Seshat Agent

Seshat is a self-evolving Recursive Self-Improvement (RSI) agent for software engineering, powered by MetaEvo — a self-evolving reasoning infrastructure that turns past successful trajectories into a reusable meta-knowledge base, then routes task-relevant meta-knowledge back into the model at inference time.

Rather than relying solely on static prompts or fixed workflows, Seshat continuously reflects on task outcomes, compresses useful experience into meta-knowledge, and re-enters the loop so capability compounds over time.

On SWE-Bench Pro, Seshat evolves a Kimi K3 base agent from 57.7% → 94.94% resolve rate. On DeepSWE-v1.1, Seshat evolves a GLM-5.3 base agent from 59.3% → 90.3% resolve rate.

MetaEvo: Meta-Knowledge for Task-Conditioned Enhancement

MetaEvo is a self-evolving reasoning infrastructure. It treats improvement as experience internalization, not external skill stacking. Its core idea: once meta-knowledge is abstracted from diverse reasoning trajectories, it can be recombined and reused on new problems — analogous to how humans transfer principles across domains (e.g., Newton → Coulomb, Darwin → genetic algorithms).

Meta-knowledge base

The meta-knowledge base stores a large set of meta-knowledge vectors extracted and purified from successful reasoning traces. Each unit is not raw text memory, but a compact, reusable reasoning primitive, for example:

• problem decomposition
• constraint propagation
• reverse / goal-directed reasoning
• contradiction elimination
• self-verification
• tool calling patterns
• structural analogy transfer
• reasoning-order / adaptive strategy switching

Offline, MetaEvo continually mines new vectors from successful trajectories and updates the base — so the agent’s experience becomes a growing prior for future tasks.

Task-conditioned capability enhancement

At inference time, MetaEvo does not dump the whole knowledge base into the prompt. Instead it builds a customized cognitive path for the current task:

1. Problem in — receive the current instance (e.g., a SWE-Bench Pro / DeepSWE bug).
2. Meta-knowledge router — match / select / compose the most relevant meta-knowledge units.
3. Cognitive path — arrange selected units into a structured reasoning plan.
4. Inject & reason — inject the path into the neural forward chain (layer-wise knowledge injection) so the base model reasons with task-specific priors.
5. Meta-critic — monitor intermediate states; continue, re-route, or re-plan when needed.
6. Evolve — successful traces feed back into the meta-knowledge base for the next round.

In short: current task → retrieve & compose meta-knowledge → enhance the base model’s reasoning on that task → deposit new meta-knowledge. Seshat is the agent that runs this RSI loop on software-engineering workloads (and transfers the same stack to coding / logic / science benches).

Key Result

Absolute gain (SWE-Bench Pro): +37.24 percentage points.
Absolute gain (DeepSWE-v1.1): +31.0 percentage points.

Evaluation evidence (patches, per-instance results, aggregated scores):

• SWE-Bench Pro: PCL-RSI-lab/SWE-Bench-Pro-Evaluation-Evidence
• DeepSWE-v1.1: PCL-RSI-lab/DeepSWE-v1.1-Evaluation-Evidence

Evolution Curve

The figure below shows Seshat’s RSI trajectory on SWE-Bench Pro: starting from the Kimi K3 zero-shot baseline and climbing through successive self-improvement rounds, eventually surpassing strong frontier agents on the same axis.

Seshat recursive self-improvement trajectory (Kimi K3 base). The curve rises from the zero-shot baseline toward the final evolved performance; dashed lines mark reference model scores.

Multi-Benchmark Performance

Beyond SWE-Bench Pro, Seshat (shown as MetaEvo in the figure) transfers RSI gains across coding, reasoning, science, and IP-matching benchmarks — typically ranking #1 among the compared systems.

Seshat scores (from the figure)

Selected comparisons

Intended Use

• Research on self-evolving / RSI agents for coding and reasoning
• Strong SWE-Bench / SWE-Bench Pro / DeepSWE style repository repair agents
• Studying how experience distillation transfers across coding, logic, science, and related tasks

Citation

If you use Seshat or the evaluation evidence, please cite this model repository and the accompanying datasets:

@misc{seshat-agent,
  title        = {Seshat: A Self-Evolving RSI Agent for Software Engineering},
  author       = {PCL-RSI Lab},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/PCL-RSI-lab/Seshat-agent}}
}

Links

• SWE-Bench Pro evaluation evidence: PCL-RSI-lab/SWE-Bench-Pro-Evaluation-Evidence
• DeepSWE-v1.1 evaluation evidence: PCL-RSI-lab/DeepSWE-v1.1-Evaluation-Evidence
• Organization: PCL-RSI-lab

Contact

seshat.ai2026@gmail.com