A Good Score Is Not Permission to Rewrite the Playbook
R² Flow shows why self-improving agents need separate mechanisms for learning what works, judging what helps, and approving permanent workflow changes.
Cognaptus Insights
Industry thoughts, trends, and commentary.
Check What Breaks: Turning Reasoning Errors into Inference-Time Controls
A reasoning-error taxonomy becomes more operationally valuable when recurring failures are converted into specific inference-time checks rather than generic instructions to be careful.
Readable Before Steerable: Why Probe Accuracy Is Not a Control Test
A developmental study of language models shows why highly accurate internal probes should not be treated as evidence that the same representation can reliably steer behavior.
Study Before the Ticket Arrives: Pre-Task Preparation for Enterprise Agents
A new agent-study framework suggests that reusable preparation can improve frozen agents and reduce repeated test-time search—but only when the preparation strategy fits the environment.
The Split Point Is Part of the Model
USplit-VQA shows that moving multimodal computation off constrained clients can sharply reduce memory and communication, but the partition itself becomes a model-quality and security decision.
Undecided Is Information: Turning Legal Reasoning Gaps into the Next Question
A symbolic legal decision-support framework shows how an unresolved compliance check can identify the exact information needed to continue reasoning.
Verify the Contract Before You Verify the Work
Magenta shows why reliable agent verification needs a semantic check before formal execution verification—and targeted repair after failures.
Evidence Before Elaboration: Why Product-Video Extraction Gains Start With Retrieval
ViS-CoT shows that better product-video extraction starts with better evidence, while staged reasoning becomes valuable only after that evidence is available.
Following Instructions Is Not the Same as Knowing More
MM-IFEval-Pro shows how deterministic multimodal instruction testing can improve compliance and expose image-embedded instruction conflicts without confusing those gains with broader model capability.
Measure the Chain Before You Target the Reward
A new credit-assignment study shows why precise turn-level rewards can underperform when the verifier sees only a small fraction of the workflow that produced success.