DeepAgent: A Deep Reasoning AI Agent that Performs Autonomous Considering, Device Discovery, and Motion Execution inside a Single Reasoning Course of

Most agent frameworks nonetheless run a predefined Motive, Act, Observe loop, so the agent can solely use the instruments which are injected within the immediate. This works for small duties, nevertheless it fails when the toolset is massive, when the duty is lengthy, and when the agent should change technique in the midst of reasoning. The workforce from Renmin College of China and Xiaohongshu proposes DeepAgent as an finish to finish deep reasoning agent that retains all of this inside one coherent reasoning course of.

Unified Reasoning With On Demand Device Discovery

DeepAgent lets the mannequin output 4 motion varieties immediately in textual content, inside thought, device search, device name, and reminiscence fold. When the agent decides to look, it queries a dense index that accommodates device descriptions from massive registries, for instance 16,000 plus RapidAPI instruments and three,912 ToolHop instruments, then it receives solely the highest ranked instruments again in context. This makes device entry dynamic, the mannequin doesn’t rely upon a entrance loaded device record, and it stays aligned with actual environments the place instruments change.

Autonomous Reminiscence Folding for Lengthy Horizon Duties

Lengthy sequences of device calls, internet outcomes, and code responses will overflow the context. DeepAgent solves this with an autonomous reminiscence folding step. When the mannequin emits the fold token, an auxiliary LLM compresses the total historical past into three reminiscences, Episodic Reminiscence that information process occasions, Working Reminiscence that information the present sub objective and up to date points, and Device Reminiscence that information device names, arguments, and outcomes. These reminiscences are fed again as structured textual content, so the agent continues from a compact however info wealthy state.

ToolPO, Reinforcement Studying for Device Use

Supervised traces don’t educate strong device use, as a result of appropriate device calls are just a few tokens inside a protracted technology. The analysis workforce introduce Device Coverage Optimization, ToolPO, to repair this. ToolPO runs rollouts on LLM simulated APIs, so coaching is steady and low-cost, then it attributes reward to the precise device name tokens, that is device name benefit attribution, and it trains with a clipped PPO model goal. That is how the agent learns not solely to name instruments, but in addition to determine when to look and when to fold reminiscence.

Benchmarks, Labeled Instruments vs Open Set Instruments

The analysis workforce evaluates on 5 basic device use benchmarks, ToolBench, API Financial institution, TMDB, Spotify, ToolHop, and on 4 downstream duties, ALFWorld, WebShop, GAIA, HLE. Within the labeled device setting, the place each technique is given the precise instruments it wants, DeepAgent 32B RL with a QwQ 32B spine reviews 69.0 on ToolBench, 75.3 on API Financial institution, 89.0 on TMDB, 75.4 on Spotify, and 51.3 on ToolHop, which is the strongest 32B degree outcome throughout all 5 datasets. Workflow baselines resembling ReAct and CodeAct can match single datasets, for instance ReAct with robust fashions is excessive on TMDB and Spotify, however none of them keep excessive on all 5, so the truthful abstract is that DeepAgent is extra uniform, not that others are at all times low.

Within the open set retrieval setting, which is the real looking one, DeepAgent should first discover instruments after which name them. Right here DeepAgent 32B RL reaches 64.0 on ToolBench and 40.6 on ToolHop, whereas the strongest workflow baselines attain 55.0 on ToolBench and 36.2 on ToolHop, so the tip to finish agent nonetheless holds the lead. The analysis workforce additionally exhibits that autonomous device retrieval itself lifts workflow brokers, however DeepAgent beneficial properties extra, which confirms that the structure and the coaching are matched to massive toolsets.

Downstream Environments

On ALFWorld, WebShop, GAIA, and HLE, all below a 32B reasoning mannequin, DeepAgent reviews 91.8 % success on ALFWorld, 34.4 % success and 56.3 rating on WebShop, 53.3 on GAIA, and the next rating than workflow brokers on HLE. These duties are longer and noisier, so the mix of reminiscence folding and ToolPO is the seemingly supply of the hole.

Key Takeaways

1. DeepAgent retains the entire agent loop inside one reasoning stream, the mannequin can suppose, search instruments, name them, and proceed, so it isn’t restricted to a hard and fast ReAct model workflow.
2. It makes use of dense retrieval over massive device registries, 16,000 plus RapidAPI instruments and about 3,900 ToolHop instruments, so instruments should not have to be pre listed within the immediate, they’re found on demand.
3. The autonomous reminiscence folding module compresses lengthy interplay histories into episodic, working, and gear reminiscences, which prevents context overflow and retains lengthy horizon reasoning steady.
4. Device Coverage Optimization, ToolPO, trains device use finish to finish with simulated APIs and token degree benefit attribution, so the agent learns to subject appropriate device calls, not solely to achieve the ultimate reply.
5. On 5 device benchmarks and 4 downstream duties, DeepAgent at 32B scale is extra constant than workflow baselines in each labeled device and open set settings, particularly on ToolBench and ToolHop the place device discovery issues most.

DeepAgent is a sensible step towards agent architectures that don’t rely upon mounted device prompts, as a result of it unifies autonomous considering, dense device retrieval over 16,000 plus RapidAPIs and three,900 plus ToolHop instruments, structured device calling, and reminiscence folding in a single loop. Using LLM simulated APIs in ToolPO is an engineering selection, nevertheless it solves the latency and instability drawback that hurts prior device brokers. The analysis exhibits constant 32B degree beneficial properties in each labeled device and open set settings, not remoted peaks. This launch makes massive toolspaces truly usable for LLM brokers. Total, DeepAgent confirms that finish to finish device brokers with reminiscence and RL are rising because the default sample.

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