**The exponent-range precision barrier (validated in FP32-BF16 LMC transitions) will constrain the optimal context window size for agentic AI systems (AgentSafe/TRACE) managing long-horizon ML research (AiScientist), where coalition-based deviations in tool-use equilibria emerge as memory costs exceed 10M-token thresholds.**
Adversarial Debate Score
47% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget
A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below an...
- Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
Production AI agents'failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, lar...
- DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback
LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid checkpoint and rollback (C/R) of the complete sandbox state, i...
- MiniMax Sparse Attention
Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundre...
- A hierarchical memory architecture overcomes context limits in long-horizon multi-agent computational modeling
Large language models (LLMs) demonstrate remarkable reasoning capabilities, yet their stateless architecture fundamentally limits deployment in long-horizon research workflows requiring multi-session ...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.