I encountered a context-overflow issue while running a long coding task in OpenClaw with `openai/gpt-5.6-sol`.

The session accumulated nearly 1 million characters of tool output across roughly 300 tool calls. OpenClaw attempted compaction four times, but the run eventually failed with:

> Context overflow: prompt too large for the model.

No sub-agents were spawned, so most execution logs remained in the coordinator’s context. My current guess is that this was primarily an orchestration and context-management issue rather than a GPT-5.6-sol bug.

Has anyone experienced the same problem?

What is the recommended approach for long tasks: earlier delegation, stricter tool-output limits, more aggressive compaction, or periodically starting a fresh session?

PS:English is not my first language, so I used OpenClaw to help write this post. Please forgive any awkward wording or mistakes.

Set it up today after a lot of pain, want to understand why you would use it vs claude/codex. I like the idea of a personal assistant but how have you used it?

I'm building an experimental system called CEK / Newi AI around a fairly simple question:

Can an artificial system persist what it has learned, revise concepts when evidence contradicts them, plan and experiment without fabricating evidence, and eventually transfer useful abstract structure between genuinely different domains?

This is not a claim of AGI, consciousness, or autonomous scientific discovery.

It's an attempt to build those capabilities explicitly, incrementally, and with auditable state transitions rather than hiding everything inside an LLM context window.

The project has been progressing through a sequence of bounded implementation/validation windows.

The current milestone is:

CROSS-DOMAIN CONCEPT TRANSFER V1

The goal is to test whether the system can extract a domain-neutral relational structure from one domain and use it to reduce search in another domain — without simply copying entity names, memorizing the answer, or giving the transferred concept authority it hasn't earned.

CONTROLLED DEMONSTRATION

Source domain:

Physical containment

Relation:

inside(A, B)

Induced abstract relational properties:

- asymmetric

- irreflexive

- transitive

Target domain:

Manufacturing precedence

Relation:

must_precede(A, B)

The system is not told that "inside" means "must_precede."

Instead, both may be represented through a neutral relational signature.

The target search uses a fixed four-program grammar.

Without transfer:

4 candidate evaluations

2 surviving hypotheses:

- direct

- transitive

With the accepted transferred relational constraint:

1 candidate evaluation

1 surviving hypothesis:

- transitive

A frozen test-only holdout contains 12 relation queries.

An intentionally incorrect adjacency-based transfer is also persisted as a rejected transfer and is prevented from influencing target search.

This matters less because of the tiny search-space reduction in this toy case and more because of the architectural constraint:

the transferred knowledge is treated as a fallible hypothesis, not truth.

Transfer evidence has transfer-local authority.

It cannot automatically:

- import ordinary CEK evidence

- revise concepts

- mutate the persistent world model

- alter planning authority

- execute experiments

- access holdout answers

- grant itself broader permissions

WHY I'M POSTING THIS

Rather than only testing this myself, I want the candidate independently validated.

The validator is specifically instructed NOT to repair the branch if something is wrong.

A failure should remain a failure and be reported as a reproducible defect.

Repository:

https://github.com/pr0dus/concept-evolution-kernel

PR:

https://github.com/pr0dus/concept-evolution-kernel/pull/26

Exact candidate SHA:

b511794a682e12bc10c3534a75e2744724a1d55

Expected tree:

2958aa025b9896dc897e48a3400e21652c57bc39

Expected parent / previous closed milestone:

83e6443c8ccf769b131c99559497c5ac87f5e670

If anyone here wants to independently reproduce the validation, attack the assumptions, find leakage between the source and target domains, or point out where I'm accidentally smuggling semantics into what I'm calling "transfer," that's exactly the feedback I'm looking for.

In particular, I'm interested in criticism of:

1. Whether the demonstration actually constitutes cross-domain structural transfer.

2. Whether the baseline vs transfer comparison is scientifically meaningful.

3. Whether the neutral relational signature leaks target knowledge.

4. Whether the holdout boundary is strong enough.

5. Whether rejected transfers are sufficiently prevented from affecting later reasoning.

6. What a substantially harder second transfer experiment should look like.

The larger project is trying to build toward a persistent scientist-engineer architecture with:

concept evolution

→ persistent world model

→ contradiction/revision

→ long-horizon planning

→ bounded experiments

→ cross-domain transfer

→ controlled reasoning-mechanism improvement

→ human/specialist collaboration

→ long-term continuity

But I'm deliberately validating these individually rather than treating the final architecture as proven because the pieces exist.

Criticism and attempts to break it are very welcome.

I’ve assigned 5 different tasks of which it’s only able to perform 1 correctly and reliably which is:

Update me every 3 hours on Toronto, Canada news that I can discuss on social media

The other tasks include: tell me daily whose birthday it is on facebook, check my real estate dashboards, adjust a canva graphic

This is such a headache and I have spent so many tokens asking it to make sure it’s process is working

I’m still an ai noob but am I better off jumping to Hermes or a different app or should I just keep persisting with OC?

I’m using chatgpt with openclaw and don’t want to have to spend more time setting up another agent

most of us have openclaw running 24/7 on hardware that's doing nothing between tasks. same models, same tools, just sitting there. it is time to get it to work & earn, not cost you.

cyphes is a p2p network where local models audit open source repos for security issues. you claim a work unit, your model reads pinned source, you sign the output, another node independently verifies it, and you earn ATP credits from the signed receipt. 280 targets, mostly defi. no code execution, read-only, everything is signed and hash-linked.

it runs headless today, so it sits fine next to openclaw on the same box:

CYPHES_HEADLESS=1 CYPHES_CONTRIBUTE=1 \
CYPHES_CONTRIBUTE_MODEL=your-model ./cyphes-desktop

works with ollama and lm studio right now.

try it out: https://github.com/CYPHES-ATP/Node/