S1CAP

System-1 Context-Aware Planning

S1CAP: Context-Aware Planning via System-1 Models for Efficient LLM Agents

Authors: Yuanming Chen · LI Changzhe

Progress: M0 complete (packages, Laya runtime verified on this machine, the plugin activates in a real DSH profile). M1 observation mode is live and verified in real sessions: the per-call pipeline runs (SEGMENTER → RECALL → ASSEMBLER) with the prompt returned untouched, the system prompt is sourced from the harness registry so the pinned block and the cache-stable prefix are non-zero (blocks.pinned = 684 in a real round), association-graph upkeep is fed by real session/event traffic, and a replay harness reproduces the control-plane records byte for byte. The settings panel that will hold the Jev key is next. Details, evidence and per-item acceptance tests: docs/STATUS.md.

S1CAP puts a cheap System-1 decision model (Jev / Laya / Kev class, speaking the /v1/systemone protocol) in charge of an LLM agent harness's context lifecycle — instead of the expensive System-2 LLM. The S1CAP control layer intervenes at exactly two points:

1. Context Awareness (what the model sees, per LLM call) — every session segment (user turn, assistant message, reasoning trace, tool call/result) is a node in a growing association graph scored by the System-1 model. Each turn, bounded BFS + relevance threshold + token budget decide which segments make it in, assembled in Trace-as-State order: [pinned prefix | state proxy T | recalled blocks | recent tail | current input].
2. Plan Ordering (the context-aware part of planning) — the LLM's candidate plans go directly to a second System-1 backend that scores them as a choice question; PLAN GATE normalizes those scores, orders the plans and caps attempts, and execution follows that order under a verification oracle, with unexecuted alternatives discarded on first success.

Everything is measured, not assumed: solve rate, token cost split by prompt-cache hit/miss (the dominant cost lever — cache-hit tokens are ~50× cheaper than misses on DeepSeek), and wall time excluding approval waits.

Why now (September 2026)

enabler	fact
Trace as State (arXiv:2609.02702)	placing the reasoning-trace state proxy before the long context beats trace-append in 26/27 model×task×metric combos — training-free, inference-time only
Decision models arrive	Jev (TypeSafe AI): $0.042/M input, output free, parallel question batches 12× cheaper than serial · open Laya (Apache-2.0) · EdgeJev local runtime: 322M INT8, 324 MB, 15.6 ms/decision on 4 vCPU
Cache economics	DeepSeek deepseek-flash: $0.006/M cache-hit vs $0.30/M cache-miss — context assembly is a cost decision, not just a quality decision

Architecture

For exact control flow and asynchronous boundaries, see the detailed technical route (dark version).

The user-facing transcript stays strictly chronological; only the model view is reassembled (native in DSH's session/surface split, replicated by the portable proxy elsewhere).

Evaluation design (pre-registered)

2×2 within-task paired factorial — factor A: Trace-as-State ordering; factor B: S1 governance (selection + plan gate):

Cell	A	B
C1 baseline	off	off (native compaction only)
C2	on	off
C3	off	on
C4 full	on	on

Benchmarks (all automated scoring, no GUI, no LLM judges): SWE-bench Verified (100/cell) · Terminal-Bench 4.0 (66/cell) · τ²-bench (full base split). Model: deepseek-flash (DeepSeek-V4.1-Flash), temperature 0.

Success rule: solve-rate non-inferiority vs C1 (paired McNemar, one-sided α=0.05, margin −2 pp) AND ≥10% improvement in cost/task or time/task (paired bootstrap 95% CI excluding 0, Holm-corrected). Winning cost while losing >2 pp solve rate is not a win.

Status & roadmap

Pre-alpha — M0 scaffolding landed: monorepo, @s1cap/core (segmenter · association graph · assembler · plan gate · telemetry v1), @s1cap/s1-client, @s1cap/laya-runtime (Python discovery + laya-serve launcher), dsh-s1cap skeleton, 2×2 cell presets; node --test 34/34 offline, and a real laya-serve round trip verified (/health readiness + a noul decision over /v1/systemone). Remaining M0: live-backend smoke against Jev. Full spec: docs/AGENT_BRIEF.md §10.

M	Scope
M0	monorepo scaffold, core + s1-client, telemetry v1, DSH plugin skeleton — scaffold done, live-backend smoke pending
M1	assembler/recall replay-correctness tests, proxy MVP, DSH hook wiring (agent/pre-step, surface ops)
M2	plan gate wiring, degradation paths, settings UI, Terminal-Bench 10-task cost pilot
M3	full 2×2 on SWE-bench Verified + τ²-bench (+ TB), optional Laya fine-tune
M4	Terminal-Bench cells, opencode transfer check, GLM model-swap check
M5	paper: Pareto + cache-waterfall figures, case studies, LaTeX draft

Development

node --test --experimental-strip-types "packages/*/test/*.test.ts"   # 51 tests, zero deps, offline
node scripts/check-diagram.mjs   # every node box inside its lane band, no overlapping nodes

The second command guards the hand-authored route diagram: it is drawn by hand, so nothing but this check stops a node from drifting out of its lane.

Local Laya backend: @s1cap/laya-runtime discovers the Python environment that can import laya (conda environments are resolved through conda env list --json, never by guessing paths), launches laya-serve and health-checks /v1/models. Install the serving extra once per environment with <python> -m pip install "laya[serve]"; configuration keys and the DSH profile patch are documented in docs/LAYA_RUNTIME.md.

Type-checking needs TypeScript ≥ 5.8 (erasableSyntaxOnly): npm i -D typescript@^5.8 && npm run typecheck. pnpm is the intended workspace manager; on Windows PowerShell call pnpm.cmd (the .ps1 shim is blocked by the default execution policy).

Repository layout

s1cap/
  packages/core        # segmenter, association graph, assembler, plan gate, telemetry v1
  packages/s1-client   # /v1/systemone client + provider matrix
  packages/proxy       # OpenAI-compatible middleware (M1)
  packages/dsh-plugin  # dsh-s1cap: first-class DeepSeek Harness plugin (skeleton)
  bench/               # cells/ presets; runners + stats land with M2
  docs/                # proposal, implementation brief, related-work dossier, formulas
  paper/               # LaTeX (M5)

Documentation

doc	audience
docs/STATUS.md	start here — done/next checklist plus agent-ready detail for every open item
docs/PROPOSAL.md	research proposal — supervisor / cooperator
docs/ARCHITECTURE.md	module reference: route SVG + connection semantics, parameters, implementation status
docs/LAYA_RUNTIME.md	local Laya backend: Python environment discovery, launcher, configuration keys
docs/CONTROL_PLANE_LOGGING.md	control-plane isolation: two-log design and the invariants that keep System-1 from scoring its own output
docs/AGENT_BRIEF.md	implementation brief for coding agents — verified facts base, interfaces, algorithms, milestones
docs/FORMULAS.md	formal definitions and formula handbook (Markdown + LaTeX)
docs/RELATED_WORK.md	verified related-work dossier + novelty audit
docs/REPO_METADATA.md	canonical repo description, topics, keywords

Environment

Verified on the dev machine (2026-09-28): Node v22.23.1 · npm 12.0.2 · git 2.45.2 · Python 3.13.13 (miniforge) · i7-12700H (AVX2 — EdgeJev-compatible) · DSH runtime bundles Node 24.18.1.

Requirements: Node ≥ 22.19 (DSH plugin engines contract) · pnpm for the monorepo (on Windows PowerShell, invoke pnpm.cmd or relax the execution policy) · Python ≥ 3.10 for local S1 runtimes (pip install "laya[serve]", pip install edgejev) · a System-1 backend: cloud Jev key, or local EdgeJev/Laya for weak CPUs (324 MB, offline).

Name

S1CAP = System-1 Context-Aware Planning — S1 is the System-1 decision model, C-A-P is the context-aware planning it performs for an LLM agent. The control layer intervenes at two points: ① Context Awareness (which segments the model sees) and ② Plan Ordering (the order its own plans run in).

Acknowledgments

TypeSafe AI (Jev) · Convai Innovations (Laya) · yzfly (EdgeJev) · jaredpalmer (Kev) · Benchmark Heaven (JevBench) · Xu Zou & Jie Tang (Trace as State, arXiv:2609.02702) · snailium (DSH plugin engineering template) · DeepSeek Harness

Citation

Paper in preparation. Authors: Yuanming Chen, LI Changzhe. Title: S1CAP: Context-Aware Planning via System-1 Models for Efficient LLM Agents (revised 2026-09-28 after the naming erratum: the acronym expands to System-1 Context-Aware Planning).

@misc{s1cap2026,
  title  = {S1CAP: Context-Aware Planning via System-1 Models for Efficient LLM Agents},
  author = {Chen, Yuanming and LI, Changzhe},
  year   = {2026},
  url    = {https://github.com/Jaffe2718/s1cap},
  note   = {Paper in preparation}
}

License

TBD (MIT proposed).