FIRE-BENCH: Playing Back the Tape of Scientific Discovery
Why frontier research agents can write code, run experiments, and still fail at the part of science that actually matters: designing the right evidence and drawing the right conclusion.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Perspective Without Rewards: When AI Develops a Point of View
A mechanism-first reading of how a reward-free AI agent can develop a slow, history-shaped internal stance—and why the business value is observability, not consciousness theater.
Thinking Isn’t Free: Why Chain-of-Thought Hits a Hard Wall
A new BAPO-CoT paper shows why some reasoning tasks cannot be compressed below linear token growth, and why enterprise AI systems need routing, tools, and architecture—not just shorter prompts.
When Benchmarks Lie: Teaching Leaderboards to Care About Preferences
A new benchmark-alignment paper shows how public LLM leaderboards can be reweighted toward downstream preferences—and why that is useful only when the benchmark already contains the right signal.
When LLMs Lose the Plot: Diagnosing Reasoning Instability at Inference Time
A paper on inference-time instability shows how token probability logs can reveal when an LLM’s reasoning trajectory is beginning to unravel.
Conducting the Agents: Why AORCHESTRA Treats Sub-Agents as Recipes, Not Roles
AOrchestra shows that the practical edge in multi-agent systems may come less from adding more agents and more from dynamically composing the right instruction, context, tools, and model for each subtask.
Conformal Thinking: Teaching LLMs When to Stop Thinking
A mechanism-first reading of Conformal Thinking, showing how risk-controlled early stopping turns reasoning budgets from guesswork into an operational error-budget decision.
More Isn’t Smarter: Why Agent Diversity Beats Agent Count
A mechanism-first reading of why multi-agent LLM systems saturate when agents repeat each other, and why useful diversity beats raw agent count.
Search-R2: When Retrieval Learns to Admit It Was Wrong
Search-R2 shows why reliable retrieval agents need local error repair, not just more search calls or larger rollout budgets.
When Agents Stop Talking to the Wrong People
TodyComm shows why multi-agent AI systems need learned communication governance, not just more agents talking more often.