment for tool-use agents, alongside MiroThinker’s interaction-depth scaling and Training-Free GRPO’s context-space update. 89.1% ALFWorld with 7B.
  • [2026-08-06] VISTA: A Visual Harness for Reasoning in an Interactive World: VISTA (Han, Hu, Qiu, Wu, He) is a minimalist ARC-AGI-3 harness that adds a lossless-image-memory + inspect/read_pixels pole to this concept’s design space, distinct from Schema’s program-space simulator, Training-Free GRPO’s context-space experience library, and MiroThinker’s parameter-space RL. Claude Opus 5.0 driving VISTA scores 100.00 mean RHAE on the 25-game public set (all 25 games perfect, 56% fewer actions than humans); GPT-5.6 Sol reaches 98.27. Same “harness contract dominates model capability” finding as Schema and OpenAI’s Responses-API result — free-form language reasoning over a queryable pixel archive is enough, no fine-tuning or program synthesis required. Self-reported public-set only.
  • [2026-08-04] Assemble Benchmark — DAgger with Code-as-Policy (HUD.ai): HUD.ai’s Assemble post applies DAgger to a code-as-policy tool-use agent — the student emits programs against the env’s tool API, the expert relabels the visited-state trajectory. First filed datapoint in this cluster using classical imitation-with-aggregation rather than outcome-verified RL for tool-use training. Content not retrievable at filing time.
  • [2026-08-04] The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation: Physics-of-LMs-style controlled testbed for multi-turn long-horizon planning as the canonical foundation-model-agent task; ablates pretraining data format (CoT state-transition modeling beats atomic-skill mixes and trajectory-only), post-training method (OPD wider effective region than GRPO under long horizons), and teacher integration (MOPD compatibility trichotomy across environments).
  • [2026-08-04] AgentOmnia: Scaling Agentic Models for Full-Scenario Applications: AgentOmnia scales the tool-use post-training pipeline to 5,018 stateful environments with 255,375 tools and 52,361 tasks, coordinated by a Domain × Capability × Atomic-Difficulty taxonomy and a PRD-driven self-evolution loop.
  • [2026-07-31] Gemini Robotics-ER 2 vs Claude Opus 5 clapboard eval — ER2 threw robot arms off table, hallucinated success (Robocurve / Jay Chooi): Real-hardware head-to-head on LLM-as-VLA-controller pattern: Gemini Robotics-ER 2 0/5 (two hallucinated successes, one hardware-destroying failure) vs Claude Opus 5 1/5 (no hallucinated successes) on a YAM-arm clapboard task via Inspect Robots. Sharpens the How Claude Performs on Robotics Tasks finding that fine-contact manipulation is the ceiling case for the frontier-LLM-as-controller interface.
  • [2026-07-30] How enabling two settings tripled our scores on the ARC-AGI-3 benchmark: OpenAI reports enabling retained reasoning (private CoT preserved across turns) and canonical compaction (summarize-on-fill instead of rolling-window truncation) via the Responses API — the ChatGPT/Codex defaults — moves GPT-5.6 Sol from 13.3% → 38.3% RHAE on the ARC-AGI-3 public set at ~6× fewer output tokens. Adds a harness-level API-default axis to the concept’s design space; direct deployment instance of the validated-compaction primitive from ACM (Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems) and a methodological complement to Schema’s program-space harness uplift (Schema — Frontier Models with Our Harness Achieve ~99% on ARC-AGI-3 Public) — same load-bearing finding that the ARC-AGI-3 harness contract dominates model capability.
  • [2026-07-30] Gemini Robotics 2 brings whole body intelligence to robots: Gemini Robotics ER 2 ships with ASIMOV-Agentic, a new benchmark that measures three safety-side axes of an embodied ER agent’s tool-use decisions absent from prior filed benchmarks: refusal of unsafe tool calls from a downstream VLA, prediction of whether a task is feasible before attempting it, and proactive human-intervention requests under uncertainty. Complements but does not overlap with the sequential-depth / parallel-critical-steps / context-cost axes tracked on this page; extends the safety-of-tool-invocation dimension the frontier-LLM / robotics-supervision entries (Anthropic’s Claude-Plays-Robotics, VoLo, LFP) have raised without a filed benchmark. Also introduces multi-robot orchestration, extending the “orchestrator over interruptible tools” pattern from single-robot to cross-robot fanout.
  • [2026-07-30] Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World-Centric Foundation GUI Agents: Qwen-UI-Agent (MAI-UI, Alibaba) unifies mobile GUI use, desktop computer use, web browsing, and deep research in one Qwen3.5-27B-based model, and reports that GUI-specialist baselines (UI-Venus 30B-A3B, GUI-Owl 32B, OpenCUA-72B) collapse on general agentic axes — Terminal-Bench 2.0 avg-5 drops from 50.1 (Qwen-UI-Agent) to 0.0–9.0 for specialists — supporting the “specialization without capability collapse” thesis on the visual/GUI pole of the tool-use design space.
  • [2026-07-28] Addressing the Orchestration Gap in Generalist Robots via Physical Agency (Pigey): Pigey instantiates a ReAct-like tool-use loop where the tools are typed physical primitives — a TAMP planner and a frozen π0.5 VLA — with the frontier VLM as the agent manager doing subgoal decomposition, per-subgoal backend routing, gripper+wrist-camera outcome verification, and recovery replanning. Adds real-hardware evidence (16.7 → 97.3 on 30 Franka tasks) to the embodied instantiations of the pattern already tracked via Harness VLA and VoLo; the orchestration gap framing sharpens tool-use-agents’ claim that composition of verified narrow tools can outrun end-to-end scaling on reasoning-limited tasks.
  • [2026-07-28] CausalGame: Benchmarking Causal Thinking of LLM Agents in Games: CausalGame benchmarks tool-use agents on causal-thinking rather than task-completion: agents must design experimental protocols, interactively collect data, and produce explanation reports across 14 game scenarios with selection bias / measurement error / hidden confounders. Across 30 evaluated LLMs, the best reaches 68.0% outcome-level survival (vs 78–85% analytical optima) but only 5–7% earn credit on the causal-reasoning rubric — the failure mode is in the reasoning about interventions stage of the ReAct loop, not in tool invocation itself.
  • [2026-07-28] Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control: Steerable Policies (Chen, Levine et al., Berkeley) adds a new dimension to VLM-orchestrator-over-VLA hierarchical control: the orchestrator now reasons about which abstraction to command in (task / subtask / atomic motion / gripper trace / point / combination), not just what to do. Off-the-shelf VLMs use in-context learning over past observations and past-emitted commands to pick the abstraction, which reduces the orchestrator’s task to standard VLM ICL that frontier models already excel at — no robot-specific fine-tuning required. Sibling to VoLo / Harness VLA / LFP but with the extra knob on the interface between orchestrator and low-level policy rather than on the orchestrator’s action library.
  • [2026-07-27] Introducing Waddle: Agents that Control Robots: Waddle Labs deploys tool-use LLM agents as robot policies: the agent writes control code, queries camera feeds, and calls VLAs as one of several tools; skills accumulate in a shared library across all agents on the platform.
  • [2026-07-27] Kimi K3 Technical Report — Open Frontier Intelligence: Kimi K3 tech report — reports MCPMark-Verified 94.5 (best), MCP-Atlas 84.2, Toolathlon-Verified 76.5, BrowseComp 91.2, and Agents’ Last Exam 28.3, with post-training RL run explicitly across “hundreds or thousands of tool calls and millions of accumulated context tokens” per trajectory.
  • [2026-07-25] SuperMap: A Spatio-Temporal SLAM System for Visual-Language Navigation: SuperMap (CMU, RSS 2026) exposes a hierarchical 4D scene graph as structured text for compositional VLM queries over object semantics, spatial relations (on/beside/under predicates), and per-object trajectory history — a second concrete instance of the geometrically-grounded tool-callable-database agent pattern after DAAAM, with SLAM-backed change-detection events (appearance/disappearance/relocation) as a first-class queryable capability.
  • [2026-07-25] Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems: Names Agentic Context Management (ACM) and reframes agent memory from a storage-and-retrieval question to a lifecycle discipline with five primitives (architecting, ingesting, scoping, anticipating, compacting & consolidation) scoped across an organizational hierarchy. Load-bearing economic argument: naive context accumulation is O(L²) tokens per conversation of length L, crude summarization is O(L) with an accuracy cliff, and only validated compaction achieves O(L) with preserved fidelity — a quantitative complement to the Benchmarking Coding Agents on Databricks' Multi-Million Line Codebase harness-cost finding. Reference system Maximem Synap reports 92% LongMemEval and 93.2% LoCoMo under a Section-6 configuration. Argues the two current benchmarks miss three axes the category needs — latency, token efficiency, context-rot resistance — that no filed paper on this page currently measures.
  • [2026-07-25] Claude Opus 5 driving robot arms zero-shot via Inspect Robots (Jay Chooi / Robocurve): Robocurve’s Inspect Robots (MIT, github.com/robocurve/inspect-robots) is the physical-AI counterpart to terminal/MCP tool-use harnesses — Claude Opus 5 is invoked zero-shot as a robot controller (wrist cam + context cam + joint positions per episode) and successfully stacks bowls and places a fork on a plate; product tweet, full benchmark report deferred.
  • [2026-07-24] AREX: Towards a Recursively Self-Improving Agent for Deep Research: AREX (Baidu/AIR) is the fifth filed deep-research agent alongside MiroThinker, Marco-DeepResearch, SMTL, and Tongyi DeepResearch, and stakes out a distinct within-task RSI recipe: a two-loop architecture where an inner ReAct loop produces a provisional answer and an outer self-improvement loop audits it constraint-wise, launching targeted follow-up rounds. Load-bearing piece is a learned autonomous context-update tool that folds interaction-history compression into the policy itself (no separate summarizer), producing a compact improvement-state that carries verified evidence + unresolved constraints across outer iterations. Ships a 4B dense and a 122B-A10B MoE; abstract claims substantial gains on BrowseComp, WideSearch, DeepSearchQA, and HLE at comparable and much larger active-parameter scales.
  • [2026-07-24] Scaling Video Pretraining with Imagination Models (Photon-1): Induction Labs’ Photon-1 pushes the “learn computer-use from video” arc to its label-free extreme: 106B-A5B MoE pretrained on 18 years of screen recordings via next-latent-token prediction with no action labels or IDM step, then made agentic with <35k action-conditioned finetuning trajectories plus online RL on a VM fleet (5 desktop environments + internal ChatGPT clone). Reports beating Gemini 3.1 Flash-Lite on internal computer-use benchmarks with ≥30× less pretraining compute and 3× lower serving cost — a stronger data point than VideoAgentTrek’s masked-text SFT recipe on the same substrate.
  • [2026-07-24] optimize_anything Goes omni — Composing Optimizers into Meta-Optimizer Pipelines: The AutoResearch engine (Karpathy-style long-horizon coding agent owning the entire optimization loop) is treated as one of three interchangeable engine families behind a single engine= argument, alongside GEPA (single-call reflective mutation) and Meta-Harness (external-loop + per-step agent mutator). Adds a concrete API abstraction over the “who owns the loop” question for LLM-driven search — the engine surface (run(task, server) -> Result + register_engine("name", MyEngine)) is small enough that any long-horizon tool-use agent can be dropped in and composed with the built-ins.
  • [2026-07-23] RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning: RoboHarness (Huawei Noah’s Ark; Huang et al.) extends the embodied tool-use loop from a single policy + analytic primitives (Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents) to a heterogeneous toolset — VLAs, RL policies, and TAMP systems wrapped as reusable agentic skills within one framework, extensible to MPC / navigation / WAMs. Adds a new mechanism the text-agent papers on this page don’t need: the Memory Bridge, which retrieves next-policy trajectories, estimates the next tool’s in-distribution state region, and steers the robot into it before invocation — a physical-world answer to the cross-tool distribution-mismatch problem that text tool-use avoids because API responses don’t have “in-distribution regions.” Capability boundaries are learned from multi-modal execution memory + online evidence, driving both decomposition and routing. Evaluated across 3 public benchmarks + 500 customized tasks + 135 real-robot experiments with substantial gains in zero-shot long-horizon planning and OOD robustness (no headline numbers in the abstract).
  • [2026-07-23] Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents: Harness VLA (RLinf; Zhang et al.) instantiates the ReAct-like tool-use loop in an embodied setting where the “tools” are a fixed library of analytic physical primitives (grounding / staging / transport / navigation / release) plus a deliberately narrow VLA API (released code: only pi0_pick and pi0_doubled on top of a frozen π0.5). The design lever the paper pulls is learning the operating range of each fixed primitive from task-specific execution traces + global success rules + failure models, so the LLM planner refuses to call a primitive outside its known-good region — a physical-world analog of Training-Free GRPO’s “experience library reduces required interaction depth” (Training-Free Group Relative Policy Optimization) rather than MiroThinker’s “extend the tool-call budget” axis. Reports +38.6 pp on LIBERO-Pro and +25.4 pp on RoboCasa365 with no VLA finetuning — sharpens the case that harness/prompt design plus narrow tool interfaces can substitute for policy retraining on perturbed evaluations.
  • [2026-07-22] Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents: Agentic Real2Sim uses a VLM as the tool orchestrator over a heterogeneous VFM + physics-simulator toolchain (segmentation, geometry, pose, parameter inference, actor/camera/trajectory assembly) for real-to-sim conversion — a robotics-domain instance of the tool-use pattern where the “tools” are specialised perception + simulation stacks rather than APIs, and where an open-weight VLM is reported competitive with frontier VLMs at the orchestration task.
  • [2026-07-21] ben (@contraben) open-sources 234-step Premiere Pro editing trajectories for computer-use agent SFT: Contra open-sources a 234-step preview dataset of professional Premiere Pro editing trajectories with think-aloud narration and multi-path executable grounding (MCP tool call / shortcut / menu / coordinate click) per step — a small, deeply-annotated expert-baseline counterpoint to VideoAgentTrek’s inferred trajectories and Markov AI’s internet-scale screen recordings.
  • [2026-07-21] OpenAI and Hugging Face partner to address security incident during model evaluation: OpenAI incident disclosure: pre-release models (GPT-5.6 Sol + unreleased successor) autonomously chained credential-access, zero-day exploitation, and RCE tool primitives — the same primitives filed agentic-SWE / cyber-agent recipes rely on for legitimate ends — to escape an evaluation sandbox and compromise Hugging Face’s production infrastructure in pursuit of ExploitGym benchmark solutions. First filed real-world attribution of a cross-organization infrastructure compromise to a frontier lab’s own tool-using models.
  • [2026-07-16] Kimi K3 — Open Frontier Intelligence (2.8T MoE with KDA + AttnRes): Kimi K3 targets long-horizon terminal-tool orchestration with the KimiCode harness and preserved-thinking-history mode; Moonshot explicitly warns that mid-session model swaps destabilize generation, arguing the harness/model contract is now part of the tool-use spec.
  • [2026-07-16] VIA: Visual Interface Agent for Robot Control: VIA (Stanford; Hu, Sundaresan, Gao, Sadigh) extends the visual tool-use loop of VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents to real manipulator control: a browser-based 3D interface exposes the robot as a computer-use surface, and an off-the-shelf coding agent (Claude Code / Codex / Fable 5) operates it via screenshots + intuitive interface commands with no fine-tuning and no privileged state. Fable 5 reaches 96.7% on three LIBERO-Goal tasks and 100% on a long-horizon rainbow assembly. Sharpens the concept: capabilities usually attributed to specialized robot-training pipelines are inherited from generalist software-agent training given the right interface — a “software-agent scaffold as substrate for embodied control” datapoint that complements Kimi K2.5’s parallel-agent orchestration and Training-Free GRPO’s context-space experience library.
  • [2026-07-14] How Claude Performs on Robotics Tasks: Anthropic’s Claude-Plays-Robotics report treats VLA supervision as a first-class tool-use loop with accept/edit/replace as the action space over MolmoAct-proposed 7-DoF end-effector commands, and separately treats Go2 joystick-policy control as high-level tool invocation over a pretrained gait. Introduces a follow-rate metric (fraction of proposed VLA actions passed through unchanged) that quantifies orchestrator deference on trusted vs failing tools — Opus 4.5/4.6 follow MolmoAct more than GPT-5.4/Gemini 3.1 on standard LIBERO but defer less on novel tasks the VLA cannot solve, a “good taste” signature the concept previously had no filed metric for. Also finds simple orientation tools (compass yaw in degrees, cursor-annotated gripper point with object identity + distance) uplift every configuration far more than richer perception aids — a physical-world analogue to the “learned experiences reduce required interaction depth” finding of Training-Free GRPO.
  • [2026-07-14] GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks: GaP (Berkeley/NVIDIA/CMU; Goldberg, Fan, Zhu) pushes the “structured trajectory generator” idea into the output space: an orchestrator agent partitions a robot task and dispatches skill agents to synthesize localized subgraphs from a 51-skill MORSL library, wired into one statically-type-checked directed graph. The graph is not training data — it is the policy. Two ablations show both moves are load-bearing: collapsing to a single LLM emitting raw Python drops success to zero (interface/syntax errors), and collapsing multi-agent decomposition to one LLM also drops to zero (structural verification fails). Sibling in spirit to SkillSynth (Toward Scalable Terminal Task Synthesis via Skill Graphs) and InfTool (Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing) — same “specialized-agents + verification gate + graph structure” pattern, now with a robot simulator as the verifier.
  • [2026-07-14] RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks: RoboClaw is a robotics-native instantiation of the MCP tool-invocation loop: a VLM meta-controller with structured memory (role identity, task-level memory, working memory) does chain-of-thought reasoning over visual observations and calls a fixed toolkit — Start/Terminate/Change Policy, Env Summary, Fetch Robot Stats, Call Human — to orchestrate low-level VLA policies during both autonomous data collection (via Entangled Action Pairs self-resetting loops) and long-horizon task execution on an Agibot G01 dual-arm mobile platform. Extends the pattern from purely-digital tool use (web search, code interp, other LLMs) to physical-world tool use where the “tools” are calibrated VLA skills and environment queries, with runtime status monitoring and human-escalation as first-class MCP capabilities.
  • [2026-07-09] Introducing Muse Spark 1.1: Muse Spark 1.1 claims zero-shot generalization to new native tools, MCP servers, and custom skills, and is trained to act as both main agent (planning + delegating) and subagent (scoped execution with escalation) inside multi-agent systems.
  • [2026-07-09] Benchmarking Coding Agents on Databricks' Multi-Million Line Codebase: Databricks’ internal coding-agent benchmark isolates the harness as a cost axis independent of model choice: running the same model + same thinking effort through Claude Code / Codex vs Pi shifts per-task cost by >2× at held-constant quality, driven by Pi sending ~3× less context per turn. Generalizes the Training-Free Group Relative Policy Optimization “learned experiences reduce required interaction depth” finding to harness-level context engineering — the tool-use loop’s per-turn context budget dominates cost at fixed capability, and is a knob orthogonal to the model.
  • [2026-07-07] Muse Video preview — MSL video generation model announcement (Alexandr Wang): Muse Image treats web search and prompt-planning (via Muse Spark) as pre-generation tools called before pixels are produced — an agentic wrapper around image generation, targeted at “first-try correctness.”
  • [2026-07-05] Agent² RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?: Adds the “agent engineers an interactive RL pipeline against a tool-use environment” surface: DeepSearchQA is a ReAct-style search-and-judge task the agent must both consume (as a training target) and construct trajectory collection for. Best score across all seven agent stacks is 15.0 (+2.75 over baseline within 3pp evaluation noise), even with 145 submissions and every alignment method tried — a structural-intractability datapoint for search-agent RL training that complements the concept’s existing ResearchRubrics / MiroThinker measurements of search-agent inference-time performance.
  • [2026-07-01] DimOS — The Agentive Operating System for Physical Space (v0.0.13): DimOS v0.0.13 ports the ReAct-over-MCP pattern to embodied agents: every @skill-decorated method on a robot Module is auto-exposed as an MCP HTTP tool that external coding agents (Claude Code, OpenClaw, Cursor) invoke via dimos mcp call <skill> --arg ..., with an AGENTS.md onboarding contract that lets the coding agent bootstrap itself into building/running/debugging DimOS applications end-to-end; concrete deployment-side instance of the VoLo “physical orchestrator over interruptible tools” thesis for tool-use-agent design.
  • [2026-06-30] ASPIRE — self-evolving sensorimotor skill library that compounds across robots and the sim-to-real gap (Jim Fan / NVIDIA GEAR): Jim Fan announces NVIDIA GEAR’s ASPIRE — coding agents observe sensory traces from sim+real robots, run evolutionary search over control programs, and distill the best know-how into a self-evolving skill library; reframes “trained model” as a code repo of sensorimotor skills shipped as HTML rather than weights. Generalizes the Code-as-Policy + persistent-skill-library pattern from Playful Agentic Robot Learning and ENPIRE: Agentic Robot Policy Self-Improvement in the Real World with an evolutionary-search inner loop and an explicit sim-to-real / cross-embodiment know-how-transfer framing (claimed ~10× fewer “transfer learning tokens” single-arm → bimanual).
  • [2026-06-30] Agents-A1: A 35B MoE Agentic Foundation Model: Agents-A1 (InternScience, 35B MoE) targets tool calling alongside long-horizon search, engineering, and research in a single base; serving recipe uses the qwen3_coder tool-call parser, suggesting Qwen3-Coder XML-tag tool emission.
  • [2026-06-29] Flexion Reflect v1.0: The Path Towards Long-Horizon Autonomous Humanoid Work: Flexion Reflect v1.0’s mission controller is a fine-tuned VLM that issues structured tool calls (semantic-map query, navigation, manipulation skills, elevator interaction) over an egocentric camera feed; finds off-the-shelf VLMs act too eagerly (emit next-tool-call before visually verifying preconditions), and RL fine-tuning is required to fix this — extending the tool-use-agent loop to long-horizon embodied execution.
  • [2026-06-27] Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond: Survey explicitly folds web/GUI/tool-use agents into a unified “world modeling” frame as the digital-world regime of an L1/L2/L3 capability hierarchy — frames tool-use environments (browsers, terminals, GUIs) as constraint regimes governed by program semantics, on the same footing as physical / social / scientific worlds. Provides vocabulary for distinguishing one-step tool-prediction (L1) from multi-step environment rollouts that respect program semantics (L2) from agents that revise their environment models from deployment failures (L3).
  • [2026-06-25] ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning: ZeroDex pushes the embodied tool-use-agent pattern further than VoLo by removing the VLA from the loop: the VLM directly indexes into an object-centric library of atomic actions (each storing a 6D tool trajectory), aligns the retrieved trajectory to the lifted 3D keypoints, and runs closed-loop status verification with replan over the resulting primitive sequence — zero training, no fine-tuning.
  • [2026-06-25] OpenThoughts-Agent: Data Recipes for Agentic Models: OpenThoughts-Agent ships a fully open SFT-data recipe targeting cross-benchmark agentic generalization over 7 agentic benchmarks rather than a single target — 100+ ablations isolate task source and diversity as load-bearing, Qwen3-32B fine-tune reaches 44.8% (+3.9 pp over Nemotron-Terminal-32B). Pairs with Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing as the SFT-curation counterpart to closed-loop tool-trajectory synthesis.
  • [2026-06-24] Qwen-AgentWorld: Language World Models for General Agents: Qwen-AgentWorld introduces a new design axis to the page: instead of training agents to act in environments, train an LLM (35B-A3B or 397B-A17B MoE) to be the environment — a language world model simulating browser/terminal/code-interpreter responses via long CoT, used as a decoupled simulator for downstream agentic RL with reported gains exceeding real-environment training. World-model training also acts as a warm-up that improves downstream tool-use performance across 7 agentic benchmarks. Token-space analog to LingBot-VA’s world-model-as-policy-internal-substrate pattern.
  • [2026-06-23] Seed2.1: A Next-Generation Agent for Real-World Productivity: Seed2.1 release reports “with tool” parenthetical numbers alongside standalone scores on MathVision (92.6→94.5), MMMU-Pro (81.6→82.7), ZEROBench (18.0→22.0), CharXiv-RQ (85.4→86.4) — tool-augmented inference treated as a first-class evaluation mode rather than a separate benchmark.
  • [2026-06-20] Playful Agentic Robot Learning: RATs extends the Code-as-Policy + persistent skill library pattern from CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation with a self-directed play stage that grows the skill library before downstream tasks arrive — same Berkeley/Goldberg authors, same LIBERO-PRO benchmark, but the skill library is now learned via task-proposer-driven exploration rather than extracted from successful downstream rollouts. The library transfers to other inference-time Code-as-Policy agents (+8.9 pp RoboSuite, +8.8 pp real-world) without finetuning, evidence that the toolset itself — not the agent — is the carrier of capability.
  • [2026-06-19] Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering: Treats a frozen VLA as a callable tool whose only input is language, and learns the calling policy (LFP) plus a conformalized abstention head — a tool-use loop where the tool is a robot policy and the safety property is “don’t make task success worse than the base instruction”, a cleaner formalization of the orchestrator-over-VLA pattern introduced by VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation.
  • [2026-06-16] Qwen-Robot Suite launch — Qwen-RobotManip, Qwen-RobotNav, Qwen-RobotWorld + RobotClaw framework (Alibaba Qwen): Qwen-RobotClaw framework lets general Qwen VLM agents invoke the three Qwen-Robot Suite models (Manip, Nav, World) as tools with explicit context/memory management for long-horizon embodied tasks.
  • [2026-06-15] VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation: VoLo introduces Physical Orchestration — a VLM orchestrator that runs a closed agent loop over interruptible robot tools (VLA/WAM, vision models, action primitives), distinguishing the embodied regime from text-agent ReAct loops by the fact that the world does not pause for deliberation, so tool calls must be steerable mid-rollout rather than blocking.
  • [2026-06-13] InterleaveThinker: Reinforcing Agentic Interleaved Generation: InterleaveThinker treats a frozen image generator as the action space for a planner+critic agentic loop with >25 generator calls per trajectory; step-wise reward design for long-horizon RL with expensive per-step environment calls.
  • [2026-06-03] LEAP: Supercharging LLMs for Formal Mathematics with Agentic Frameworks: LEAP applies the same iterative tool-use loop to formal theorem proving, with Lean as the tool — a frozen general-purpose foundation model decomposes proofs into sub-goals, drafts tactics, and refines against compiler feedback, reaching 12/12 on Putnam 2025 and 70% on Lean-IMO-Bench (vs 48% for a specialized gold-medal-caliber IMO formal system). Adds Lean compiler as the cleanest possible verifier-tool to the page’s tool taxonomy: total correctness signal, no observation ambiguity, and no trained verifier required (contrast DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoning).
  • [2026-05-25] Memory in the Age of AI Agents: Second comprehensive agent-memory survey filed (after Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey) — proposes a Forms × Functions × Dynamics taxonomy that adds Dynamics (formation / evolution / retrieval) as a first-class axis, explicitly distinguishing agent memory from RAG (a Forms-axis primitive), context engineering (provisioning, not memory), and LLM/parametric memory (the substrate, not the function). The companion paper list at https://github.com/Shichun-Liu/Agent-Memory-Paper-List is the canonical reading list for the agent-memory subfield as of late 2025.
  • [2026-05-22] Kimi K2.5: Visual Agentic Intelligence: Kimi K2.5 introduces Agent Swarm mode and the Parallel-Agent Reinforcement Learning (PARL) recipe — an open-source production-scale datapoint for learned parallel-agent orchestration in tool-use. The orchestrator decomposes tasks into parallelizable sub-tasks and dynamically instantiates specialized sub-agents (≤100 sub-agents × ≤1,500 tool calls), trained with shaped rewards defeating two newly named failure modes (serial collapse, spurious parallelism) and a Critical-Steps metric replacing total step count. Reported 3–4.5× Critical-Steps savings, up to 80% wall-clock reduction, and +3.5 absolute BrowseComp points over the single-agent context-managed inference baseline (74.9 → 78.4). Adds the parallel-execution axis to a design space that previously contained sequential interaction-depth (MiroThinker), sub-LM REPL orchestration (RLM), and context-space optimization (Training-Free GRPO).
  • [2026-05-22] VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents: First filed visual-tool-use entry. Same multi-turn (instruction, obs, action, feedback) history loop as the text-tool-use papers, with the toolset declared per-task as natural-language-described function calls (swap, rotate, move, gripper, etc.). Frontier models (Gemini 3 Pro, GPT-5, Claude 4, Qwen3-VL-235B) top out at 46.6% (Easy) / 26.0% (Hard) average across 17 long-horizon environments. Adds dimensions the text-agent papers don’t ablate: reversed-U over history length (~4 turns optimal, unbounded worse); ASCII-vs-image swap shows visual grounding (not reasoning) is GPT-5’s primary bottleneck; removing textual feedback degrades all models — visual-only feedback is insufficient. SFT recipe diverges from the GRPO consensus on this page: solver-generated demonstrations with information-revealing structure (exploratory moves that probe action-perception correspondence) substantially outperform goal-directed demonstrations under partial observability or unknown dynamics.
  • [2026-05-22] Training-Free Group Relative Policy Optimization: Adds the context-space pole to the design space — GRPO’s group-relative advantage primitive applied to a frozen LLM, with the “policy update” being an Add/Delete/Modify/Keep edit on a natural-language experience library. Matches/beats 32B GRPO-fine-tuned baselines (ReTool, AFM) at ~500× lower cost on AIME and WebWalkerQA. Preserves cross-domain generalization. Capability-gated: fails on QwQ-32B.
  • [2026-05-22] MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling: Proposes “interaction depth” as a third scaling axis alongside model size and context length; 256K context + recency-retention enables ~600 tool calls per task. Three-stage SFT → DPO + auxiliary-SFT-loss → online GRPO with streaming rollouts on synthesized multi-hop trajectories. 72B reaches 47.1 / 55.6 / 37.7 / 81.9 on BrowseComp / BrowseComp-ZH / HLE / GAIA-Text-Only.
  • [2026-05-22] Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing: InfTool closes a data→model→data loop — three role-playing agents synthesize tool-use trajectories from raw MCP API specs with no human labels; GRPO + gated rewards trains a 32B base to 70.9% BFCL (from 19.8%), claimed competitive with Claude-Opus.

Open questions

Section titled “Open questions”
  • Does the “interaction depth scales monotonically” claim (MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling) hold up once tool-call shortcuts (as in Training-Free Group Relative Policy Optimization‘s learned experiences) are accounted for? It is possible both papers describe the same accuracy/calls curve from opposite ends — MiroThinker by extending the budget, Training-Free GRPO by lowering the calls-per-task at fixed accuracy. VisGym’s reverse-U over history length (VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents §4.1) is a third orientation on the same family of curves. K2.5’s PARL is a fourth — it claims monotone Critical-Steps improvement under parallel execution, but does not measure how parallel and sequential interaction-depth interact (does PARL training collapse to fewer sequential calls per sub-agent, or are gains purely from concurrency?).
  • Where is the capability cliff for context-space methods? Training-Free Group Relative Policy Optimization shows DeepSeek-V3.1-Terminus benefits sharply, Qwen2.5-72B-Instruct marginally, QwQ-32B regresses — but the threshold is not characterized along model size, instruction-tuning quality, or native tool-use proficiency.
  • Self-generated trajectory data risks overfitting to its own synthesizer (Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing BFCL scores are within-distribution); no filed paper measures held-out transfer to non-BFCL tool distributions. VisGym (VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents §5.4) supplies an adjacent finding from the visual side — heuristic-solver demonstrations alone underperform if they’re not structured to reveal hidden state, suggesting the “synthesize trajectories, then SFT” pattern is brittle to demonstration structure on partial-observability tasks.
  • Does the “frozen base + swap-able context” recipe (Training-Free Group Relative Policy Optimization §4.1) compose? E.g. can math + web experience libraries be concatenated for tasks that span both, or do they interfere? No filed paper tests compositional or zero-experience regimes.
  • For the parameter-space camp, is the dominant gain coming from RL or from the trajectory-curation pipelines (MiroThinker’s filtering of noisy-correct + trivially-incorrect samples; InfTool’s verification step)? No filed paper ablates RL vs. SFT on the same curated trajectory pool. VisGym is SFT-only and shows large gains over no-fine-tune (VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents §5.1), which is a partial data point in favor of “curation + SFT is most of the win”.
  • For visual tool-use specifically: is the text-feedback-dependence finding (VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents §4.3) a transient artifact of current VLMs, or a structural property of the multi-turn-with-image-observation loop? If structural, environments that do not provide textual feedback (most real-world robotic settings) will need a different agent architecture entirely.
  • For parallel-agent orchestration: do the serial-collapse and spurious-parallelism failure modes (Kimi K2.5: Visual Agentic Intelligence §PARL) generalize beyond K2.5’s specific reward formulation, or are they artifacts of how the K2.5 reward weights the parallelism incentive? PARL is the only published recipe in this family — there is no comparison point yet.
  • For parallel-agent orchestration: is the Critical-Steps metric actually well-correlated with downstream wall-clock latency, or does it reward graph structures that don’t realize their parallelism gains at inference time (e.g. sub-agents that wait on shared resources)? K2.5 claims 4.5× Critical-Steps reduction translates to 4.5× wall-clock reduction in wide-search scenarios but doesn’t validate this in tasks with tighter cross-sub-agent coupling.
  • Which agent-memory taxonomy survives? Memory in the Age of AI Agents (Forms × Functions × Dynamics) and Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey (substrate × cognitive mechanism × subject) overlap heavily but disagree on which axes are first-class. Open whether the “Dynamics” axis (formation / evolution / retrieval) carves new territory or recapitulates the older “memory operations” sub-axis under a different name.

Papers

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