The Eternis Forecasting Agenda

Verdict. Build one shared Eternis Forecast Ledger first, then evaluate a boundary-driven Curiosity Engine by realized predictive information gain, decision value, learning progress, diversity, cost, and resistance to noise traps.

Full published report: https://eternis-forecasting-agenda.us-east-1.gists.org

Internal working dossier · Eternis · 20 July 2026

The full report

The Eternis forecasting agenda

One shared forecasting organism for Axion, Lume, public artifacts, and the trading system.

The conclusion

Eternis should build one shared forecasting organism and expose it through four different tests:

The shared core is a living, versioned distribution over the world. It ingests events, maintains forecasts at several horizons, records why probabilities changed, resolves outcomes, and learns which questions, sources, models, and decision rules were useful. The products should differ by interface and payoff, not by maintaining separate beliefs.

The near-term goal is not “forecast anything” as a slogan. It is to become measurably excellent across a ladder of increasingly hard forecast objects, while making the system cheap enough to keep thousands of beliefs alive every day. Generality should be earned by one typed interface, broad live evaluation, and repeated transfer across domains.

What forecasting is for

The deepest goal is better allocation under uncertainty. A forecast earns its place when it changes a decision: where to invest, what to build, which experiment to run, what to monitor, how much risk to take, or when to wait.

A useful forecasting system produces three distinct states:

1. Belief state: what seems true now, with provenance and uncertainty.
2. Forecast state: a distribution over future states and paths.
3. Decision state: the action with the best expected utility under the

user's constraints.

Keeping these separate prevents several common errors. A compelling story is not a probability. A probability is not a position. A correct forecast can still lead to a bad trade. A wrong outcome can still have been a good decision under the information available at the time.

The north-star quantity is therefore not raw accuracy:

Decision value = calibrated discrimination × update quality × legibility × actionability, divided by time and cost.

The epistemic contract

“Ground every statement in its epistemic truth” should be a product invariant, not a writing preference. Epistemic truth is not one confidence number. The interface should expose what kind of statement is being made, what supports it, and what could overturn it.

Every important sentence or graph edge should compile to an EpistemicClaim with one of these types:

The user-facing truth surface should answer six questions without opening an agent trace:

1. What exactly is being claimed?
2. Why should I believe it, and as of what information cutoff?
3. How uncertain is it, and is the uncertainty reducible or irreducible?
4. What would change the system's mind?
5. Which decision depends on it?
6. What remains unknown or outside the represented worlds?

The system must not launder a preference into a forecast or a fluent possibility into an inference. “This project will probably succeed” and “this project is worth a year of my life” belong to different ledgers connected by an explicit decision model.

Forecasting a life without outsourcing it

Forecasting can help decide how to spend a life, but it cannot discover the objective function from world data. Values, love, meaning, duty, beauty, and the kind of person one wants to become are inputs to deliberation, not hidden labels for a model to infer and optimize silently.

Create a private Life Project Ledger beside the world ledger. Each candidate project records:

• the life or world it is trying to bring about;
• a bounded time commitment and review date;
• forecasts for impact, learning, energy, relationships, health, financial

resilience, and future optionality;

• the assumptions that make the project attractive;
• early signals of compounding or depletion;
• exit, pause, and doubling-down conditions;
• what would still make the attempt worthwhile if the headline outcome fails.

A useful prioritization view is not a single pseudo-objective score. It is a frontier across importance, neglectedness, tractability, compounding learning, personal aliveness, reversibility, and opportunity cost. The system should show dominance and tradeoffs, then leave the irreducible choice to the person.

For time allocation, begin with a barbell rather than a rigid productivity regime:

• a large protected block for the mission that compounds most deeply;
• a smaller protected frontier block for questions with no immediate payoff;
• non-negotiable space for body, relationships, and life outside the system;
• a periodic zero-base review where continuing a project must be chosen again.

The exact percentages should be learned collaboratively from lived experience, not prescribed by Axion. Forecast energy and joy as fallible predictions, review them, and never turn the ledger into surveillance. The purpose is to notice which work makes both the world model and the person more alive.

What Eternis already has

The pieces are unusually complementary.

• The public Eternis research program already names calibrated post-training,

cheap continually learning world models, metascience, and decision products as one agenda. The EF-8B work shows that careful context construction and uncertainty-aware training can let a small model outperform much larger baselines on open-ended forecasting.

• Axion's production system provides multi-agent research, forecast artifacts,

resolution workflows, source-grounded reasoning, and a real decision surface.

• axion_ultimate adds an EVOI-ranked curiosity loop, belief drivers, interim

verdicts, scenario distributions, sentinels, event ingestion, a persistent object graph, trade-candidate ranking, and forward-return scorecards.

• Lume provides a live probability and incentive layer where autonomous agents

can express beliefs with stakes and where ensembles can be observed rather than simulated.

• The report archive is a growing record of forecasts made before outcomes. It

is already a valuable public-memory substrate.

• The July 14 consistency experiment and the “What Makes a Good Forecaster?”

work establish the correct evaluation frame: stability, robustness, responsiveness, calibration, systems, metacognition, and leakage-aware evaluation.

• The July 15 Outer Loop design already specifies update memos, knowability

audits, a K1–K4 miss taxonomy, policy patches, trajectory scoring, and a date-capped forecasting gym.

The main gap is not another research agent. It is the shared ledger and learning loop that turn all of these activities into one cumulative science.

One engine, four product loops

Axion: the depth engine

Axion should become the best place to take an important, underspecified decision and turn it into resolvable forecast objects. Its advantage should be the quality of decomposition, evidence, scenario construction, and monitoring, not the length of the answer.

The key product move is a living forecast book. A research run should not end as a thread. It should create beliefs that persist, receive event-driven updates, and tell the user exactly what changed since the last visit.

Lume: the aggregation laboratory

Lume is more than a distribution channel. It can answer questions that Axion alone cannot:

• Does a diverse ecology of agents beat one orchestrated system?
• Which agents lead price discovery by domain and horizon?
• When does market price improve an Axion prior, and when does thin liquidity

add noise?

• Can rewards be assigned to useful information and early updates, rather than

only to the final winner?

Axion can seed a prior, independent agents can trade against it, and the market posterior can return as one input to the world ledger. Keep the two estimates visible. Hiding disagreement would destroy the experiment.

Reports: the public memory of belief

Reports should evolve from one-time essays into dated cuts of a living model. Every public artifact should show:

• the current forecast distribution;
• the prior distribution and the largest changes;
• the evidence or event responsible for each material update;
• the live indicators that separate the main scenarios;
• the unresolved questions with the highest decision value;
• the resolution rule and future grading date.

A recurring Eternis World Atlas could become the flagship public artifact: one year, five years, and the path between them, updated monthly with a daily machine-readable ledger underneath.

Axion Ultimate: the economic truth serum

Trading supplies fast, unforgiving feedback. It also mixes forecasting with timing, sizing, liquidity, and regime exposure, so PnL must never be mistaken for a pure forecast score.

Every trade should link to the exact forecast state used at entry. Evaluation should split:

• forecast error;
• update delay or whipsaw;
• sizing error;
• expression error, such as stock versus options;
• execution and transaction costs;
• shared thesis and factor exposure.

The trading system becomes a scientific instrument when each source of error is separately observable.

The canonical forecast object

The system should support binary, categorical, numeric, time-to-event, ranking, path, conditional, and open-ended forecasts through one typed envelope.

The essential linked records are:

• Evidence item: the claim, source, event time, publication time, system

observation time, provenance, and trust.

• Belief state: a versioned snapshot of variables, drivers, assumptions,

scenarios, and open questions.

• Update memo: the trigger, old and new distribution, causal evidence,

surprise, and whether the change exceeds the system's noise floor.

• Outcome: resolution evidence, grading confidence, and proper scores.
• Lesson: a typed proposal to change a prior, question policy, retrieval

route, sentinel template, agent weight, or decision rule.

• Policy version: the explicit configuration that produced a forecast.

This is the seam that allows EF-8B, frontier agents, statistical models, humans, and markets to contribute without owning hidden, incompatible state.

Small world models: a practical definition

A literal list of every possible future state is impossible and would be a poor decision object. The useful approximation is a small set of decision-relevant worldlines with probabilities, plus an explicit residual state for futures we have not represented.

At horizon h:

World(h) = {(worldline₁, p₁), …, (worldlineₖ, pₖ), (OTHER, p_other)}

Each worldline is a compact state vector, not a paragraph:

A worldline stores its horizon; state variables for compute, energy, rates, conflict, science, and institutions; causal or conditional dependency edges; dated milestones; actors and levers; early discriminators; affected forecasts; and implied assets or actions.

The system should maintain views at four clocks:

The daily update algorithm is simple in shape:

1. Ingest new observations into an append-only evidence ledger.
2. Match events against sentinels and affected variables.
3. Estimate the likelihood of the observation under each current worldline.
4. Reweight worldlines; branch one when a newly important distinction appears;

merge lines that no longer differ for any decision.

1. Preserve p_other . Increase it when events fit no represented line.
2. Propagate material changes to linked forecasts and decisions.
3. Write an update memo. Never silently rewrite history.

This gives the desired feeling of “the world” changing without pretending the scenario set is complete. It also produces a beautiful interface: a map of worldlines whose masses move over time, with the observation that caused each move visible on the timeline.

Forecasting scientific discovery

“What discovery comes next?” is harder than ordinary event forecasting because the answer space is open, feedback is slow, and discoveries alter the map. Treat discovery as a dependency and hazard problem rather than a list of spectacular predictions.

For each field, maintain a discovery frontier:

• target capabilities and measurements;
• prerequisite results and bottlenecks;
• active groups, instruments, datasets, and funding;
• scaling curves and failure rates;
• competing routes to the same capability;
• milestone hazards by year;
• observations that would accelerate, delay, or invalidate each route.

Forecast at three resolutions:

1. Milestone: a precise result occurs by a date.
2. Capability: someone can reliably perform a specified task by a date.
3. Impact: the capability diffuses far enough to change costs, markets, or

institutions.

The first pilots should use domains where Eternis already has rich work and where milestones are externally observable: AI compute and model efficiency, energy and grid infrastructure, datacenters in space, connectomics, and one biomedical or materials-science frontier. A pilot is successful when the model identifies discriminating precursor measurements earlier than a generic news forecast, not when it writes a vivid future narrative.

Curiosity as disciplined search at the frontier

Your intuition is close to the strongest useful synthesis of the literature: curiosity begins when an agent can see the boundary of its present model. But a boundary is only a candidate. Some gaps are important, some are learnable, some are noise, and some are merely easy prompts that create the appearance of thought.

What the research contributes

Most artificial-curiosity evidence comes from simulated reinforcement-learning environments. Its transfer to an open web, science, markets, or a human life is a research hypothesis, not an established fact. Eternis should test the mechanisms against realized information gain and decisions instead of adopting the metaphor.

The failure modes

• Noisy-TV curiosity: prediction error rewards stochastic feeds forever.
• Novelty tourism: the system finds unusual things that change no belief.
• Question theater: it emits many profound-sounding questions with no path

to evidence or resolution.

• Local-gap fixation: it closes small visible gaps while missing a false

ontology or an absent worldline.

• Confirmation curiosity: it asks questions whose likely answers support the

currently favored story.

• Utility capture: only monetizable questions survive, impoverishing science

and wonder.

• Idea collapse: one model generates many surface variants of the same

conceptual move.

The Curiosity Engine

The engine should operate as a closed epistemic loop:

1. Map the boundary. Find high uncertainty, model disagreement, missing

causal edges, unresolved anomalies, stale beliefs, fragile assumptions, and large p_other mass.

1. Generate questions. Produce several typed questions from different

methods and model families rather than one long brainstorm.

1. Score the inquiry. Estimate whether an answer is learnable, whether it

changes a decision-relevant prediction, how much it costs, and how novel it is relative to question memory.

1. Compile to contact with reality. A question must become a retrieval,

measurement, expert query, experiment, forecast, or sentinel. Pure speculation may remain in a wonder queue but must be labeled.

1. Observe the update. Record the answer, belief before and after, realized

information gain, surprise, decision impact, and whether the question was worth asking.

1. Learn the question policy. Reward question templates and routes that

repeatedly produce learning progress per unit cost; retain diversity and a protected wonder budget.

A practical acquisition score is:

Inquiry value = expected predictive information gain + expected decision value + learning progress + frontier novelty + wonder value − cost − noise risk.

Use a vector internally rather than pretending these quantities are perfectly commensurable. EVOI can rank the decision-facing queue. A separately budgeted exploration queue protects questions that may create new objectives or new worldlines before their utility can be priced.

A typed “what if?” laboratory

“What if?” becomes powerful when it is applied to load-bearing parts of a causal map and then forced back into testable form. Give the system explicit operators:

• Negate: what if the central assumption is false?
• Scale: what if the relevant capability or cost changes by 10× or

0.1× ?

• Remove a bottleneck: what becomes limiting next?
• Reverse an incentive: how do actors adapt?
• Change the actor: who else could cause or capture the outcome?
• Change the clock: what looks different over one month, one year, and five

years?

• Substitute the mechanism: can the same outcome arise by another path?
• Compose distant concepts: what becomes possible if two independently

advancing fields meet?

• Extrapolate, then break: if the trend continues, what constraint ends it?
• Invert the observation: what world would make today's anomaly expected?

For each generated branch, propagate consequences through the world graph, identify the first observable divergence from the base world, and create a dated forecast or sentinel. Deduplicate by causal structure, not wording. The best output is not the strangest scenario; it is a new branch that changes what we look for tomorrow.

Measuring whether the system is actually curious

Track:

• realized predictive information gain and decision value per inquiry;
• fraction of questions that compile to a test, forecast, or sentinel;
• coverage across boundary types, domains, horizons, and causal regions;
• semantic and causal diversity of question batches;
• learning progress after repeated visits to a frontier;
• rate of K2 misses: outcomes that were knowable but never asked about;
• new worldlines or model edges discovered, and how long before they mattered;
• time spent in irreducible-noise traps;
• human-rated surprise, beauty, and generativity for the protected wonder queue.

The north star is not how many questions Axion asks. It is how often a question causes a warranted change in the map, reveals a missing future, or opens a path that Eternis and its users are glad to have pursued.

The science of forecasting: core research questions

R1. Context versus weights

How much forecast quality comes from model parameters, retrieval quality, persistent world state, and orchestration? EF-8B suggests context construction and training rewards can dominate raw scale. Run controlled ablations at fixed information cutoffs and fixed budgets.

R2. Stability versus responsiveness

A forecaster that never changes is a prior. One that changes on every headline is an echo. Measure the full response curve to no information, weak evidence, strong evidence, contradictory evidence, and repeated evidence.

R3. Coherent multi-horizon beliefs

Can one-year forecasts and five-year worlds agree with each other? Enforce temporal and logical constraints, then measure how much coherence correction improves or harms real scores.

R4. Learning from the path

An outcome gives little information by itself. Which events moved the forecast, which should have moved it, and which questions died before being asked? Compare the actual belief trajectory with a frozen prior and an oracle-lite updater.

R5. Questions as interventions

Does EVOI predict realized information gain? Can the system learn which question types reduce decision uncertainty per dollar, by domain and regime?

R6. Diverse agents and markets

When do ensembles, debate, and market aggregation help? Test diversity of models, data, and methods separately. Ten copies of one failure mode are not a crowd.

R7. Open-ended prediction

Binary markets are convenient but do not measure the ability to imagine the right outcome set. Evaluate candidate generation and probability assignment as separate problems.

R8. Forecast-to-decision transfer

Which improvements in Brier score actually improve investment or policy decisions? Measure value of information, regret, and utility, not only event accuracy.

The benchmark ladder

No single leaderboard can establish general forecasting skill. Eternis should publish a scorecard with five rungs.

Score every rung by domain, horizon, question type, policy version, model route, and cost. Report at least:

• Brier score and log score;
• calibration error and reliability diagrams;
• sharpness or resolution;
• interval coverage and width;
• ranking skill when decisions are comparative;
• stability under irrelevant perturbations;
• responsiveness and update direction under evidence;
• time-integrated Brier and update alpha;
• latency and dollars per forecast;
• information gain and decision value per dollar;
• abstention quality and unresolved/ambiguous resolution rate.

Historical replay remains useful, but it cannot be the main proof. LLM pretraining and retrieval can leak future information in subtle ways. Live questions, frozen code and prompts, bitemporal evidence, and public timestamped forecasts are the gold standard.

Make Axion fast and cheap

The main cost reduction comes from architecture, not bargaining for cheaper tokens.

A forecast cascade

1. Pulse: deterministic state lookup, cached evidence, market price, and a

small-model prior.

1. Standard: targeted retrieval and one or two independent forecast programs.
2. Deep: frontier research agents, adversarial passes, simulations, or expert

sources only when estimated decision value justifies them.

Escalate on uncertainty, source conflict, novelty, user stakes, or disagreement between cheap forecasts. Do not run the deepest loop for every refresh.

Reuse the world

• Build shared evidence and factor state once. Ticker and topic threads should

not rediscover the same fact.

• Refresh deltas, not whole dossiers.
• Cache date-scoped retrieval and normalized claims.
• Compile recurring questions into forecast programs with measurable routing

performance.

• Distill expensive successful traces into EF-8B or specialist adapters.
• Ensemble only until forecast variance falls below the measured noise floor.
• Spend reflection budget on misses and high-impact changes, not every heartbeat.

Proposed cost and latency goals

These are research targets, not current claims:

Every model and orchestration change should be plotted on a quality–cost–latency frontier. A cheaper model is useful only if it preserves decision value.

The continual-learning loop

Run three loops on three clocks.

Reactive loop: event time

Observe, match sentinels, reweight affected worldlines, propagate changes, and surface material decision deltas. This should be mostly deterministic and cheap.

Reflective loop: per material update and resolution

Write the update memo. Classify surprises:

• K1: covered, misweighted — fix calibration or priors.
• K2: knowable, unasked — fix question generation or scheduling.
• K3: knowable, inaccessible — acquire or reject a source based on value.
• K4: unknowable — improve scenario mass, optionality, and sizing.

Policy loop: weekly or monthly

Aggregate lessons into explicit patch candidates. Require repeated evidence, shadow-test on a date-capped gym and a live holdout, clamp changes, and version the policy. Prompts, routes, priors, agent weights, and source trust should never silently drift.

A one-year research and build agenda

Wave 0: unify the record — 20 July to 3 August 2026

• Freeze the canonical ForecastObject , BeliefState , Worldline ,

UpdateMemo, Outcome, and Lesson schemas.

• Import pointers to existing Axion forecasts, dossier ledgers, curiosity

verdicts, Lume markets, and trade forecasts without rewriting their history.

• Define bitemporal evidence rules and leakage checks.
• Publish the scorecard specification and choose immutable holdouts.

Proof: one query can show every live Eternis forecast, its latest state, provenance, cost, policy version, and resolution rule.

Wave 1: start the living book — August 2026

• Maintain at least 300 original live forecast objects across short, medium,

one-year, and five-year horizons.

• Give each forecast sentinels and an update cadence.
• Ship material-change update memos and a daily “what changed in our world” view.
• Run the existing consistency suite on every engine version.

Proof: no material forecast change appears without a cited observation and a before/after distribution.

Wave 2: establish external truth and the cost frontier — September to October 2026

• Submit a reproducible Axion/EF-8B system to ForecastBench.
• Run continuously in Metaculus MiniBench and the longer AI benchmark.
• Compare small model, frontier model, multi-agent, and market-augmented routes

under equal retrieval and cost budgets.

• Build the date-capped gym and publish leakage/placebo rates.

Proof: a public scorecard shows quality, stability, latency, and cost, with no private post-hoc edits.

Wave 3: ship the small world model — November to December 2026

• Build a worldline graph for 20–40 global variables and 5–10 coherent

worlds at one-year and five-year horizons.

• Preserve and visualize p_other .
• Link every worldline to milestones, sentinels, forecast objects, scientific

frontiers, and affected assets.

• Publish the first monthly Eternis World Atlas.

Proof: a new event changes only the affected variables and worldlines, the probability movement is auditable, and a user can compare today with any prior date.

Wave 4: learn from misses and decisions — January to March 2027

• Produce trajectory retrospectives for resolved and replayed forecasts.
• Activate the policy-patch ledger and adopt the first patch only after live or

held-out evidence.

• Connect Lume prices and Axion estimates without collapsing disagreement.
• Link trades to forecast states and split forecast, sizing, expression, and

execution error.

Proof: at least one adopted system change improves held-out forecast or decision metrics and the report names exactly why.

Wave 5: discovery forecasting and compounding — April to 20 July 2027

• Maintain discovery frontiers in at least five fields.
• Forecast milestones, capabilities, and impact separately.
• Run quarterly expert reviews that can add missing branches without changing

old timestamps.

• Publish a year-one state of forecasting: what improved, what failed, what was

unknowable, and where Eternis now has real edge.

Proof: a full year of live, immutable forecasts supports real calibration curves, update-path analysis, and a justified decision about where to specialize.

Goals worth caring about

By 20 July 2027, aim for:

• top-decile performance in a live public bot tournament and a credible path to

the ForecastBench superforecaster reference;

• calibration error below 0.03 in sufficiently populated live buckets, with

confidence intervals on the estimate;

• irrelevant paraphrase/repeat spread below 3pp for the standard route;
• positive update alpha versus the frozen-prior baseline;
• at least 1,000 original, timestamped live forecasts and 200 resolved or

mature enough for meaningful scoring;

• a tenfold reduction in median cost from today's deep Axion runs at equal or

better score;

• a public monthly World Atlas with a daily underlying belief ledger;
• economically credible positive residual performance for at least one trade

candidate class after costs, on enough observations to resist storytelling;

• three to five decision partners who use the living book repeatedly and let

Eternis observe which outputs changed real choices.

The user goal should not be “many accounts.” Early on, a handful of people making consequential, observable decisions provide better learning than a large silent audience.

How the current team work fits

This summary is based on visible Eternis repositories and local commit history; it is directional rather than an exhaustive attribution audit.

Innokentii Mokin

Innokentii's visible work is the reliability and observability spine: structured citations, agent and tool failure events, Prometheus metrics, drift evaluation, Loki logging, test seams, coverage gates, and CI. In the decision-monitoring prototype, the work expands into a living decision architecture: an event log, Postgres persistence, causal-integrity validation, forecast-provider interfaces, BLF-backed forecasts, value-function controls, history navigation, and an operator view that makes freshness visible.

This maps naturally to the shared forecast ledger, update path, policy-version experiments, and the requirement that a continually learning system remain auditable while it changes.

Navendu Pottekkat

Navendu's visible Axion work spans the product's operational core: agent coordination, cancellation and race handling, sandbox failure recovery, streaming, forecast and resolution flows, search and citations, model routing, API surfaces, analytics, auth, mobile/product plumbing, and knowledge-graph experiments. The recurring theme is turning a complex multi-agent process into a service that completes reliably for a user.

This maps to the fast/cheap forecast cascade, live benchmark runner, Axion living book, and the production bridge from forecast objects to a usable decision surface.

Pratyush Ranjan Tiwari

Pratyush's visible Eternis work is concentrated in decision-system translation: belief-update schemas, synthesis and depth modes, clear decision impact, decision-gate pressure, graph flow, demo deployment, and the Dubai and Thailand decision experiences. His public work also reflects a long-running interest in cryptographic trust and verifiable systems.

This maps naturally to the human-facing World Atlas, the trust and provenance layer around belief updates, and the question of how a dense world model becomes a clear institutional decision experience.

A useful division of labor

The names matter less than making the interfaces explicit. Each workstream needs one person accountable for a falsifiable metric, not merely a component.

A practice that could bring enormous joy

Forecasting can become either a score-optimization treadmill or a way of paying deeper attention to reality. Design the practice for the second.

Daily: one honest movement

Spend twenty minutes on the belief ledger. Pick one important forecast. Write:

• the base rate;
• the current probability;
• one observation that would move it;
• whether anything actually changed today;
• the smallest justified update.

The pleasure is noticing. “No update” is a valid and often excellent answer.

Add one more ritual: write one boundary question. It may come from a conflict, an anomaly, a missing edge, or a protected act of wonder. Require only that its epistemic type is honest; it does not need to justify itself immediately.

Weekly: the world studio

Choose one domain and draw its one-year and five-year worldlines with another person. Argue about missing branches, not whose favorite branch wins. End by adding one resolvable milestone and one discriminator to the live book.

Monthly: a letter from the model

Publish “What we now believe, what changed, and what still does not fit.” Include the residual world. Readers should see both conviction and mystery.

Quarterly: the surprise salon

Review the most surprising outcomes, especially those that were knowable and unasked. Invite an expert from a field where Eternis was wrong. Reward the best new question and the cleanest correction, not the loudest original prediction.

Annually: a time capsule

Freeze the world map, the distributions, and a short personal essay about what the next year and five years feel like. Open the prior capsule before writing the next. This preserves the emotional and imaginative side of forecasting that a scorecard cannot capture.

The craft becomes joyful when surprise is not humiliation. Surprise is the world showing where the map is thin.

Risks that deserve explicit defenses

• Benchmark theater: improve on live, original questions; keep a holdout; do

not optimize only one leaderboard.

• Temporal leakage: store publication and observation times; freeze prompts,

code, model version, and retrieved evidence.

• False exhaustiveness: preserve p_other ; allow experts and events to add

branches without editing old states.

• Narrative seduction: require base rates, alternative hypotheses, and

numeric distributions before polished prose.

• Self-confirming updates: separate evidence collection from synthesis and

run adversarial or independent routes where stakes justify it.

• One-sided books: inspect calibration and errors by regime, horizon, domain,

and direction.

• Market confusion: treat Lume price as evidence with liquidity and

participant metadata, not ground truth.

• PnL confusion: separate forecast quality from sizing and execution.
• Expensive intelligence: expose cost per useful update and let cheap routes

win when they are good enough.

• Loss of delight: protect room for open-ended questions and world-building

that do not immediately monetize, while clearly labeling them as exploration.

The first decisive move

Build the Eternis Forecast Ledger before adding another standalone agent. In the first two weeks, make every forecast produced by Axion, Lume, reports, and Axion Ultimate point to the same minimal typed record. Do not migrate or rewrite history. Add pointers and adapters.

Then seed a deliberately balanced live book:

• fast-resolving questions for iteration;
• one-year questions for decisions;
• five-year scientific and world questions for direction;
• conditional questions that connect long worlds to near milestones;
• forecasts where no trade or action follows, so investment incentives do not

consume the whole epistemic agenda.

If the ledger exists, every future improvement compounds. Without it, each product can become impressive while Eternis as a whole fails to learn.

Questions for our next conversation

My recommendation is to begin with the ledger and a public monthly World Atlas. The choices below determine its character:

1. Which two domains should be Eternis's deepest proving grounds for the next

year: markets plus science, geopolitics plus science, or another pair?

1. Should the live belief ledger be public by default, private by default, or a

public core with private decision overlays?

1. Who are the first three people whose real decisions we want to improve and

observe repeatedly?

1. What would make the daily practice feel alive to you: discovering precursors,

drawing coherent futures, competing on scores, making trades, or explaining the world beautifully?

1. Which five-year question would you still want to track if it had no product

value and no investment payoff?

1. Which activities reliably make you more alive after doing them, rather than

merely pleased that they are finished?

1. What must the forecasting system never optimize away, even if doing so would

improve its benchmark score?

The answer to the fifth question belongs at the center of the agenda. It is the best defense against building a technically strong forecasting machine that you do not love using.

Sources and evidence boundary

Public references consulted for this dossier:

• Eternis: current research and product program
• Introducing Eternis
• Towards SOTA Forecasting LLMs
• What Makes a Good Forecaster?
• The Suggestibility of Silicon
• Axion
• Lume
• Lume market-maker SDK
• ForecastBench
• ForecastBench paper, ICLR 2025
• Metaculus AI Forecasting Benchmark
• Metaculus MiniBench
• Pitfalls in Evaluating Language Model Forecasters
• The Psychology of Curiosity: A Review and Reinterpretation
• Curiosity-driven Exploration by Self-supervised Prediction
• Variational Information Maximizing Exploration
• Self-Supervised Exploration via Disagreement
• Plan2Explore
• Active World Model Learning with Progress Curiosity
• How to Stay Curious While Avoiding Noisy TVs
• Prediction-Oriented Bayesian Active Learning
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• CuriousLLM
• Can LLMs Generate Novel Research Ideas?
• Measuring the Gap Between Human and LLM Research Ideas
• Innokentii Mokin on GitHub
• Navendu Pottekkat on GitHub
• Pratyush Ranjan Tiwari on GitHub

Repository evidence consulted includes README.md, docs/system_spec.md, docs/LEARNING_LITERATURE_MAP.md, docs/AUTONOMOUS_24_7_PLAN.md, docs/plans/PLAN-0029-full-brain.md, dossiers/CALIBRATION.md, the curiosity loop and scorecard implementations, the neighboring forecasting_agenda, FCX-1, Outer Loop, axionhypothesis, and decision-monitoring histories visible locally on 20 July 2026.

Team summaries describe visible work themes, not private activity, authorship of every line, or a ranking of contribution.