Generative AI Act II
Test Time Scaling Drives Cognition Engineering
The first generation of LLMs, "Act I," achieved success through scaling but hit fundamental limits. "Act II" (2024-present) leverages test-time scaling to unlock deep thinking, transitioning models from knowledge retrieval to thought construction. This new paradigm, cognition engineering, fosters mind-level connections with AI through language-based thoughts. Our paper clarifies its foundations, provides tutorials, and enables practitioners to join AI's second act.
Executive Impact & Key Metrics
Cognition Engineering marks a fundamental shift, moving AI beyond basic knowledge retrieval to dynamic wisdom and deep problem-solving.
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Core Contributors
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Increase in Reasoning Depth
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Improvement in Accuracy
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Years into Act II Era
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
DeepSeek-R1: Achieving Human-Competitive Math Performance
79.8% AIME ScoreDeepSeek-R1 sets a new benchmark in mathematical reasoning, outperforming traditional models without long CoT and approaching human competitive performance on the American Invitational Mathematics Examination (AIME).
| Feature | Natural Language Reasoning | Formal Language Reasoning |
|---|---|---|
| Approach | Questions with verifiable answers | Formal systems (Lean, Isabelle) |
| Benefit | Facilitates search & learning, wide TTS application | Ensures verifiability, precise signals for tree search |
| Challenge | Logical errors, lack of rigor in intermediate steps | Lack of training data, limited development |
Challenges in AI Mathematical Reasoning
Despite significant advancements, AI in mathematical reasoning still faces hurdles. Solutions generated by LLMs may contain logical errors or lack rigor in intermediate steps due to the difficulty in strictly verifying reasoning process correctness. Future work needs to unify formal and natural language advantages.
Revolutionizing Software Development with AI Coding
High Productivity BoostThe rapid evolution of coding capabilities in language models, exemplified by Codex and AlphaCode, has transformed software development, boosting productivity and establishing coding as a core feature for general-purpose foundation models.
State-of-the-Art Performance on Elite Coding Benchmarks
Gold Medal Competitive ProgrammingModels like the o1-series achieve state-of-the-art performance on benchmarks such as SWE-bench and human-competitive platforms like Codeforces, even earning gold medals at the International Olympiad in Informatics (IOI) through RL and human-guided learning.
Overcoming Challenges in AI Code Generation
Critical challenges remain in AI code generation, including security risks from naive execution (requiring robust sandboxing), performance degradation from 'overthinking' reflection behaviors, and limitations in execution-based evaluation due to false positives. Aligning models with real-world coding tasks also needs further investigation.
DeepSeek R1: A Milestone in RL Training for VLMs
1st Effective RL ScalingDeepSeek R1 marks a significant achievement as the first model to fully demonstrate the effectiveness of reinforcement learning training in scaling test times for Vision-Language Models (VLMs), dividing research into pre-R1 and post-R1 phases.
VLM Adaptation Process for Test-Time Scaling
LLM Test-time Scaling Techniques
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Textual Output Generation
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VLM Adaptation
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Multimodal Understanding
Addressing Hallucinations in Multimodal AI
Long-CoT-based test-time scaling on VLMs faces challenges, particularly in integrating rethinking and reflection on visual inputs to address hallucinations from non-textual inputs. Future research must better integrate test-time scaling with diverse modality inputs and leverage synergies for improved effectiveness.
Scaling AI Agents for Complex Research and Computer Use
Hundreds Steps for Complex TasksAutonomous LLM Agents are evolving to handle open-ended tasks like deep research (5-30 mins per task) and complex computer use (hundreds of steps), demonstrating clear test-time scaling behavior.
Agent Decision-Making Process with Test-Time Scaling
Current Environment Observations
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Historical Trajectories
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Verification & Reflection
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Enhanced Decision-Making
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Long-term Goal Alignment
Key Barriers to Agentic System Deployment
Barriers to robust agentic system deployment include insufficient general model capabilities, over-reliance on prompt engineering, and context window constraints for long trajectories. The lack of reliable external verifiers for many tasks also hinders effective RL-based reward mechanisms.
Embodied AI: Foundational Link to AGI
Critical to AGI AdvancementEmbodied AI is crucial for advancing AGI by establishing the foundational link between cognitive representation and interaction with the physical world, enabling robots to perform real-world and complex tasks requiring sophisticated cognition and reasoning.
| Feature | High-level Planning | Low-level Control Policy Execution |
|---|---|---|
| Phase | Creating sequence of sub-tasks | Translating tasks into executable actions |
| Function | Decision-making based on state/outcomes, optimizing efficiency/safety/goal | 7-DOF robotic arm control, distinguishes objects |
| Key Challenge | Requires advanced cognitive abilities for analytical thinking | Relies on high-quality expert datasets, distribution shift |
Integrating Planning and Execution in Embodied AI
Despite progress in long CoT reasoning, significant room for improvement remains in embodied AI. Current frameworks typically decouple high-level planning from low-level policy. A more effective paradigm would integrate planning and execution into a unified process with continuous optimization and iterative feedback.
AI as a Safety Enabler: Detecting and Mitigating Risks
High Impact on SafetyAI systems capable of complex reasoning can help address and identify emerging safety concerns, potentially detecting and mitigating risks that humans might miss, allowing for thorough exploration of edge cases and vulnerabilities.
| Feature | Parallel Sampling for Safety | Direct Model Weight Modification |
|---|---|---|
| Approach | Sampling multiple responses at inference | Retraining or fine-tuning model weights |
| Goal | Amplify alignment, robustness, factual reliability | Directly embed safety policies into model |
| Scalability | Decouples from expensive training processes | Requires substantial computational & data resources |
Multi-agent Collaboration for Enhanced AI Safety
Increasing interaction steps through multi-turn correction or multi-agent collaboration can improve safety and robustness. Frameworks enable models to critique and refine responses, reducing harmful outputs like hallucination and adversarial attacks by promoting nuanced reasoning and cross-validation.
IterDRAG: Unlocking Test-Time Scaling Laws for RAG
Near-Linear Scaling Law for RAGIterDRAG demonstrates a near-linear relationship between RAG performance and effective context length, establishing a clear test-time scaling law for RAG systems and strengthening reasoning capabilities for complex multi-hop queries.
Limitations of Current RAG Reward Systems
A significant limitation in current RAG research is the primary focus on open-domain question answering tasks and reliance on rule-based rewards for short, factual answers. This approach may not generalize well to more complex reasoning tasks requiring detailed explanations.
Iterative Search & Reasoning in RAG
IterDRAG methodically decomposes complex questions into sequential sub-queries and conducts iterative search and reasoning to construct comprehensive answers, validated by several follow-up studies.
LLM-as-a-Judge: A Paradigm Shift in Evaluation
Human-like AssessmentThe LLM-as-a-Judge paradigm transforms evaluation from rule-based metrics (BLEU, ROUGE) to more human-like assessment, significantly enhancing quality by allocating additional computational resources during inference.
LLM-as-a-Judge Enhancement Process
Allocate Additional Computational Resources
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Fine-grained Evaluation (step-by-step)
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Structured CoT (Planning & Execution Phases)
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Multi-agent Systems (Intermediate Feedback)
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Parallel Sampled Crowd Responses
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Enhanced Evaluation Quality
Enhancing Evaluation for Complex, Long-Horizon Tasks
Current evaluation frameworks increasingly focus on complex agents and workflows. Future research must emphasize enhancing the reliability of evaluation for these tasks, requiring strategies to balance test-time scaling benefits with evaluation speed.
Calculate Your Potential ROI
Estimate the impact of Test-Time Scaling and Cognition Engineering on your enterprise operations.
Projected Annual Savings
Annual Cost Savings
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Your Cognition Engineering Roadmap
A phased approach to integrate Test-Time Scaling and unlock deep cognitive capabilities in your AI systems.
Phase 1: Foundation & Assessment
Conduct a thorough assessment of existing LLM infrastructure and identify key use cases for cognition engineering. Define initial objectives and success metrics, and train core teams on foundational test-time scaling methods.
Phase 2: Pilot Implementation & Data Curation
Implement test-time scaling methods (e.g., parallel sampling, tree search) on pilot projects. Begin curating high-quality cognitive data from human experts and AI-generated thoughts, laying the groundwork for advanced training strategies.
Phase 3: RL-Enhanced Cognition & Iterative Refinement
Introduce reinforcement learning to elicit long CoT capabilities and autonomous self-improvement. Establish iterative self-reinforced learning loops, continually refining models based on new cognitive data and performance feedback.
Phase 4: Broad Deployment & Advanced Integration
Expand cognition-engineered AI across enterprise applications. Integrate with existing workflows, develop advanced human-AI collaborative paradigms, and explore new architectural innovations to push the boundaries of AI intelligence.
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