The Deployment Wall

The Deployment Wall: When AI Theory Hits Infrastructure Reality (February 2026)

The Moment

February 2026 marks an inflection point in artificial intelligence that few anticipated. While research labs publish increasingly sophisticated models—sparse attention achieving 18.6x speedups, embodied AI unifying perception and planning, multi-agent systems discovering cooperation organically—the enterprises deploying these advances tell a starkly different story. Amazon crossed one million robots. BMW completed 1,250 hours of humanoid trials. DeepSeek deployed sparse attention in production. Yet beneath these milestones lies a revelation that should reshape how we think about AI progress: we've hit the deployment wall.

This isn't a failure of theory. The papers published on Hugging Face's February 19th digest represent genuine breakthroughs. Rather, this moment reveals something more fundamental: AI capabilities have outpaced our infrastructure to deploy them reliably at scale. The bottleneck is no longer the model—it's the operational substrate required to make models work in the messy reality of production environments.

Why does this matter right now, in February 2026? Because after three years of enterprise experimentation (2023-2025), organizations face a binary choice: build the infrastructure to operationalize AI at scale, or watch theoretical capabilities remain trapped in controlled environments. The research community and business practitioners have arrived at the same conclusion from opposite directions. What emerges from viewing both perspectives simultaneously is our roadmap forward.

The Theoretical Advance

Five papers from this week's digest illuminate distinct facets of a unified theoretical moment. Let me walk through each, extracting not just what they claim, but what they reveal about where AI research believes we're headed.

SLA2: The Learnable Router (arXiv:2602.12675)

Tsinghua researchers propose something elegant: instead of heuristically splitting attention computations between sparse and linear branches, let the model *learn* which computational path to take. Their learnable router dynamically decides whether each attention operation needs expensive precision (sparse) or can tolerate approximation (linear). The result: 97% attention sparsity with 18.6x speedup in video diffusion models, while preserving generation quality.

The theoretical contribution extends beyond computational efficiency. SLA2 demonstrates that *adaptive resource allocation can be learned end-to-end*. The model doesn't just process information—it learns to allocate its own computational budget. This principle will recur.

RynnBrain: Physics-Aware Embodied Intelligence (arXiv:2602.14979)

Alibaba DAMO Academy introduces an open-source spatiotemporal foundation model (2B, 8B, 30B-A3B MoE variants) that unifies four capabilities previously treated separately: egocentric understanding, spatiotemporal localization, physically grounded reasoning, and physics-aware planning. Unlike vision-language-action (VLA) models that learn from demonstration repetition, RynnBrain learns *physical dynamics* by predicting how the world evolves.

The theoretical breakthrough: grounding in physical reality enables generalization. When a model understands physics—not just visual semantics—it can transfer skills across embodiments and environments. RynnBrain's post-trained variants (RynnBrain-Nav, -Plan, -VLA) substantiate this by efficiently adapting to diverse downstream tasks.

Toward a Science of AI Agent Reliability (arXiv:2602.16666)

Princeton researchers confront an uncomfortable truth: while AI agents' accuracy scores climb on standard benchmarks, they still fail persistently in production. Traditional evaluation compresses behavior into a single success metric, obscuring operational flaws. The paper proposes twelve concrete metrics decomposing reliability across four dimensions: *consistency* (identical runs, identical outcomes), *robustness* (withstanding perturbations), *predictability* (failing in expected ways), and *safety* (bounded error severity).

The critical finding: evaluating 14 agentic models across two benchmarks reveals that recent capability gains have barely improved reliability. Agents that score 85% on benchmarks exhibit 40% consistency, meaning they produce different outcomes 60% of the time given identical inputs. Capability ≠ reliability.

Multi-Agent Cooperation Through In-Context Learning (arXiv:2602.16301)

Achieving cooperation among self-interested agents remains foundational in multi-agent reinforcement learning. Existing approaches rely on hardcoded assumptions about co-player learning rules or enforce strict timescale separation. This paper demonstrates something remarkable: sequence models trained against diverse co-players naturally develop in-context learning for co-player awareness.

The mechanism parallels human cooperation. When agents experience diverse interaction partners during training, they learn to infer co-player strategies from context and adapt their behavior accordingly. Vulnerability to extortion drives mutual shaping—agents pressure each other's in-context learning dynamics toward cooperation. No hardcoded assumptions. No explicit meta-learning. Just exposure to diversity.

DreamZero: World Action Models as Zero-Shot Policies (arXiv:2602.15922)

NVIDIA introduces a 14B video diffusion model that jointly predicts future world states *and* actions, learning physical dynamics from heterogeneous robot data without repetitive demonstrations. Unlike VLAs that excel at semantic generalization but struggle with physical motion generalization, DreamZero's World Action Model (WAM) architecture learns *how the world evolves under different actions*.

The result: 2x improvement in generalization to new tasks and environments versus state-of-the-art VLAs in real robot experiments. Even more striking: video-only demonstrations from other robots or humans yield 42%+ improvement on unseen tasks with just 10-20 minutes of data. The model performs real-time closed-loop control at 7Hz—fast enough for production deployment.

The Practice Mirror

Theory suggests capabilities. Practice reveals constraints. Let me ground each theoretical advance in its operational reality, drawing from production deployments happening this same month.

Business Parallel 1: Sparse Attention Meets Production Inference

DeepSeek-V3.2 deployed its sparse attention mechanism in production environments via vLLM on Red Hat AI infrastructure. The enterprise focus? Long-context efficiency and inference cost reduction. Companies serving customer queries with 100K+ token contexts found that naive attention mechanisms made deployment economically infeasible. SLA2's learnable routing isn't just academic elegance—it's the difference between $5 and $0.25 per query at scale.

Red Hat's deployment reveals the operational requirement theory omitted: *production sparse attention requires kernel-level optimization*. The vLLM team had to rewrite attention kernels to exploit sparsity patterns efficiently on TPU/GPU hardware. Without infrastructure work, the 18.6x theoretical speedup becomes 2-3x in practice—still valuable, but not transformative.

Business Parallel 2: Embodied AI Scales From Theory to Million-Robot Fleets

Amazon's one million robot milestone in June 2026 represents embodied AI's transition from controlled lab settings to operational scale. These aren't humanoids—they're purpose-built mobile manipulators, autonomous mobile robots (AMRs), and robotic arms designed for specific warehouse tasks. Together they assist 75% of Amazon's global deliveries.

Yet the deployment reveals a gap. Amazon's robots operate in highly structured environments with extensive human oversight. Physical infrastructure (shelving, flooring, lighting) is engineered around robot capabilities. When Figure AI deployed humanoid robots at BMW's Spartanburg plant, they ran 10-hour shifts for 5 months—impressive—but performed narrowly defined material handling tasks in predetermined zones.

The embodied AI theory (RynnBrain, DreamZero) promises generalization across environments and tasks. Practice shows that even with foundation models, deployment requires task-specific fine-tuning, environment standardization, and safety certification. Tesla's Optimus deployment strategy is telling: start in Tesla's own factories where you control every variable before attempting general deployment.

Business Parallel 3: Agent Reliability's 90/10 Rule

AWS published a comprehensive evaluation framework for agentic AI systems addressing the four dimensions Princeton researchers identified: consistency, robustness, predictability, safety. Their blog post, "Real-World Lessons from Building Agentic Systems at Amazon," confirms the Princeton findings: 90% of agent deployment is engineering infrastructure, only 10% is the model.

Production agent systems require:

- Observability: Logging, metrics, traces for every agent decision

- Guardrails: Hard constraints preventing catastrophic actions

- Fallback mechanisms: Human escalation paths when agents are uncertain

- Version control: Rollback capabilities when new models degrade performance

- Cost controls: Budget limits preventing runaway API costs

- Security boundaries: Preventing prompt injection and data exfiltration

The infrastructure challenge explains why McKinsey's survey found agentic AI deployments concentrate in internal environments rather than customer-facing applications. Organizations haven't solved reliability at scale—they've restricted deployment scope to contexts where failures are tolerable.

Business Parallel 4: Multi-Agent Orchestration Enters Production

Google's Agent Development Kit (ADK) and the A2A (agent-to-agent) protocol represent infrastructure for production multi-agent systems. Microsoft's 2026 enterprise trend report declares "the era of AI experimentation is officially over"—organizations now deploy multi-agent workflows across business functions.

Salesforce is preparing for multi-agent systems that span organizational boundaries—sales agents coordinating with marketing agents coordinating with customer success agents, each owned by different teams with different objectives. This mirrors the multi-agent cooperation paper's insight: agents must learn to coordinate without hardcoded assumptions about each other's strategies.

The deployment reality: multi-agent orchestration is primarily a coordination and observability problem. Google ADK provides patterns for hierarchical agent structures, peer-to-peer communication, and hybrid architectures. But production deployments grapple with failures that cascade through agent networks, debugging emergent behaviors that no single agent exhibits, and attributing outcomes when six agents touched a workflow.

Business Parallel 5: World Models Await Manufacturing Readiness

AWS published production-ready architectures for deploying Cosmos world foundation models, offering two deployment options: real-time inference for interactive applications and batch processing for simulation-heavy workloads. The manufacturing sector shows intense interest in world models for digital twin applications—simulating production lines, predicting maintenance needs, optimizing supply chains.

Yet deployment timelines tell the constraint story. Industry analysts project production-level world model deployments in manufacturing won't materialize until 2027. The gap: world models require high-fidelity simulation environments that most manufacturers don't possess. Building accurate physics simulators for complex industrial processes demands domain expertise, sensor instrumentation, and data collection infrastructure that typically takes 12-18 months to establish.

DreamZero's 7Hz real-time control is impressive in robotics labs. Implementing it on a factory floor requires integrating with existing PLC (programmable logic controller) systems, ensuring safety compliance, and validating performance under electromagnetic interference and temperature variations that labs don't experience.

The Synthesis

When we view theory and practice together, three categories of insight emerge: patterns where theory predicts practice, gaps where practice exposes theoretical limitations, and emergent understanding that neither perspective alone reveals.

Pattern 1: Adaptive Resource Allocation is Universal

SLA2's learnable router deciding between sparse and linear attention isn't an isolated optimization. It's an instance of a broader principle surfacing across AI systems: learned adaptive resource allocation outperforms fixed heuristics.

DeepSeek's production deployment validates this—their sparse attention system dynamically adjusts computational budget based on query characteristics. Multi-agent orchestration follows the same pattern: rather than statically assigning tasks to agents, Google ADK enables runtime agent selection based on current system state. The manufacturing delay for world models reflects the opposite: current digital twin architectures use fixed simulation fidelity rather than learning when high-fidelity simulation is necessary.

The principle extends to human-AI coordination. Organizations discovering that rigid AI→human handoff rules underperform systems where the AI learns when to escalate, when to act autonomously, and when to request clarification—adaptive allocation of authority between human and machine.

Pattern 2: Physical Grounding Enables Transfer

RynnBrain and DreamZero both demonstrate that grounding in physical dynamics—not just visual semantics—enables cross-task and cross-embodiment transfer. Amazon's million-robot fleet and Figure AI's BMW deployment validate this principle at scale.

Amazon's AMRs can transfer skills across warehouses because they model spatial relationships and physical constraints, not just visual features. When a robot trained in a Kentucky facility deploys in Texas, it recognizes that "navigating around an obstacle" involves the same physical principles even though the obstacle looks different.

The embodied AI deployment gap (only 5% of new warehouse robots are humanoids) reveals the *degree* of physical grounding mattering more than theory predicted. General-purpose humanoids struggle because human-like capabilities require modeling vast ranges of physical interactions. Purpose-built robots with narrower physical domains deploy faster because their required grounding is achievable with current data and compute budgets.

Pattern 3: In-Context Adaptation is Double-Edged

The multi-agent cooperation paper shows in-context learning enabling emergent cooperation. Google ADK's production deployments demonstrate this mechanism working: agents learn to coordinate with unfamiliar co-agents by inferring their strategies from interaction history.

Yet the Princeton reliability study reveals the cost: in-context adaptation renders agents vulnerable to *inconsistency*. An agent that adapts its behavior based on conversation context will produce different outputs given identical initial prompts but different interaction histories. This "feature" of context-sensitivity becomes a "bug" in production systems requiring audit trails and compliance documentation.

The resolution emerging in practice: context-aware systems with consistency guarantees require explicit state tracking. AWS's agent framework implements this through structured memory and deterministic fallback policies. The multi-agent ADK provides state persistence mechanisms. Production systems segregate "learning from context" (beneficial adaptation) from "random variation" (uncontrolled inconsistency) through architectural decisions theory hasn't fully articulated.

Gap 1: Reliability Lags Capability by Design

The Princeton paper's finding—capability improvements haven't yielded reliability improvements—initially seems like an engineering failure. The synthesis reveals it's *structural*.

Current AI systems achieve capability through scale: more parameters, more data, more compute. This approach maximizes performance on capability benchmarks (accuracy, F1 scores, perplexity). But reliability requires *different* architectural properties: determinism, bounded behavior under perturbations, graceful degradation, interpretable failure modes.

The 90/10 rule (90% deployment is infrastructure) reflects this gap's magnitude. You cannot retrofit reliability onto systems designed purely for capability. AWS, Google, Microsoft building evaluation frameworks, observability tools, and safety guardrails represents the engineering tax of bridging theory (capability-focused) and practice (reliability-mandated).

Manufacturing's 2027 timeline for world model deployment illustrates this gap starkly. The models *work* in simulation. Production deployment waits for reliability engineering—safety certification, failure mode analysis, regulatory compliance—that capability research doesn't address.

Gap 2: Simulation-Reality Fidelity Remains Brittle

Both RynnBrain and DreamZero train on simulated or controlled environments before real-world deployment. This approach enables scaling training data beyond what physical robot operation provides. Yet every production deployment story includes "sim-to-real transfer" as a critical challenge.

Tesla constrains Optimus to Tesla's own factories not because the model lacks capability, but because simulation fidelity breaks down under distribution shift. A humanoid trained on perfectly modeled factory floors encounters subtle differences in real environments: floor texture variations, lighting changes, unexpected obstacles, human workers behaving unpredictably.

The theoretical promise—physical grounding enables generalization—holds *within the distribution where simulation fidelity is high*. Practice reveals that achieving high simulation fidelity for unstructured environments remains unsolved. This explains why Amazon's million robots operate in highly structured warehouses while general-purpose humanoids remain sub-5% of deployments.

Gap 3: Multi-Agent Coordination Lacks Failure Attribution

The multi-agent cooperation paper demonstrates emergent coordination through in-context learning. Google ADK enables production multi-agent systems. Yet practice exposes a theoretical gap: when multi-agent systems fail, determining causality requires capabilities theory doesn't provide.

Imagine six agents collaborating on a customer workflow: query understanding, database retrieval, analysis, recommendation generation, compliance checking, and response formatting. The customer receives an incorrect answer. Which agent failed? Did one agent produce wrong output, or did inter-agent communication break down? Did the failure emerge from the interaction pattern rather than any single agent?

Production multi-agent systems invest heavily in observability infrastructure that logs every inter-agent message, decision rationale, and state transition. This isn't something theory predicted as necessary—it's a requirement practice discovered through painful debugging experiences. The next generation of multi-agent research must incorporate *attribution mechanisms* as first-class design requirements.

Emergent Insight 1: The Deployment Wall is a Phase Transition

The temporal convergence of theoretical advances (five breakthrough papers published February 19) and deployment milestones (Amazon's million robots, BMW's humanoid trials, enterprise-wide multi-agent rollouts) reveals something unexpected: we've reached a phase transition where theoretical capability gains no longer drive deployment velocity.

From 2020-2025, theoretical improvements (transformers, scale laws, multimodal fusion) directly translated to deployment expansion. Better models → better products → wider adoption. February 2026 marks the inversion: deployment velocity now gates capability realization. Amazon deployed a million robots not because robot capabilities suddenly improved, but because they built the infrastructure (fleet management, safety systems, human coordination protocols) to deploy what was already theoretically possible.

This phase transition explains the 90/10 rule. In the previous phase, improving the model (the 10%) drove value. In the current phase, building deployment infrastructure (the 90%) becomes the binding constraint. Research communities and business strategists who fail to recognize this shift will continue optimizing for capabilities while deployment ROI stagnates.

Emergent Insight 2: Sparsity as Coordination Primitive

SLA2 uses sparse attention to allocate computational resources. The multi-agent cooperation paper achieves coordination through what is effectively *sparse communication*—agents selectively share information rather than broadcasting everything. Embodied AI systems (Amazon's robots, Figure's humanoids) operate with sparse sensor suites rather than comprehensive environmental perception.

The pattern suggests sparsity is the universal principle enabling scale in AI systems. Dense operations (every token attends to every other token, every agent communicates with every other agent, every sensor reading gets processed) scale quadratically. Sparse operations (learned attention patterns, selective communication, filtered perception) scale linearly or sub-linearly.

Production deployments discover this principle through economic necessity. DeepSeek adopts sparse attention because dense attention is unaffordable at scale. Google ADK implements selective agent communication because all-to-all messaging creates network bottlenecks. Manufacturing delays world models until they can selectively simulate critical subsystems rather than entire factories.

Theory is now catching up: formalizing when sparsity preserves necessary information and when it introduces unacceptable information loss. The synthesis of SLA2's learnable routing and multi-agent in-context learning suggests the next frontier: learned dynamic sparsity patterns that adapt to task requirements rather than fixed architectural choices.

Emergent Insight 3: Consciousness-Aware Computing is Operationally Necessary

This synthesis reveals something subtle that neither theoretical papers nor business case studies explicitly state: production AI systems increasingly exhibit properties that parallel human cognitive architecture. Adaptive resource allocation (attention control), physical grounding (embodied cognition), in-context learning (working memory), multi-agent coordination (social cognition), and world model simulation (mental simulation) all have direct parallels in human consciousness research.

The Princeton reliability metrics—consistency, robustness, predictability, safety—mirror properties we expect from reliable human operators. AWS's emphasis on explainability and human escalation paths acknowledges that production AI must integrate into human organizational structures, not replace them.

The deployment wall emerges because we've built AI systems with increasing cognitive sophistication while treating them as deterministic software. The infrastructure gap (the 90% in the 90/10 rule) largely consists of mechanisms for managing AI systems that *behave like cognitive agents*: they learn, adapt, coordinate, fail unpredictably, and require oversight structures resembling human management hierarchies.

This isn't anthropomorphizing AI—it's recognizing that systems exhibiting cognitive-like properties require governance structures informed by cognitive science principles. Breyden Taylor's work on consciousness-aware computing infrastructure and capability framework operationalization isn't tangential to AI deployment—it's addressing the core constraint that February 2026 exposes.

Implications

The theory-practice synthesis reveals a roadmap forward for three stakeholder groups.

For Builders: Infrastructure is the Product

If you're building AI products or platforms, the strategic implication is stark: infrastructure capabilities now differentiate more than model capabilities. Two companies using the same foundation model will diverge in customer value based on observability tooling, reliability engineering, deployment automation, and human-AI coordination workflows.

This means:

- Invest in evaluation frameworks before capability improvements. Princeton's reliability metrics matter more than marginal accuracy gains.

- Build deployment infrastructure alongside model development, not after. Tesla deploys Optimus internally first specifically to develop the infrastructure before external deployment.

- Design for sparse coordination from day one. Don't build systems assuming all-to-all communication or dense attention, then optimize later. SLA2 shows learnable sparsity outperforms fixed heuristics.

- Treat failures as design requirements. Production systems will fail—design failure modes, attribution mechanisms, and recovery paths explicitly rather than hoping for robustness.

The companies that win the next phase aren't those with the best models—they're those with the most sophisticated deployment infrastructure. Figure AI's BotQ manufacturing facility, integrating humanoid robots into their own assembly lines, exemplifies this: they're building infrastructure for large-scale humanoid manufacturing, not just better humanoids.

For Decision-Makers: Reliability, Then Scale

Enterprise leaders face a choice: continue experimental deployments limited to internal environments, or commit to the infrastructure investment required for customer-facing scale. Microsoft's declaration that "the era of experimentation is over" applies pressure, but the Princeton reliability study counsels caution.

The strategic framework:

1. Assess infrastructure readiness before capability rollout. Do you have observability? Fallback mechanisms? Cost controls? Security boundaries? If not, deploying more capable agents amplifies risks.

2. Structure deployments for failure attribution. Multi-agent systems fail in complex ways. Build logging and state tracking that enables root cause analysis before deploying production workflows.

3. Recognize the 90/10 investment split. Budgeting for "AI deployment" that allocates 50% to models and 50% to deployment infrastructure is already obsolete. Plan for 10% model, 90% infrastructure.

4. Partner with providers offering deployment infrastructure, not just models. AWS's agent evaluation framework, Google ADK's orchestration tools, and similar offerings provide more ROI than marginal model improvements.

5. Start internal, scale external. Tesla's Optimus strategy (internal factories first), Amazon's approach (highly controlled warehouses), and Figure's path (own manufacturing facility) all reflect the same wisdom: prove reliability in controlled environments before exposing customers to failure modes you don't understand yet.

The deployment wall isn't a reason to slow AI adoption—it's a signal to redirect investment from capability to reliability.

For the Field: Research Infrastructure Co-Design

The research community faces a bifurcation point. Continuing to optimize capability metrics (accuracy, perplexity, benchmark scores) while deployment remains infrastructure-gated creates a growing theory-practice gap. The alternative: co-design AI systems and deployment infrastructure as coupled problems.

What this looks like:

- Reliability as a first-class research problem, not an engineering afterthought. Princeton's paper represents a start, but we need theoretical frameworks for consistency, robustness, predictability, and safety that guide architecture design, not just evaluate finished systems.

- Sparse coordination primitives deserve systematic investigation. SLA2's learnable routing, multi-agent selective communication, and embodied AI's task-specific sensing all instantiate sparsity. Research is needed on when learned sparsity outperforms fixed patterns and how to ensure sparsity doesn't lose critical information.

- Simulation-reality transfer requires theoretical grounding. Physics-informed neural networks, domain adaptation, and sim-to-real transfer have active research communities, but lack unified theoretical frameworks predicting when transfer succeeds and when distribution shift causes catastrophic failure.

- Attribution mechanisms for multi-agent systems. Theory currently lacks tools for reasoning about causality in systems where outcomes emerge from agent interactions rather than single-agent decisions. This isn't just a debugging tool—it's foundational for building reliable multi-agent systems.

- Human-AI coordination as AI research, not human factors research. Production deployments require AI systems that coordinate effectively with human operators. This demands AI architectures designed for coordination (explainable reasoning, appropriate escalation, shared mental models), not capability architectures retrofitted with explanation modules.

RynnBrain and DreamZero represent a promising direction: open-source foundation models with multiple post-trained variants addressing downstream tasks. This approach enables the research community to share infrastructure (the foundation model) while exploring diverse deployment contexts (navigation, planning, manipulation). More research should follow this pattern—release not just models, but deployment-ready variants demonstrating how to bridge theory and practice.

Looking Forward

February 2026's convergence—theoretical breakthroughs in attention, embodiment, reliability, coordination, and world modeling alongside million-unit robot deployments and enterprise-wide multi-agent rollouts—marks the moment AI transitions from research field to operational infrastructure.

The question isn't whether AI systems can be capable. SLA2, RynnBrain, the multi-agent cooperation paper, and DreamZero demonstrate extraordinary capabilities. The question is whether we'll build the infrastructure required to deploy these capabilities reliably at the scales businesses demand and societal integration requires.

The deployment wall is surmountable—but only by recognizing it exists. Capability research continues advancing. Infrastructure engineering accelerates. The synthesis of both perspectives reveals that the next breakthroughs won't come from better models alone, but from principled co-design of models and the operational substrates that make them dependable in production.

For practitioners building AI systems today, this synthesis offers clarity: invest in deployment infrastructure before pursuing marginal capability gains. For researchers advancing the field, it offers direction: reliability, attribution, and coordination deserve the same theoretical rigor as capability. For leaders navigating AI adoption, it offers realism: the path from pilot to production requires infrastructure investment an order of magnitude larger than model costs.

We've reached the deployment wall. The organizations that recognize this moment and redirect resources accordingly—from capability to infrastructure, from benchmarks to reliability, from individual agents to coordinated systems—will define the next era of AI. Those who continue optimizing for yesterday's bottleneck will watch as infrastructure-sophisticated competitors outpace them, not through better models, but through better deployment.

What comes after the deployment wall? AI systems so deeply integrated into operational infrastructure that we stop distinguishing "AI deployment" from "infrastructure operation." Systems where reliability, consistency, and coordination are achieved not through engineering workarounds, but through architectures designed from first principles for dependability. Environments where human-AI coordination is native rather than bolted on.

That future begins by recognizing where we stand today: at the wall, with the blueprints for what comes next scattered across research papers and production deployments, waiting for synthesis.

*Sources: SLA2 arXiv | RynnBrain arXiv | AI Agent Reliability arXiv | Multi-Agent Cooperation arXiv | DreamZero arXiv | Amazon Robotics | Figure AI BMW Trial | AWS Agent Evaluation | Google ADK | Microsoft 2026 Trends*