Передовые направления исследований: AGI и далее

# Illustration of long-horizon memory challenges:

LONG_HORIZON_CHALLENGES = {
    'compression': {
        'problem': 'Summarising months of interactions loses nuance',
        'current_approach': 'Hierarchical summarisation (recent detail, old summary)',
        'limitation': 'Important details get compressed away; hallucination risk in summaries'
    },
    'retrieval': {
        'problem': 'Finding the relevant memory among millions of entries',
        'current_approach': 'Embedding-based similarity search (vector databases)',
        'limitation': 'Semantic similarity does not always match relevance; false negatives'
    },
    'reasoning_over_time': {
        'problem': 'Connecting observations from 6 months apart',
        'current_approach': 'Temporal indexing + LLM reasoning',
        'limitation': 'LLMs struggle with precise temporal ordering of distant events'
    }
}

for challenge, details in LONG_HORIZON_CHALLENGES.items():
    print(f'{challenge}: {details["limitation"][:80]}')

# Measuring domain robustness
import statistics

def measure_domain_robustness(agent_fn, test_suite: dict) -> dict:
    """
    test_suite: {domain: [(input, expected_output)]}
    Returns per-domain accuracy and overall robustness score.
    """
    domain_scores = {}
    for domain, cases in test_suite.items():
        correct = 0
        for inp, expected in cases:
            result = agent_fn(inp)
            # Simplified scoring: check if expected phrase is in result
            if expected.lower() in result.lower():
                correct += 1
        domain_scores[domain] = round(correct / len(cases), 3)

    scores = list(domain_scores.values())
    return {
        'domain_scores': domain_scores,
        'mean_accuracy': round(statistics.mean(scores), 3),
        'min_accuracy': min(scores),  # robustness = performance on worst domain
        'variance': round(statistics.variance(scores), 4)
    }

# High variance = brittle (good at some domains, bad at others)
# Low variance + high mean = robust

if __name__ == '__main__':
    def toy_agent(inp):
        return {
            '2+2': 'The answer is 4',
            'capital of France': 'Paris is the capital'
        }.get(inp, 'I do not know')

    test_suite = {
        'math': [('2+2', '4')],
        'geography': [('capital of France', 'paris')],
    }
    result = measure_domain_robustness(toy_agent, test_suite)
    print('Domain scores:', result['domain_scores'])
    print('Mean accuracy:', result['mean_accuracy'])

import anthropic
import json

client = anthropic.Anthropic(api_key='YOUR_API_KEY')

# Simple task negotiation between two agents
def negotiate_task_division(
    task: str,
    agent1_capabilities: list,
    agent2_capabilities: list
) -> dict:
    prompt = (
        f'Task: {task}\n\n'
        f'Agent A capabilities: {agent1_capabilities}\n'
        f'Agent B capabilities: {agent2_capabilities}\n\n'
        'How should this task be divided between Agent A and Agent B?\n'
        'Minimise handoffs. Assign subtasks to the best-suited agent.\n'
        'Return JSON: {"agent_a_tasks": [str], "agent_b_tasks": [str], '
        '"shared_tasks": [str], "handoffs": int}'
    )
    response = client.messages.create(
        model='claude-opus-4-5', max_tokens=512,
        messages=[{'role': 'user', 'content': prompt}]
    )
    return json.loads(response.content[0].text)

# Open research challenge:
# How do agents coordinate without a central planner
# when each has only partial information?

# Practical interpretability techniques available today:

INTERPRETABILITY_TECHNIQUES = {
    'chain_of_thought': {
        'description': 'Ask model to show reasoning steps',
        'limitation': 'CoT may not reflect true internal computation',
        'example': 'Q: Why did you choose action X? A: Because...'
    },
    'attention_visualisation': {
        'description': 'Show which input tokens the model attended to most',
        'limitation': 'Attention != causation; incomplete explanation',
        'example': 'Highlight most attended tokens in a response'
    },
    'logit_lens': {
        'description': 'Read out predictions at each transformer layer',
        'limitation': 'Requires model internals access (not API-accessible)',
        'example': 'Prediction at layer 12 vs layer 24'
    },
    'activation_patching': {
        'description': 'Intervene on specific neurons to find causal circuits',
        'limitation': 'Research technique, not yet practical in production',
        'example': 'Anthropic mechanistic interpretability research'
    }
}

for technique, info in INTERPRETABILITY_TECHNIQUES.items():
    print(f'{technique}: {info["limitation"][:80]}')

CAPABILITY_MAP_2025 = {
    'strengths': [
        'Multi-step reasoning (GSM8K, MATH near human performance)',
        'Code generation (HumanEval >90%)',
        'Instruction following (complex multi-part prompts)',
        'Tool use (reliable function calling)',
        'Vision understanding (OCR, chart analysis, scene description)',
        'Context: 128K-1M tokens',
        'Multi-agent orchestration (AutoGen, CrewAI frameworks)'
    ],
    'limitations': [
        'Long-horizon planning (>20 steps degrades significantly)',
        'Reliable factual grounding without hallucination',
        'Consistent reasoning in out-of-distribution domains',
        'True causal reasoning (vs pattern matching)',
        'Self-knowledge of own uncertainty',
        'Physical world understanding without embodiment'
    ]
}

print('Strengths:', len(CAPABILITY_MAP_2025['strengths']))
print('Active limitations:', len(CAPABILITY_MAP_2025['limitations']))

AGI_RESEARCH_AREAS = {
    'sample_efficiency': {
        'question': 'How to learn from 10 examples what LLMs need 10M for?',
        'approaches': ['meta-learning', 'few-shot learning', 'in-context learning']
    },
    'causal_reasoning': {
        'question': 'How to distinguish correlation from causation reliably?',
        'approaches': ['causal graphs', 'do-calculus integration', 'intervention-based training']
    },
    'open_world_operation': {
        'question': 'How to act effectively in environments not seen during training?',
        'approaches': ['world models', 'imagination-based planning', 'transfer learning']
    },
    'recursive_self_improvement': {
        'question': 'Can an agent improve its own architecture safely?',
        'approaches': ['neural architecture search', 'prompt optimisation', 'constrained self-modification']
    }
}

for area, info in AGI_RESEARCH_AREAS.items():
    print(f'{area}: {info["question"][:70]}')

ENGINEERING_PRINCIPLES_FROM_RESEARCH = {
    'long_horizon_memory': (
        'Use hierarchical summaries + vector retrieval. '
        'Set a hard context age limit and revalidate critical facts. '
        'Never trust old memories without verification.'
    ),
    'domain_robustness': (
        'Evaluate your agent on held-out domains before production. '
        'Monitor domain distribution of production inputs. '
        'Fall back to human when input is out-of-distribution.'
    ),
    'multi_agent': (
        'Minimise inter-agent communication. '
        'Use shared state (not message passing) where possible. '
        'Assign clear non-overlapping scopes to each agent.'
    ),
    'interpretability': (
        'Always request chain-of-thought for high-stakes decisions. '
        'Log all tool calls and intermediate reasoning steps. '
        'Build anomaly detection on the CoT stream, not just final output.'
    )
}

for principle, guidance in ENGINEERING_PRINCIPLES_FROM_RESEARCH.items():
    print(f'{principle}: {guidance[:80]}...')

# Historical emergent capability timeline (approximate):
EMERGENCE_TIMELINE = [
    {'year': 2020, 'scale': 'GPT-3 (175B)',
     'emergent': 'Few-shot in-context learning without fine-tuning'},
    {'year': 2022, 'scale': 'PaLM (540B)',
     'emergent': 'Chain-of-thought reasoning with step-by-step prompts'},
    {'year': 2023, 'scale': 'GPT-4',
     'emergent': 'Reliable code generation, bar exam performance'},
    {'year': 2024, 'scale': 'Claude 3 Opus, GPT-4o',
     'emergent': 'Reliable multi-step tool use, vision-language integration'},
    {'year': 2025, 'scale': 'Claude Opus 4, GPT-4o class',
     'emergent': 'Extended multi-agent task delegation, agentic autonomy'}
]

for entry in EMERGENCE_TIMELINE:
    print(f'{entry["year"]} ({entry["scale"]}): {entry["emergent"]}')

print('\nKey insight: capabilities can appear suddenly as scale increases — '
      'current limitations may not be permanent.')

SAFETY_RESEARCH_AREAS = {
    'scalable_oversight': (
        'Challenge: how do humans supervise agents that are better than us at the task?\n'
        'Approach: break tasks into verifiable sub-problems humans can check\n'
        'Status: active research at Anthropic, DeepMind, OpenAI'
    ),
    'debate': (
        'Challenge: finding truth when the agent is more capable than the evaluator\n'
        'Approach: two AI agents argue for different answers; human judges quality of argument\n'
        'Status: theoretical framework, limited empirical results'
    ),
    'weak_to_strong_generalization': (
        'Challenge: a weak supervisor training a stronger model\n'
        'Approach: show strong model responses can be elicited by weak supervision\n'
        'Status: OpenAI 2024 paper showed promising early results'
    ),
    'interpretability': (
        'Challenge: understanding neural network internals\n'
        'Approach: mechanistic interp, sparse autoencoders, circuit analysis\n'
        'Status: Anthropic found emotion-like representations in Claude'
    )
}

for area, desc in SAFETY_RESEARCH_AREAS.items():
    print(f'{area}:')
    print(f'  {desc.split(chr(10))[0]}')

DEVELOPER_ROADMAP = {
    'immediate': [
        'Master prompt engineering + few-shot design',
        'Build reliable tool-use agents with retry + error handling',
        'Implement proper logging, monitoring, and human oversight',
        'Study agent frameworks: LangChain, AutoGen, CrewAI'
    ],
    'next_6_months': [
        'Build multi-agent systems with clear agent scopes',
        'Implement vector memory + episodic reflection',
        'Contribute to open-source agent tooling',
        'Run proper evals: domain robustness, alignment red-teaming'
    ],
    'long_term': [
        'Follow interpretability research (Anthropic, DeepMind papers)',
        'Engage with alignment research community',
        'Build agents that remain human-overseen as capability grows',
        'Contribute to safety-conscious deployment standards'
    ]
}

for horizon, items in DEVELOPER_ROADMAP.items():
    print(f'{horizon}:')
    for item in items:
        print(f'  - {item}')