Inside ZERO1 Core / Shared memory

A company of agents.
One shared understanding.

Your processes, decisions, and know-how belong at the center of the swarm. Evidence Memory is the shared-context layer we are building into Core—so each specialist can work from the same approved understanding.

Start with the evidence

Research behind the approach

A useful memory.
A well-chosen team.

Reusable workflows, retrieval design, and coordination each matter. These studies help us ask better questions—not promise universal gains.

Not ZERO1 performance. External benchmark findings, not customer outcomes or business ROI.

01 / Reuse

ICML · 2025 · Peer-reviewed

Build on a learned workflow.

WebArena · task success
BrowserGym reference23.5%
Agent Workflow Memory35.5%

12 percentage points between the reported methods.

Reported methods, not an isolated memory-only effect or business ROI.

Study details & limits

WebArena · GPT-4 · Table 1

AWM reuses routines from earlier runs. The paper also reports a 15.0% accessibility-tree-only BrowserGym variant; the 23.5% reference uses a different observation setup.

02 / Remember

ICLR · 2025 · Peer-reviewed

Make the right context findable.

LongMemEval-M · answer accuracy
Original-text keys67%
Text + extracted-fact keys72%

5 percentage points with expanded indexing keys.

One retrieval configuration, not a universal memory improvement.

Study details & limits

LongMemEval-M · GPT-4o · round values · top-10 retrieval · Table 3

Extracted user facts are added to the indexing keys. Original records remain available in both conditions. This is a retrieval-design comparison, not memory versus no memory.

03 / Coordinate

Preprint · 2025 · v2

Fit the team to the task.

Coordination helped on Finance Agent and harmed performance across multi-agent variants on PlanCraft.

Task-dependent findings, not evidence that more agents always help.

Parallel investigationSeparate threads. Combined findings.
Sequential executionEach step depends on the last.

Diagrams: our design interpretation, not a live agent run.

Study details & limits

180 configurations · four benchmarks · five architectures · three model families

The study compares architectures under standardized tool and token budgets. Splitting sequential work can add overhead. Our design inference is to choose the coordination pattern after understanding the task.

What this means at work.

Supporting context—not tests of persistent memory or agent teams.

01 / The frictionSurvey · 2023
62%[1]

Too much time looking
for the information.

62 of 100 dots are highlighted to represent 62 percent, not 100 individual survey participants.

Reported by respondents in Microsoft’s global worker survey.
31,000 workers
31 markets

Self-reported search burden—not 62% of the working day.
02 / The opportunityField study · 2025
+15%[2]

More issues resolved
per hour, on average.

5,172 support agents. Effects varied by experience and skill. AI assistance—not a memory-only or swarm study.

Our design responseGive agents the right company context. Then measure the result in the workflow where it matters.

What memory has to get right

Finding it isn’t enough.
Knowing how to use it matters.

Two recent benchmarks inform our design choices. These are controlled research settings, not production guarantees.

Routing ≠ answeringPreprint · 2026

The right source can still
lead to the wrong answer.[3]

1,151 tasks · four model backbones · gold-constrained tool access. Different metrics on a 0–100 scale; ranges are not paired results.

Design responseKeep original evidence attached to the answer.

Memory ≠ permissionPreprint · 2026

A stored statement must not
become a new instruction.[4]

Authority collapse observed in 48 of 49 evaluated configurations: 48 blue squares and one unfilled square.

48/49tested configurations showed
authority collapse.

Seven consolidators × seven model backbones. Benchmark configurations, not the share of real-world companies affected.

Design responseRemember who said it—and what they can authorize.

The memory inside the swarm

Shared context.
Traceable all the way through.

Evidence Memory is part of Core’s Company Model. Follow one piece of information from its source to a result your team can inspect.

Architecture illustration / Choose a stage

01 / Source

Keep the original.

Start with approved records, policies, and prior work. Keep the source attached.

Proposed architecture, not a live data feed.

Source → permissions → retrieval → evidence → human review.

Illustrative policy record

Remember the rule.
And its boundaries.

A new policy should supersede the old one without erasing how earlier decisions were made.

Approval policyVersion 2 / current
Source
Approved operations policy
Owner
Operations lead
Valid from
Its recorded effective date
Replaces
Version 1 Kept for history
Allowed use
Prepare a draft—not authorize its release

Example schema. No customer records shown.

Learning, with your team

Adapt to your processes.
Keep your people in control.

The learning loop we are building turns reviewed corrections into testable changes. Agents do not silently rewrite company rules.

  1. 01 / ReviewCorrect the work.

    Your team marks what should change.

  2. 02 / ProposeCapture the lesson.

    Attach the correction to its source.

  3. 03 / ReplayTest it on prior work.

    Check for gains and regressions.

  4. 04 / ApproveA person promotes it.

    Release a version that can be traced.

A little more detail.

Open only what you need.

How does this differ from a bigger prompt or a vector database?

These are complementary tools, not mutually exclusive choices. Core’s design selects the retrieval method for the question.

Long context
Read a bounded working set in detail.
Semantic retrieval
Find candidates with similar meaning.
Exact queries
Resolve identifiers, quantities, and structured facts.
Graph / file / live retrieval
Follow relationships, open original records, or check current state.
Evidence Memory
Keep source, time, access, and authority attached across those routes.
What do the research figures prove—and what don’t they?

The workflow and memory comparisons test particular methods. The coordination study finds gains and losses depending on the task. The survey and field study add workplace context. None evaluates ZERO1 Core.

The new percentage charts start at zero and end at 100. Differences are percentage points, not relative gains. The support-productivity bars use a derived index. Route F1 and answer accuracy are distinct measures. Authority-collapse squares represent tested configurations, not customers.

Our architecture responses are design inferences, not conclusions the researchers tested about our product.

What is implemented, and how will Core be evaluated?

The first working artifact is a synthetic RFQ-to-quote workflow. Founder-led diagnosis is current; live connectors, agent-led diagnosis, and governed production improvement are build work, scoped to each engagement.

The planned evaluation compares fixed tasks, model and tool budgets, source correctness, answer quality, permissions, latency, and cost. A sealed evaluation set and the comparative run are not complete. We are not publishing a ZERO1 benchmark score or a customer ROI claim.

See the first workflow

Sources, without the guesswork.

Checked September 4, 2026

  1. [1]
    Microsoft — Will AI Fix Work?

    2023 Work Trend Index. Vendor-funded survey; fieldwork February–March 2023. The 62% figure is respondents reporting search burden, not measured hours lost.

    ↑
  2. [2]
    Brynjolfsson, Li & Raymond — Generative AI at Work

    The Quarterly Journal of Economics, 140(2), 889–942 (2025). Peer-reviewed study. Reported average effects differ across worker groups.

    ↑
  3. [3]
    WorkSurface-Bench — Enterprise knowledge routing

    arXiv:2607.25765v1, July 2026. Preprint. Displayed ranges apply to gold-constrained access across tested model backbones.

    ↑
  4. [4]
    AuthMem-Bench — When Memory Becomes Authority

    arXiv:2608.01679v2, August 2026. Preprint. Authority collapse means a stored claim loses constraints and acquires unwarranted instructional authority.

    ↑
  5. [5]
    Agent Workflow Memory

    ICML 2025. Table 1; WebArena comparison.

    ↑
  6. [6]
    LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

    ICLR 2025. Table 3; specified GPT-4o retrieval configuration.

    ↑
  7. [7]
    Towards a Science of Scaling Agent Systems

    Preprint, December 17, 2025 version (v2). Section 4.2; task-dependent coordination results.

    ↑

ZERO1 IO / Your agents. Your flow.

Start with the work
your team knows best.

Define your first result