снова напиши все описание исправленное вставлю

Вот полностью обновлённое описание, уже с правками про minimum vs full funding и усиленным summary. Просто замени соответствующие блоки на Manifund.

***

Project summary

Current AI safety evaluation mostly relies on probabilistic, post-hoc behavioral sampling — it can tell that an agent behaved badly, but not where and why the failure was baked into its decision process. This project builds and validates a deterministic, provenance-aware trace analysis layer for AI agents, combining a structural trace protocol (LTP) with a causal memory graph (CML) to detect misalignment before catastrophic behavior in multi-agent systems. If successful, this gives the AI safety community a concrete, reusable way to catch dangerous failure modes earlier and more reliably than today’s benchmarks.

Concretely, I will implement LTP + CML on top of existing open-source agent frameworks, run them against adversarial tasks, and release: (1) an open benchmark corpus of agent traces with safety-relevant failure labels, and (2) a reusable oversight layer that exposes hallucination, provenance violations, and semantic drift directly in the trace. All code and benchmarks will be open-source so other safety researchers can build on top of this work.

***

What are this project's goals? How will you achieve them?

Goals

- Build and validate a deterministic oversight layer (LTP + CML) that flags safety-relevant failures in agent traces across multiple architectures.

- Release an open benchmark (LTP-Bench) that other researchers can use to evaluate oversight methods on the same adversarial fixtures.

- Produce a clear empirical answer to: “When does structural trace analysis meaningfully outperform pure behavioral sampling for safety evaluation?”

How I’ll achieve them

- Integrate LTP (trace protocol with approval/provenance checks and hallucination blocking) into at least 3 agent frameworks (ReAct-style, plan-and-execute, multi-agent orchestrators).

- Build CML, a causal memory graph that tracks cross-turn dependencies and detects drift and corruption in long-horizon tasks.

- Construct an adversarial fixture suite across coding, tool-use, and research-assistant domains, with independently labeled safety failures (deception, spec violation, dangerous tool calls).

- Compare LTP+CML signals to standard behavioral benchmarks and publish the results plus all code and data.

***

How will this funding be used?

I’m requesting $20,000 as a 3–4 month bridge grant:

- $12,000 — full-time stipend (3 months × $4,000) so I can focus fully on this project as an independent researcher.

- $5,000 — compute: LLM API credits and GPU time to run adversarial evaluations across multiple agent setups.

- $3,000 — infrastructure: data storage, annotation tooling, small contractor help for labeling/validation and documentation polish.

This is enough to ship a first public version of LTP + CML, a minimal but usable trace oversight library, and an accompanying benchmark corpus that other AI safety groups can reuse.

With the minimum funding ($10,000) I will focus on integrating LTP+CML into one agent framework and releasing a smaller adversarial fixture suite plus an initial version of the benchmark. With the full funding ($20,000) I can cover 3–4 months of work, integrate at least three architectures, significantly expand the adversarial corpus, and ship a more polished oversight library, documentation, and evaluation write-up.

***

Who is on your team? What's your track record on similar projects?

I’m working as a solo independent researcher, with a background of 12+ years in fintech QA and tooling and a recent pivot into AI safety. My previous work includes:

- Graph-based causal memory tooling for safe human–AI interaction — the basis for my LTFF grant application ($8,000 requested, under evaluation).[1]

- LTP/CML prototypes with adversarial security-oriented fixtures, focusing on structural oversight of agent traces rather than just behavior.

- Open-source safety/tooling repos on GitHub, including DAO_lim and related infrastructure projects used in my grant applications to NLNet NGI Zero (GardenLiminal, LiminalBD).

I’m comfortable building testing frameworks, adversarial fixtures, and production-grade tooling from my fintech background, and now apply that skillset directly to AI safety infrastructure.

***

What are the most likely causes and outcomes if this project fails?

Most likely failure modes

- LTP structural signals (provenance violations, hallucination flags, approval gate failures) do not significantly outperform standard behavioral benchmarks at predicting safety-relevant failures.

- The causal memory layer (CML) detects drift and corruption, but only in narrow settings and not robustly across architectures.

- I underestimate engineering complexity and only deliver partial integrations instead of a polished, reusable oversight layer.

Outcomes if it fails

- The field gets a clear negative result: empirical evidence that deterministic trace analysis alone is not enough for scalable oversight, and where it breaks down.

- The adversarial fixtures and trace corpus remain useful as public testbeds for other oversight methods (e.g. scalable oversight via learned evaluators or debate).

- I will still publish all code, benchmarks, and a write-up explaining what didn’t work, so others can avoid repeating the same dead ends.

***

How much money have you raised in the last 12 months, and from where?

In the last 12 months I have not yet received any grant payouts, but I have multiple applications under evaluation:

- Long-Term Future Fund (EA Funds) — $8,000 project grant requested for graph-based causal memory tooling (status: under evaluation).[1]

- Open Philanthropy / Coefficient — career development funding application submitted (status: under evaluation).

- OpenAI Safety Fellowship & Astra Fellowship — fellowship applications submitted for a 6-month full-time transition into AI safety research (status: under evaluation).[2]

- NLNet NGI Zero — €50,000 requested for Liminal Stack-related infrastructure; €30,000 NLNet Entrust application prepared for GardenLiminal and LiminalBD (status: one application submitted, one in preparation).[3]

So far, I am effectively unfunded and looking for a first concrete grant as an independent researcher to prove traction on this line of work.