PCL-RSI-lab/Seshat-agent

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

Setting	Base model	Benchmark	Score
Zero-shot baseline	Kimi K3	SWE-Bench Pro	57.7%
After RSI evolution (Seshat)	Kimi K3 + Seshat	SWE-Bench Pro	94.94% (694 / 731)
Zero-shot baseline	GLM-5.3	DeepSWE-v1.1	59.3% (67 / 113)
After RSI evolution (Seshat)	GLM-5.3 + Seshat	DeepSWE-v1.1	90.3% (102 / 113)

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)

Benchmark	Seshat	Notes
SWE-Bench Pro	95.1	Evolved from Kimi K3 (57.0); official submit evidence: 94.94% (694/731)
DeepSWE-v1.1	90.3	Evolved from GLM-5.3 zero-shot 59.3% (67/113) → 90.3% (102/113); evidence dataset linked above
CyberGym	80.3	MetaEvo on DeepSeek V4-Pro 4-Bit (base 41.2 → 80.3)
HumanEval	100.0	#1
StrategyQA	82.3	#1
LogicBench	86.4	#1
LiveCodeBench	49.6	#1
ScienceQA	98.0	#1
SciCode	21.2	#1
PatentMatch	76.6	#1

Selected comparisons

Benchmark	Seshat	Strongest listed baseline	Gap
SWE-Bench Pro	95.1	Claude Opus 5.5 (89.9)	+5.2
CyberGym	80.3	Claude Mythos (83.1) / GPT-5.5 (81.8)	−2.8 / −1.5
HumanEval	100.0	Claude Sonnet 5 (98.8)	+1.2
StrategyQA	82.3	Hy3 (70.1)	+12.2
LogicBench	86.4	Gemma 3 27B (84.0)	+2.4
LiveCodeBench	49.6	Doubao Seed 2.0 (41.6)	+8.0
ScienceQA	98.0	Doubao Seed 2.0 (97.3)	+0.7
SciCode	21.2	Claude Sonnet 5 (18.8)	+2.4
PatentMatch	76.6	Qwen3.5 27B (74.1)	+2.5

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