intelligence-radar/ResearchProgram.md

Intelligence Radar Research Program

Status: Draft 0.1
Date: 2026-09-13

1. Research objective

Maintain a continuously improving map of:

1. what intelligence is proposed to mean;
2. how intelligence is generated and constrained across substrates;
3. how it can be measured;
4. what artificial systems can currently do;
5. what those capabilities cost and where they are available;
6. what the consequences are for engineering, science, economics, society, safety, law, and governance.

The program intentionally joins slow epistemic research with fast operational monitoring.

2. Research streams

R1 — Models of intelligence

Questions:

• What definitions and operationalizations exist?
• Which phenomena do they explain well?
• Which assumptions do they make about goals, representation, embodiment, learning, or agency?
• Which theories are complementary versus genuinely contradictory?
• What evidence could discriminate among them?

R2 — Biological intelligence

• neural computation;
• memory and learning;
• animal cognition;
• development;
• evolution;
• perception/action loops;
• energetic efficiency;
• social cognition.

R3 — Systems, cybernetics, and control

• requisite variety;
• regulation;
• viability;
• feedback;
• adaptation;
• recursive organization;
• observer dependence;
• self-reference;
• autopoiesis/enaction.

R4 — Artificial architectures

• deep learning;
• transformers and alternatives;
• recurrent/state-space systems;
• world models;
• reinforcement learning;
• memory;
• retrieval;
• planning;
• tool use;
• program synthesis;
• neuro-symbolic systems;
• robotics;
• multi-agent systems.

R5 — Capability measurement

• construct validity;
• benchmark design;
• contamination;
• saturation;
• evaluator/judge reliability;
• reproducibility;
• ecological validity;
• long-horizon evaluation;
• hidden/private tests;
• human baselines;
• task distributions.

R6 — Capability frontier

Track:

• model releases;
• execution profiles;
• agent scaffolds;
• context and memory;
• multimodality;
• tool/computer use;
• reliability;
• autonomy horizon;
• coding;
• research;
• scientific reasoning;
• professional knowledge work;
• security-relevant capability.

R7 — Economics of intelligence

Track:

• input/output/cache/reasoning token prices;
• tool charges;
• cost per solved task;
• runtime;
• throughput;
• latency;
• energy;
• accelerator economics;
• self-hosting economics;
• open-weight availability;
• batching/flex/priority tiers;
• geography and access restrictions.

Goal:

Build a quality-adjusted price surface for intelligence rather than a token-price table.

R8 — Intelligence engineering

• inference infrastructure;
• accelerators;
• memory bandwidth;
• networking;
• compilers;
• quantization;
• serving;
• speculative decoding;
• distributed inference;
• edge intelligence;
• agent runtimes;
• observability;
• evaluation harnesses.

R9 — Safety, security, and reliability

• dangerous capabilities;
• cybersecurity;
• biosecurity;
• alignment;
• deception/manipulation;
• robustness;
• privacy;
• misuse;
• incident evidence;
• control and monitoring methods.

R10 — Economic and social effects

• productivity;
• task substitution/complementarity;
• labor;
• organizational design;
• education;
• science;
• inequality;
• access;
• market concentration;
• new business models.

R11 — Law, policy, standards, and institutions

Track original texts and official implementation material for:

• AI regulation;
• safety obligations;
• privacy/data law interactions;
• copyright;
• liability;
• competition;
• standards;
• procurement;
• sector regulation;
• export controls and compute policy where relevant.

3. Monitoring cadence

Daily

Detect high-value deltas.

Output:

• event;
• primary source;
• what changed;
• why it matters;
• affected catalog entities;
• affected claims/policies;
• confidence;
• follow-up required.

Weekly

Synthesize rather than concatenate.

Recommended sections:

1. Five most consequential changes
2. Capability frontier
3. Cost and availability frontier
4. Benchmark/evaluation health
5. Science of intelligence
6. Agents/software/hardware
7. Safety and security
8. Economics and society
9. Politics, law, regulation, standards
10. Compendium changes
11. What became less certain
12. Watchlist for next week

Monthly

Perform maintenance that daily/weekly news cannot:

• benchmark health review;
• source quality review;
• thinker/source additions/removals;
• stale claims;
• pricing/catalog reconciliation;
• calibration analysis from real tasks;
• research-gap prioritization.

Quarterly

Produce a deeper State of Intelligence revision:

• major model/theory changes;
• frontier movement;
• economics;
• evaluation validity;
• capability-to-cost curves;
• autonomy horizon;
• open vs closed availability;
• important regulatory changes;
• newly emerging research questions.

4. Evidence discipline

Tier A — Primary evidence

Preferred for durable facts:

• peer-reviewed papers / primary preprints;
• official datasets/evaluation repos;
• benchmark maintainers;
• model/system cards;
• provider documentation;
• official pricing;
• source code/releases;
• regulator/legislator texts;
• standards bodies;
• original experiment data.

Tier B — Independent measurement / synthesis

Examples:

• independent evaluation organizations;
• systematic benchmark comparisons;
• high-quality academic syntheses;
• major index reports.

Useful for triangulation and cross-provider comparison.

Tier C — Expert interpretation

Researchers, engineers, economists, legal experts, and other thinkers.

Useful for hypotheses, interpretation, and discovery.

Not equivalent to primary evidence.

Tier D — General reporting/social signal

Useful as an early warning or discovery stream.

A durable Radar claim should normally resolve to stronger evidence.

5. Research item template

id: research-item-...
title: ...
research_streams: [R5, R6]
question: ...
status: open
priority: high
claims:
  - ...
evidence_needed:
  - ...
sources:
  - ...
next_actions:
  - ...
last_reviewed: ...

6. Claim lifecycle

candidate
  → sourced
  → structured
  → challenged
  → triangulated
  → compendium claim
  → periodically re-evaluated

No claim becomes durable merely because many secondary articles repeat it.

7. Benchmark research lifecycle

Every benchmark gets a health record.

Review:

• intended construct;
• real task distribution;
• scoring correctness;
• ceiling/saturation;
• public-test contamination;
• benchmark gaming incentives;
• scaffold sensitivity;
• judge sensitivity;
• version comparability;
• human baseline quality;
• cost to run;
• licensing/access;
• predictive correlation with Intelligence Radar task outcomes.

A benchmark can be:

• candidate
• active
• watch
• degraded
• saturated
• retired

Retirement does not delete historical results.

8. Frontier research question

Do not ask only:

What is the best model?

Track multiple frontiers:

• maximal quality;
• quality per dollar;
• quality per joule;
• quality per second;
• quality per GB of memory;
• quality under local/self-hosted constraints;
• quality under privacy/ZDR constraints;
• autonomy horizon;
• open-weight frontier;
• deployable frontier by region/provider;
• multimodal frontier;
• specialized domain frontiers.

9. Calibration research

The most strategically important proprietary dataset will be the Task → Model Performance Ledger.

Research questions:

• Which public benchmarks best predict which task clusters?
• When do model-family/vendor effects remain after benchmark scores?
• How stable are rankings across scaffolds?
• Which task features predict failures?
• How much does decomposition help?
• When is a verifier more valuable than a larger generator?
• How do costs scale with ambiguity?
• How well does model self-estimated confidence predict actual success?
• Which benchmarks lose predictive value first as the frontier moves?

10. Anti-goals

Avoid:

• benchmark tourism;
• leaderboard worship;
• treating press-release evaluation tables as neutral evidence;
• silently mixing model-only and agent-scaffold results;
• using stale prices;
• measuring task success only with model judges;
• collapsing heterogeneous scientific concepts into a single AGI narrative;
• overreacting to every daily release.

11. First twelve research tasks

1. Build a genealogy of definitions of intelligence across psychometrics, cybernetics, cognitive science, neuroscience, AI, and collective intelligence.
2. Establish a benchmark construct taxonomy.
3. Create a benchmark health scorecard.
4. Model execution_profile explicitly and normalize existing leaderboard evidence into it.
5. Build price-observation ingestion for major providers.
6. Build a first capability-vector ontology from 50 representative real tasks.
7. Test the five-benchmark selector manually on those 50 tasks.
8. Establish initial success-probability calibration using repeated model trials.
9. Compare monolithic vs decomposed execution on at least 20 complex tasks.
10. Measure benchmark correlation and redundancy.
11. Define availability/privacy constraints as first-class ranking filters.
12. Publish the first quarterly State of Intelligence report from accumulated Radar evidence.