Cognaptus Insights

CRAGRU reframes recommendation unlearning as retrieval control, offering a cheaper route to targeted forgetting without pretending retrieval filtering is the same as legal erasure.

KarmaTS shows how expert-edited causal graphs can become executable time-series simulators for benchmarking, synthetic data, and more disciplined AI validation.

A business-focused analysis of how psychologically grounded reward design can make robot navigation more socially acceptable without pretending VR comfort scores are field deployment proof.

MarsRL shows why multi-agent reasoning needs trained critics, role-specific rewards, and pipeline-aware reinforcement learning rather than a few optimistic verifier prompts.

A mechanism-first look at how test-time policy shaping can steer reward-maximising agents away from harmful behaviour without retraining them.

A precise look at EGuR, a test-time system that learns not just what to remember, but which reasoning strategy to run next.

A practical reading of communication-constrained multi-agent reinforcement learning, where robustness, delay, and bandwidth become deployment design choices rather than academic footnotes.

LoReTTA shows that temporal graph models can be weakened by subtle, resource-light poisoning of interaction history, not only by noisy brute-force attack.

A mechanism-first look at how pretrained language models can be surgically converted into depth-recurrent reasoners—and why the gains are useful, conditional, and not remotely free.

A comparison-led reading of why VLM robot planners need feedback, memory, and carefully tuned replanning rather than blind faith in more model calls.